Significant efforts in the past decades to teach evidence-based practice (EBP) implementation has emphasized increasing knowledge of EBP and developing interventions to support adoption to practice. These efforts have resulted in only limited sustained improvements in the daily use of evidence-based interventions in clinical practice in most health professions. Many new interven-tions with limited evidence of effectiveness are readily adopted each year - indicating openness to change is not the problem. The selection of an intervention is the outcome of an elaborate and complex cognitive process which is shaped by how they represent the problem in their mind and is mostly invisible processes to others. Therefore, the complex thinking process which support appropriate adoption of interventions should be taught more explicitly. Making the process visible to clinicians increases the acquisition of the skills required to judiciously select one in-tervention over others. The purpose of this paper is to provide a review of the selection process and the critical analysis that is required to appropriately decide to trial or not trial new intervention strategies with patients.

Quality evidence-based decisions and strategies are critical and valuable tools in strengthening health systems policies, strategic priorities action plans and comprehensive care delivery management. Our finding showed that there is scarcity of local/national and internal effective evidence-based and management strategies for informed decision making based on the disease or health epidemics nature, extend, ecological and geo-location of pandemics and epidemics crises burden and impacts. Evidence-based programs or projects are crucial in evolving pandemics and/or (Ebola, meningitis, Cholera and influenza) epidemics persistent morbidity and mortality/ case fatality reduction or prevention; as well as on poverty and inequity alleviation within the vulnerable population and citizenry over time. This paper assesses implications of evidence-based on health systems surveillance and monitoring systems, preparedness and emergency response gaps and needs in improving care delivery uptake and usefulness, coverage and effectiveness in Africa. Furthermore, the article advocates for quality, access to and uptake of knowledge-based policy-decision making and practice improvements in building efficient and standardized surveillance, preparedness and response approaches. Also, in enriching data sharing and inclusiveness through understanding the links between poverty, poor health and inequited related emerging infectious diseases epidemics in Africa. Leveraging on cumulative lessons learnt experiences and innovations in integrating participatory knowledge-based policies and approaches is paramount in fostering vulnerable population awareness and engagement, skills empowerment collaborative productivity and sustainable healthy solutions and measures. Strengthening new partnerships, alliances, and networks requires collaborative and quality evidence policy decisions, appropriate and reliable monitoring and evaluation systems approaches and strategies in improving local preparedness and emergency response capabilities against future emerging infectious diseases epidemics and fast-tracking poverty alleviation knowledge-based livelihoods and health solutions for impact. Furthermore, integrated, participative partnerships and collaborative responsibilities, cost effective and reliable evidence health financing and budget allocation, and targeted capacity development aiming at reducing and averting the burden of poverty related emerging threats and epidemics preparedness and response programs in African countries.

Purpose: Trauma informed care as an organisational intervention has gained increasing attention in recent years. Substance use settings may be overrepresented with service users who have experienced trauma and adversity. This systematic review will examine the effectiveness of trauma informed care as an organisational intervention from an implementation perspective. Methods: The preferred reporting items for systematic review and meta-analyses (PRISMA) guidelines were followed. Three databases were searched; Academic Search Complete, Embase, and Scopus, supplemented with a Google Scholar search. Articles were included if they were peer reviewed in the English language from inception to February 2024 and reported on trauma informed care in substance use settings with an implementation context. Quality appraisal was conducted with the Mixed Method Appraisal Tool. Results: This systematic review of trauma informed care with implementation domains in substance use settings included (N=15) studies reporting on 12 datasets. Studies were classified as low quality in (n=5), moderate in (n=1) and high in (n=6). Studies reported positive findings on reductions in substance use, and reductions on mental health and trauma symptoms, and treatment retention across community and residential settings. While satisfaction with services provided was also highlighted for service users and employees. The results further highlight the importance of the role of leadership across implementation domains, and the possibility of using the 10 trauma informed implementation domains in different treatment contexts as a framework to support implementation. Findings in this review are mapped onto these 10-trauma informed care implementation domains and reported as a narrative synthesis. Conclusion: Trauma informed care is a promising organisational wide intervention with the potential to improve outcomes for service users and employees. Implementation can be supported by using the 10 trauma informed care implementation domains described in this review. However, most of the studies were qualitative and quantitative descriptive meaning drawing casual inferences is difficult. As such, further research should be undertaken.

There is a systematic exclusion of gender-based violence, safe abortion, reproductive cancers, infertility services, comprehensive sexuality education, sexuality services, and STI’s other than HIV in essential health packages in LMICs. To accelerate progress on sexual reproductive health (SRH), the Guttmacher–Lancet Commission proposed the adoption of these interventions into an essential health package of SRH services that should be universally available. In this commentary, we use a healthcare priority-setting processes lens to review the importance of these services for universal health coverage. We isolate inherent challenges in social value judgments for terminal, process and content evidence for their healthcare priority-setting. We then advance promising emerging practical examples from low to middle-income countries on evidence-informed decision-making processes. We recommend capacity development through regional support, generating equity and efficiency evidence and strengthening political and publicly acceptable processes to institutionalise and operationalise evidence-informed decision-making.

Physics-informed machine learning (PIML) refers to the emerging area of extracting physically relevant solutions to complex multiscale modeling problems and has enjoyed significant interest from the research community. This paper discusses the recent critical advancements in the PIML domain. Novel methods and applications of domain decomposition in physics-informed neural networks (PINN) in particular are highlighted. Additionally, we explore recent Works toward utilizing neural operator learning to intuit relationships in physics systems traditionally modeled by sets of complex governing equations and solved with expensive differentiation techniques. Finally, expansive applications of traditional physics-informed machine learning and potential limitations are discussed. In addition to summarizing recent work, we propose a novel taxonomic structure to catalog physics-informed machine learning based on how the physics-information is derived and injected into the machine learning process. The taxonomy assumes the explicit objectives of facilitating interdisciplinary collaboration in methodology, thereby promoting a wider characterization of what types of physics-problems are served by the physics-informed learning machines, and assisting in identifying apt targets for future work. To summarize, the major twofold goal of this work is to summarize recent advancements and introduce a taxonomic catalog for applications of physics-informed machine learning.

Background The prevalence of autism has increased substantially among Latine children, however, few service systems are prepared to provide culturally relevant services. Parents Taking Action (PTA), is a culturally informed parent-mediated psychoeducation program designed to meet the informational needs of Latine families of children with autism. The purpose of this study was to pilot a hybrid model of intervention by including direct parent coaching through pivotal response training (PRT) along with PTA among Latina mothers of children with autism. Methods Ten Latina mothers of children 8 and under with autism participated in this study. Mothers received PTA from two bilingual trained promotoras (community health workers). PTA included 14 2-hour psychoeducation sessions. Mothers also received 4 one-hour sessions of PRT from bilingual/bicultural coaches. Results Paired samples t-tests indicated significant increases in family outcomes, self-efficacy in using the intervention strategies, and frequency of using strategies, from pre to post-test. Conclusions This study suggests that culturally informed parent-mediated autism intervention coupled with parent coaching positively affects family outcomes among Latine families of children with autism.

Parametric models have been widely used in pertinent literature for reconstructing, modifying and representing a wide range of airfoil and/or hydrofoil profile geometries. Design spaces corresponding to these models can be exploited for modeling and profile-shape optimization under various performance criteria. Accuracy requirements, along with the need for modeling local features, often lead to high-dimensional design spaces that hinder the process of shape optimization and design through analysis. In this work, we propose a shape-informed dimensional reduction approach that attempts to tackle this deficiency by producing low-dimensional latent design spaces that can be efficiently used in shape representation and optimization. Furthermore, geometric moments are introduced in an attempt to cost-effectively capture analysis-relevant information that is generally expensive to produce. Specifically, geometric integral properties, although intrinsic features of the shape, are quite commonly related to performance indicators employed in performance optimization and therefore provide a cost-effective physics-informed component in the description of the design in the latent space. To this end, we employ the generalized Karhunen-Loeve expansion to produce a shape- and physics-informed subspace retaining the highest possible geometric variance and robustness, that is, lack of invalid designs. At the same time, a series of shape discretizations, encoding the foil's shape profile, are examined with regards to their effect on the resulting latent space's quality and efficiency. Our results demonstrate a significant reduction in the dimensionality of the original design space while maintaining a high representational capacity and a large percentage of valid geometries that facilitate fast convergence to optimal solutions in performance-based optimization.

The adoption of technology and digital tools across various user groups has brought about a heightened risk of cyber-attacks targeting devices and shared data. These elevated levels of cybercrime necessitate thorough investigations to address victims’ inquiries about the nature, causes, affected data, and impacted resources of the attack. Digital forensics serves as a crucial means to answer these queries. Additionally, it enables the formulation of strategies and delineation of roles essential for fortifying future defenses against such attacks. Within digital forensics, digital evidence holds pivotal importance in the investigative process as it serves as a cornerstone in legal proceedings to identify perpetrators. Numerous research endeavors focus on devising solutions to safeguard evidence, including centralized storage centers or cloud computing. These efforts aim to create frameworks integrating diverse security levels for sharing and preserving evidence effectively. In this paper, a new framework is proposed that seeks to amalgamate novel technologies with forensic practices to address challenges in the preservation phase of digital forensics. Strategies include segmenting evidence into smaller components, leveraging steganography to support preservation, and employing blockchain technology to ensure the integrity of collected evidence.

Gaining insight into tribological systems is crucial for optimizing efficiency and prolonging operational lifespans in technical systems. Experimental investigations are time-consuming and costly, especially for reciprocating seals in fluid power systems. Elastohydrodynamic lubrication (EHL) simulations offer an alternative but demand significant computational resources. Physics-informed neural networks (PINNs) provide a promising solution using physics-based approaches to solve partial differential equations. While PINNs have successfully modeled hydrodynamics with stationary cavitation, they have yet to address transient cavitation with dynamic geometry changes. This contribution applies a PINN framework to predict pressure build-up and transient cavitation in sealing contacts with dynamic geometry changes. The results demonstrate the potential of PINNs for modeling tribological systems and highlight their significance in enhancing computational efficiency.

A novel approach is developed to improve the convergence of Physics-Informed Neural Networks (PINNs), aiming to employ them as real-time computational models within the framework of the digital twin for manufacturing processes. This method entails the weighting of physical equations, boundary conditions, and initial conditions to ensure their comparable magnitudes, with power being the chosen quantity in this study. The approach is applied to thermal problems, which are crucial for predicting manufacturing part defects. Different configurations, including complex boundary conditions and complex physics, were tested to assess the model’s robustness. The W-PINN demonstrates good predictions and strong stability compared to the classical PINN.

The Physics Informed Neural Networks framework is applied to the understanding of the dynamics of Coronavirus of 2019. To provide the governing system of equations used by the framework, the Susceptible-Infected-Recovered-Death mathematical model is used. The study focused on finding the patterns of the dynamics of the disease which involves predicting the infection rate, recovery rate and death rate; thus predicting the active infections, total recovered, susceptible and deceased at any required time. The study used data that was collected on the dynamics of COVID-19 from the Kingdom of Eswatini between March 2020 and September 2021. The obtained results showed less errors thus making highly accurate predictions.

Population growth, climate change, changes to land use, and the advent of emerging infectious disease agents has put our abilities to feed the growing global population at risk, which is of particular concern in low- or middle-income countries. Research to improve the profitability and sustainability of common livestock production systems is key to tackling this issue. However, despite considerable research investment to address this concern, there has been no attention to development of robust methodologies for the synthesis of existing research evidence and its re-use. Syntheses of evidence are critical for fostering best practices, minimising wastage of valuable research resources, and enabling informed decision and policy making. Whilst the use of evidence syntheses is well entrenched in human healthcare, their use within animal sciences is limited and specific methodologies for performing them are underdeveloped. In this call to action, we propose some methodological development activities that should be undertaken within this field of research to increase the potential of evidence syntheses to contribute to tackling pressing societal issues relating to livestock agriculture.

The dynamic nature of quadrotor flight introduces significant uncertainty in system parameters, such as thrust and drag factor. Consequently, operators grapple with escalating challenges in implementing real-time control actions. This study delves into an approach for estimating the model of quadrotor Unmanned Aerial Vehicles using Physics-Informed Neural Networks (PINNs) when you have a limited amount of data available. PINNs offer the potential to tackle issues like heightened non-linearities in low-inertia systems, elevated measurement noise, and constraints on data availability. The effectiveness of the estimator is showcased in a simulation environment with real data and juxtaposed with a state-of-the-art technique, such as the Extended Kalman Filter (EKF).

Nonlinear optimization (NOPT) is a meaningful tool for solving complex tasks in fields like engineering, economics, and operations research, among others. However, NOPT has problems when it comes to dealing with data variability and noisy input measurements that lead to incorrect solutions. Furthermore, nonlinear constraints may result in outcomes that are either infeasible or suboptimal, such as non-convex optimization. This paper introduces a novel regularized physics-informed neural network (RPINN) framework as a new NOPT tool for both supervised and unsupervised data-driven scenarios. By using custom activation functions and regularization penalties in an artificial neural network (ANN), RPINN can handle data variability and noisy inputs. Besides, it uses physics principles to build the network architecture, computing the optimization variables based on network weights and learned features. In addition, it uses automatic differentiation training to make the system scalable and cut down on computation time through batch-based back-propagation. The test results for both supervised and unsupervised NOPT tasks show that our RPINN can provide solutions that are competitive compared to state-of-the-art solvers. In turn, RPINN’s robustness against noisy input measurements makes it particularly valuable in environments with fluctuating information. Additionally, RPINN’s ANN-based foundation offers significant flexibility and scalability.

Introduction: The aim of this study was to prospectively evaluate parents’/guardians’ late recall of surgical procedures and risks of elective surgery after pre-operative interview with surgeons. Materials and Methods: All parents/guardians of patients <18 years of age undergoing elective procedures from January 15th to September 1st, 2022, were prospectively enrolled in the study. Before discharge, parents/guardians were asked to complete a questionnaire regarding the duration of the consent procedure, duration of the interview, quality of the informative handouts, and their ability to recall the type of disease, type of surgical procedure, and surgical risks. Answers were graded from 1 to 4. Results: One hundred and two questionnaires were returned. In all cases, informed consent was obtained between 24 and 72 hours prior to surgery. The following responses were collected: 98/102 (96%) parents/guardians reported that the duration of the consent process was adequate; 95/102 (93%) reported that the handouts were fully informative, and 7/102 (7%) reported that they were partially informative regarding explanation of the disease and surgical procedure; regarding complications, 93/102 (91%) perceived the handouts to be fully/partially informative, while 4/102 (4%) perceived the handouts to be poorly/non-informative, and 5/102 (5%) did not provide a response; 94/102 (92%) stated that they remembered the pathology, but only 87/94 (93%) recalled it correctly; 90/102 (88%) stated that they remembered the type of procedure, but only 76/90 (84%) recalled it correctly; and 53/102 (52%) stated that they remembered the surgical risks, but only 20/53 (38%) could recall more than one complication. Conclusions: Late recall of surgical complications by parents was poor despite the high perceived quality of the surgical risk handouts and medical interview.

Dementia is recognized as a disability under the United Nations Convention on the Rights of Persons with Disabilities (UNCRPD). People with disabilities like dementia have the right to access specialized health and social care services, including interventions that support independence and community participation. Cognitive Stimulation Therapy (CST) is an evidence-based psychosocial intervention that improves cognition, communication, confidence and quality of life for people living with dementia (PLwD), but an implementation gap means that CST is often not available. We recruited trained CST practitioners (n=62; 91.9% female) to a mixed methods study to examine facilitators and barriers to the implementation of CST in Ireland. Statistical analysis showed that 54.8% of practitioners had run CST following training; ratings of intervention efficacy predicted the likelihood of running CST groups (p=0.006); and seeing the benefits of CST first-hand predicted that practitioners would run a greater number of CST groups (p=0.01). Thematic analysis of qualitative data identified three key themes of ‘resources’, ‘awareness and education’, and ‘acceptability of CST’. Overall, the results show that while CST is acceptable and deemed highly effective, resources and staffing often impede implementation. The results are discussed in the context of prioritising the rights of people with disabilities and recommendations are made around improving access to evidence-based supports.

This article explores the profound implications of Dr. Anthony Fauci's testimony before the U.S. House Select Subcommittee on the Coronavirus Pandemic, where he admitted that key COVID-19 policies lacked scientific backing. By analyzing the foundation of these decisions, the piece delves into the broader issues of scientific evidence, hypothesis validation, and the societal consequences of implementing unverified measures. It raises critical questions about the role of scientific rigor and accountability in policy-making, urging a reflection on our approach to scientific inquiry and decision-making in public health.

Abstract: Background: Dental fear and uncooperative behavior can hinder dental treatment quality. Pediatric Procedural Sedation and Analgesia (PPSA) is used to facilitate treatment when coping capacity is exceeded. Out-of-hospital PPSA has been associated with more adverse outcomes compared to hospital-based settings. Updated Dutch PPSA guidelines recommending two anesthesiologists and a ventilator during out-of-hospital PPSA have increased costs and raised concerns about the accessibility to specialized high quality dental care for children in the Netherlands. Aim: This study aims to investigate the impact of the updated 2017 guideline on the occurrence rate of adverse events during PPSA, as recorded in the anesthesia complication database of twelve participating Dutch dental clinics, with adjustments made for age, weight, and duration of treatment. Method: Data of 25,872 children treated at 12 dental clinics performing out-of-hospital PPSA between 1997 and 2019 were analyzed. Eligible children were aged 2-18 with ASA classification I and II. A logistic two-level mixed-effects model is used to estimate the updated guideline’s impact on adverse events. Results: In the final model we analyzed the data of 21,759 children in nine dental clinics. The OR of the occurrence rate of an adverse event adjusted for age, weight, and duration of treatment was 0.75 (95% CI 0.64 – 0.89) after the implementation of the updated guideline, compared to before the updated guideline. This outcome was significant with a P=0.001, indicating a protective effect. The average odds per clinic of having an adverse event was 0.04 with a 3.81% chance. Conclusion: Our findings demonstrate a significant reduction in adverse events after the implementation of the updated guideline and highlight the importance of adherence to evidence-based guidelines in improving patient safety and the quality of care provided during pediatric procedural sedation and analgesia in out-of-hospital dental clinics. Our study underlines the necessity of considering the complex underlying mechanisms that play a role in the development and implementation of evidence-based practice and safety initiatives on the occurrence of adverse events.

Condition monitoring of rotating shafts is essential for ensuring the reliability and optimal performance of machinery in diverse industries. In this context, as industrial systems become increasingly complex, the need for efficient data processing techniques is paramount. Deep learning has emerged as a dominant approach due to its capacity to capture intricate data patterns and relationships. However, a prevalent challenge lies in the black-box nature of many deep learning algorithms, which often operate without adhering to the underlying physical characteristics intrinsic to the studied phenomena. To address this limitation and enhance the fusion of data-driven methodologies with the fundamental physics of the system under study, this paper leverages physics-informed neural networks (PINNs). Specifically, a simple but realistic numerical case study of an extended Jeffcott rotor model, encompassing damping effects and anisotropic supports for a more comprehensive modelling, is considered. PINNs are used for the estimation of five parameters that characterize the health state of the system. These parameters encompass the radial and angular position of the static unbalance due to the disk installed on the shaft, the stiffness along the principal axes of elasticity, and the non-rotating damping coefficient. The estimation is conducted solely by exploiting the displacement signals from the centre of the disk and, to showcase the efficacy and precision provided by this novel methodology, various scenarios involving different constant rotational speeds are examined. Additionally, the impact of noisy input data is also taken into account within the analysis.

This article presents the development of a Faith-Informed Advocacy Model that integrates faith-based principles and values into advocacy efforts within Christian social work. Drawing from the intersection of Christian ethics and social work principles, this model provides a framework for Christian social workers to engage in advocacy work that is grounded in their faith and guided by ethical principles. The article includes case studies that illustrate the application of the model in real-world scenarios.

Real estate market analysis and place-based decision-making can both benefit from understanding house price development. Although considerable amounts of interest have been devoted to housing price modelling, the assessment of house price fluctuation still requires further comparing studies. Housing price prediction is challenging as contributing factors are quite dynamic and subject to a variety of regulating elements. The future understanding of the housing market trends not only provides sufficient customers’ investment trust potential but also enables the financial support to progress more realistic in advance. In this study, a comprehensive data-informed framework is developed to investigate and anticipate real estate house prices using historical data by combining explanatory features. We examined about 500 houses in the Boston area as a case study and discussed how the increase in housing prices could vary by each of the contributing components. Fourteen Machine Learning (ML) regressors imply to the dataset and lead to a comparative study of the accuracy of all the models. ML-based regressors forecast real estate home prices as a function of thirteen influencing factors. The most informative features were also selected by conducting the Permutation Feature Importance technique on all the features The study provides a comprehensive tool for evaluating the robustness and efficiency of ML models for housing price predictions. The results highlighted Random Forest as the best model has an R2 equals to 0.88 and Voting Regressor as the second highest rated model has R2 equals to 0.87. Results of multivariate exploratory data analysis also implied that the average number of rooms and percentage of the lower status of the population have the most significant impact on the price range predictions.

Several years now, the global scientific community has accepted and recognized the importance of Evidence-based practice (EBP) for Nursing science. The main factor for the implementation of EBP is the competence of undergraduate Nursing students towards EBP, so that they as active nurses be ready for its application in their clinical practice, in order to provide better care for their patients. The aim of the present study is to examine the level of evidence-based practice competency evaluated with the self-reported EBP-COQ questionnaire. A quantitative study based on cross-sectional design was conducted from February to June 2022 in a convenience sample of Greek undergraduate nursing students. The Evidence-Based Practice Competence Questionnaire (EBP-COQ) and a questionnaire on undergraduate students’ gender, age, academic year, and training in the field of research were used. The SPSS 26.0 program was used to perform descriptive, bivariate and multivariate analyses. A total of 175 undergraduate students participated at the Hellenic University Nursing (Greece), specifically from the 2nd and 3rd academic year. The mean score of EBP-COQ for nursing undergraduate students was 3.03 ± 0.26, indicating moderate EBP. Among the EBP-COQ dimensions, the mean scores were obtained 3.03 ± 0.32 for Attitude toward EBP, 3.01 ± 0.49 for Skills in EBP, and 3.03 ± 0.49 for Knowledge in EBP. They were not found significant differences among participants’ mean EBP-COQ scores regarding gender (p = 0.766), age (p = 0.400), academic year (p = 0.153), and training in the field of Research Methodology (p = 0.538). It appears that the level of readiness towards EBP is mediocre between undergraduate nursing students at a university in Greece. Therefore, it is necessary to carry out new studies in the future, so that there is a correct approach of all the elements that contribute to the readiness of Nursing undergraduate students regarding EBP.

The implementation and sustainability of evidence-based practice (EBP) is a complex process. Best practice guidelines (BPGs) can facilitate the translation of knowledge from theory into practice, but they require working groups to adapt recommendations and encourage adherence to them. The aim of this study was to identify facilitators and barriers of BPGs in the Registered Nurses’ Association of Ontario®, within the framework of Best Practice Spotlight Organisations® (BPSOs®), in a hospital setting. The study was conducted using the modified Delphi method (two rounds) with a cohort of BPSO® Best Practice Champions. The facilitators and barriers included in the first round were identified through a bibliographic search. The degree of concordance was determined in the second round using the highest mean resulting values. A total of 44 nurses responded, with a mean age of 42.57±8.563. Facilitators included: Work Environment; Working in a hospital that encourages research; and Dedicating time to EBP (p&lt;0.001). Barriers: Excessive workload; Professional mobility (p&lt;0.001); and Lack of time (p=0.002). As such, it is necessary to approach human resource management by implementing new policies that guarantee systemic sustainability. The BPSO® programme is an excellent framework for promoting the translation of theoretical knowledge into practice and effecting institutional change.

This paper examines the marketing mix of rattan canes businesses, with a focus on small and medium enterprises (SMEs). Rattan cane enterprises rely on both domestic and international sources for raw materials, and the market is largely unorganized, with individual-level operations dominating the industry. The primary aim of this study is to evaluate the marketing mix in relation to the 7Ps framework, which includes price, product, place, promotion, people, process, and physical evidence. A simple random sampling method was employed to select participants from rattan processing enterprises. A questionnaire survey was then conducted among 46 rattan entrepreneurs, representing a total of 66 enterprises, including rattan processing industries, rattan traders, and Community Forest User Groups. The findings of the study indicate that, among the seven Ps, place and process are relatively stronger aspects of service delivery, while price and people are considered moderate. However, product, promotion, and physical evidence are identified as weak areas in terms of service delivery within the rattan business. It is concluded that rattan processing industries continue to rely on traditional tools and technologies, which has resulted in a lack of competitiveness in penetrating both national and international markets. Furthermore, poorly segmented distribution channels have hindered their sales efforts, while limited promotional activities have restricted awareness of their products among potential customers. In conclusion, the study highlights the need for rattan businesses to modernize their operations and adopt more effective marketing strategies in order to enhance their competitiveness and expand their market reach.

This article evaluates the reliability and consistency of nutrition- and supplementation-related advice for pregnant women provided by ten selected health-related Internet portals. The portals were chosen based on their perceived reliability and prominence in Google searches, with representation from both English and Polish language sources. The evaluation criteria included the adherence of the presented information to official recommendations and its evidence-based character based on specific items representing dietary aspects important in pregnancy. While the overall reliability was deemed acceptable, significant variations existed both among the portals and specific evaluated items. Notably, HealthLine, Medline Plus, and NCEZ emerged as the most evidence-based, while WebMD and Medycyna Praktyczna were identified as less reliable. Despite a number of issues, the analysed portals remain valuable sources of nutritional information for pregnant women, offering user-friendly accessibility superior to alternatives such as social media, on the one hand, and scientific articles, on the other. Improved consistency and attention to detail, especially in relation to vitamin intake and supplementation, would improve the overall quality of health portals.

Taiwan’s auditors have suffered from processing excessive audit data, including drawing audit evidence. This study advances sampling techniques by integrating machine learning with sampling. This machine learning integration helps avoid sampling bias, keep randomness and variability, and target the risker samples. We first apply a Naive Bayes classifier to classify data into some classes. Next, a user-based, item-based, or hybrid approach is employed to draw audit evidence. The representativeness index is the primary metric for measuring the representativeness of audit evidence. The user-based approach denotes the selection of samples between two percentiles in a class as audit evidence. It may be equivalent to a combination of monetary and variable sampling methods. The item-based approach represents the choice of risky samples as audit evidence. It may be identical to a combination of non-statistical and monetary sampling methods. Auditors can hybridize those user-based and item-based approaches to balance representativeness and riskiness in selecting audit evidence. Three experiments show that sampling using machine learning integration has the benefits of drawing unbiased samples, handling complex patterns, correlations, and unstructured data, and improving efficiency in sampling big data. However, the limitations are the classification accuracy output by machine learning algorithms and the range of prior probabilities.

Forensic assessments may involve the sampling of textile fibers when examining crime scenes. The need to characterize and identify those fibers is crucial as they can provide extensive information relating to a crime, linking a suspect to a location. Fibers in particular may contain issues in terms of both size and quantity of sample, and micro Fourier transform Infrared in attenuated total reflectance mode (micro FTIR-ATR) spectroscopy presents a non-destructive method to identify those fibers. In this study we carried out a rapid forensic assessment via micro FTIR-ATR of sixty textile fibers recovered from twenty white fabrics relying on tape lifting method, in order to discriminate those materials. Two dimensional principal component analysis and radar chart were applied to enhance the visual comparison of the fibers. Results of infrared spectra revealed that the technique allows the discrimination of textile fibers according to their spectral composition (cellulose, polyamide, polyester, or mixture of these composites) and to some characteristics, as number and width of peaks, peak position according to the wavenumber, absorbance index related to peak sharpness, etc. The technique was deemed useful in the forensic assessment of the fibers, presenting rapid and enlightening results.

A bijective analysis is confirming, the expansion of universal space has never been directly observed, and this assumption is unproven thus far. The measurement of the gravitational redshift, which was confirmed using the Mossbauer experiment, proves only the gravitational redshift and nothing more because there is no causality between the gravitational redshift and hypothetical expansion. Thus, even if the universal space is assumed to expand, the gravitational redshift cannot be assumed to be proof of the expansion. In addition, the Doppler effect was never observed in an expanding space; thus, the claim that the cosmological redshift is partially caused by the Doppler effect, which is caused by the expansion of space, is an unproven assumption. Furthermore, the discovery of cosmic microwave background (CMB) radiation simply proves that the radiation is emitted by the entire universal space, but does not prove the existence of a recombination period. In evidence-based cosmology, every element in the model has a corresponding element in physical reality that is observed and measured. The evidence-based cosmology model is related to the real universe by a bijective function of set theory. Supermassive black holes in the centre of galaxies are rejuvenating systems of the universe. They rotate their local superfluid space which is the physical origin of galactic rotation curves.

This paper outlines evidence-based practice in the context of professionalism, and highlights the contribution evidence-based practice can make to the professional practice of higher education course managers. Implications of the changing HE landscape for the status of academics as professionals are reviewed, and evidence-based practice is proposed as a solution for both enhanced course management and to remedy perceived deprofessionalisation. Finally, questions regarding researching professional practice within one’s own institution are addressed.

Introduction: Neoadjuvant chemotherapy in breast cancer offers the possibility to facilitate breast and axillary surgery, it is a test of chemosensibility in vivo with significant prognostic value and may tailor adjuvant treatment according to response. Material and Methods: a retrospective single institution of 482 stage II and III breast cancer patients treated with neoadjuvant chemotherapy based on anthracycline and taxans plus antiHEr2 in those Her2 positive cases. Survival was calculated at 5 and 10 years. Kaplan Meier with log rank test were calculated for difference according to TNM, pathological and molecular surrogated subtype, TILs cut-off 20%, response to chemotherapy and the presence of vascular invasion. Results: In the univariate those factors that influenced long-term patient outcome were TNM (p=0.000); pathologic subtype (p=0.003); Tils cut off 20%; achieving pCR (p=0.000), RCB (p=0.000) and vascular invasion (p=0.000). After a mean follow up of 126 months, Luminal tumors presented a substantial difference of survival rates calculated at 5 or 10 years (81.2% compared to 74.7%) whereas for TNBC was 75.3 and 73.5, respectively. The greatest difference was seen according to response those patients with pCR presented a 10yDDFS of 95.5 vs 72.4% for those patients without pCR. This difference was especially meaningful in TNBC were 10y DDFS according to the RCB 0 to 3 was 100%, 80.6%, 69% and 49.2% respectively. Patients with a special bad prognosis in our series were lobular carcinomas with 10y DDFS of 42.9% vs 79.7%, p= 0.001 for ductal carcinomas; and those patients with vascular invasion at the surgical specimen with a 10yDDFS of 59.2% vs 83.6, p=0.,000 for those patients without vascular invasion. Conclusion: long-term outcomes after neoadjuvant chemotherapy can help patient and clinician make well-informed decisions.

In this research, the application of the Physics-Informed Neural Network (PINN) model is explored to solve transport equation-based Partial Differential Equations (PDEs). The primary objective is to analyze the impact of different activation functions incorporated within the PINN model on its predictive performance, specifically assessing the Mean Squared Error (MSE) and Mean Absolute Error (MAE). The dataset used in the study consists of a varied set of input parameters related to strut diameter, unit cell size, and the corresponding yield stress values. Through this investigation, the aim is to understand the effectiveness of the PINN model and the significance of choosing appropriate activation functions for solving complex PDEs in real-world applications. The outcomes suggest that the choice of activation function may have minimal influence on the model's predictive accuracy for this particular problem. The PINN model showcases exceptional generalization capabilities, indicating its capacity to avoid overfitting with the provided dataset. The research underscores the importance of striking a balance between performance and computational efficiency while selecting an activation function for specific real-world applications. These valuable findings contribute to advancing the understanding and potential adoption of PINN as an effective tool for solving challenging PDEs in diverse scientific and engineering domains.

Background: Gram-negative bacteremia, especially from Klebsiella spp., Escherichia coli, and Pseudomonas aeruginosa, poses a significant public health threat due to multidrug resistance and high mortality rates. Cost-effective treatments are essential in low-middle income countries as Colombia. Methods: This descriptive, phase IV study was conducted in a tertiary care hospital in Colombia. Adult patients with Gram-negative bacteremia treated with generic piperacillin/tazobactam (Penibactam®) were evaluated. Clinical and microbiological responses were assessed over 12 months. Results: The study included 30 critically ill patients with an average APACHE II score of 22.12. The clinical response at day 7 was 66% (n = 20), and the microbiological response was 56.7% (n = 17). By the end of follow-up, clinical improvement was observed in 63.3% (n = 19), with a mortality rate of 23% (n = 7). The cardiovascular system was the most common infection site. Conclusions: Generic piperacillin/tazobactam demonstrated effectiveness and safety comparable to existing literature in treating Gram-negative bacteremia, offering a viable, cost-effective alternative for critically ill patients in real-world settings.

This study addresses the importance of conducting mass movement susceptibility mapping and hazard assessment using quantitative techniques, including machine learning, in the Northern Lima Commonwealth (NLC). Previous exploration of the topographic variables revealed high correlation and multicollinearity among some of them, which led to dimensionality reduction through principal component analysis (PCA). Six susceptibility models were generated using weights of evidence, logistic regression, multilayer perceptron, support vector machine, random forest, and naive Bayes methods to produce quantitative susceptibility maps and assess the hazard associated with two scenarios: the first being an El Niño phenomenon and the second being an earthquake exceeding 8.8Mw. The main findings indicate that machine learning models exhibit excellent predictive performance for the presence and absence of mass movement events, as all models surpassed an AUC value of &gt;0.9, with the random forest model standing out. In terms of hazard levels, in the event of an El Niño phenomenon or an earthquake exceeding 8.8Mw, approximately 40% of the NLC area would be exposed to the highest hazard levels. It also highlights the importance of integrating methodologies to reduce model uncertainty, such as comprehensive analysis of variables supported by high quality data and organized workflows. The findings of this research are expected to serve as a supportive tool for land managers in formulating effective disaster prevention and risk reduction strategies.

Nurses’ competency towards Evidence-based Practice (EBP) has been extensively investigated by several studies worldwide. However, factors affecting the competence of Greek nurses working in the NHS have not been fully investigated in terms of EBP. Thus, this study aimed to explore the impact of individual qualifications of nurses on their competence towards EBP. Data from 473 registered nurses working in 10 hospitals of in the Greek National Health Service (NHS) were collected between October and December 2022 using a convenience-sampling method in a cross-section design. The Greek version of the 35-item (5-point Likert scale) Evidence-Based Practice Competency Questionnaire for Professional Registered Nurses (EBP-COQ Prof) was used to assess the competence level of nurses focusing on the attitudes, skills, and knowledge as well as the utilization of EBP in clinical practice. One-way ANOVA and Pearson coefficient tests were applied to compare possible differences among variables (two or more groups) as appropriate. A multi-factorial regression model was applied to explore participants’ qualifications, including demographics (MSc degree, gender, English language knowledge, etc.) as independent variables, and to control for potential confounding effects toward EBP competency. P-values &lt;0.05 were considered statistically significant. The mean age of 473 participants (402 women and 71 men) was 44.7 ± 9.2 years old. The mean value of competence subscales was found as follows: attitudes 3.9±0.6, knowledge 3.7±0.6, skills 3.1±0.8, and utilization 3.4±0.7. A multivariate regression analysis revealed that associates of “Master's degree” (t=3.039, p=0.003), “Writing an academic article” (3.409, p=0.001), “Working in a University clinic” (2.203, p=0.028) and, “Computer Skills” (2.404, p=0.017) are positively affecting ‘Attitudes’, ‘Knowledge’, ‘Skills’ and ‘Utilization’. Respectively. The research data suggest that nurses working in Greek NHS are limited in competence towards EBP in comparison to other European countries. Therefore, Vocational, Educational and Training programs tailored to EBP enhancement are crucially important.

Smart local energy systems (SLES) have been promoted in policy as a solution to decarbonisation challenges which also bring wider benefits, such as community prosperity and energy affordability. But the combination of conditions required to enable their successful emergence and operation are still to be elaborated. This paper reports on the development of a Theory of Change (ToC) for the “societal project” of emergence of SLES with benefits. ToC is a process of making explicit the causal links by which activities lead to outcomes, surfacing assumptions, and recognising possible unintended consequences. We describe the ToC development process, involving consultation and collaboration across a research consortium. It consists of layers (e.g. users, skills, data and digital), and shows conditions considered necessary to deliver SLES, and for these to deliver wider benefits. It also provides interactive links to evidence emerging from the consortium, as well as policy/governance conditions and metrics. We reflect on potential uses of the ToC – internally to the consortium and externally – along with challenges we encountered in pursuing this approach. Policy implications relate to the importance of enabling conditions across multiple sectors, the absence of any of which could inhibit delivery of either SLES or their ensuing benefits.

The latest World Atlas of Desertification represents a turning point in the diagnosis of desertification. While it forgoes desertification mapping due to the intrinsic complexity of the phenomenon and the impossibility of measuring it using a single indicator, it introduces the Convergence of Evidence paradigm, which identifies socioeconomic and biophysical variables whose behavior allows pointing out those areas prone to desertification. The Spanish National Action Program Against Desertification (PAND), back to 2008, already implemented a similar approach to identify five “desertification landscapes” within Spain using both socio-economic and climatic information. The PAND was not only pioneering but also, unfortunately, accurate. Desertification in Spain has continued to worsen and the first two decades of the 21st century have consolidated an agri-food model whose dynamics have exacerbated the desertification processes identified in the PAND. Despite its scientific value, the PAND lacked a proper action plan and was completely detached from the diagnosis. As a result the diagnosis it provided was not followed by effective actions to halt desertification in Spain. The Spanish Government's recent declaration of climate and environmental emergency requires a new strategy to combat desertification. This commitment is an excellent opportunity to update the diagnosis of the situation and, more crucially, to unify the different desertification sectoral policies and actions under a single front. We provide here elements for a roadmap to be designed around the pressures, impacts, and drivers that define today's Spanish desertification landscapes to manage them effectively and avoid their further degradation.

Decision-making using evidence-based practice (EBP) is generally universally accepted by nurses. Such acceptance may affect the personnel’s behaviour towards patients, which is also demonstrated by taking into consideration the patient’s preferences, including the patient’s spiritual needs, in the care plan. The provision of such care requires the development of an attitude of approval and an adequate level of communicative competence, which will enable the actual implementation of the EBP. The purpose of our study was to assess the perception of spirituality and the nurse’s role in providing spiritual care, as well as the perception of the significance of communication skills in the approval of EBP in professional practice. A multi-centre cross-section study was conducted on a population of 1176 participants (459 undergraduate (Bachelor programme, BP) and 717 postgraduate students (Master programme, MP)) from 10 medical universities in Poland. Three tools were used in the study to evaluate the participants’ approach: Evidence-Based Practice Competence Questionnaire (EBP-COQ), The Spirituality and Spiritual Care Rating Scale (SSCRS), and Communication Skills Attitude Scale (CSAS). Structural equation modelling was used for the analysis. An analysis of structural equations revealed the presence of positive relationships of the attitude to spiritual care and the role of communicative competences with the approach to EBP regardless of the cohort. A significant difference was found related to the influence of age on the attitude toward learning communicative competences. The approval in this respect was observed to decrease with age in the MP group. Increasing approval of EBP requires strengthening the approach to activity-centred spiritual care, with the simultaneous development of a positive attitude towards learning communicative competences. The model reveals the need to integrate a humanistic approach with EBP, which can be achieved by planning different interventions in different groups of recipients: nurses, academic teachers and students.

Background: It is clear that breastfeeding is the gold standard of infant feeding because of the many advantages it offers to both the child and the mother. Objective: to identity the main reasons for cessation breastfeeding declares by the mother themselves during the first year. Design: A prospective cohort study was conducted, recruiting 969 newborns in a third level hospital in Spain. The main maternal variables studied were: maternal age, parity, educational level, work occupation, smoking habit, gestational age, birth, weigh, feeding type, and duration of breastfeeding. All the participants were followed for a year to determinate the duration of breastfeeding and to know the reason of the abandonment. Results: At 6 months, the percentage of maternal lactation was cut in half and only 24.6% of these mothers maintain. Mainly 15.80% of the mothers decide to give up the exclusive maternal lactation of their own free desire, and 15.41% because they suspect hypogalactia. The work cause is the third reason of abandonment in both cases. Conclusions: Our results show the need to improve the health policies of promotion, protection and support the initiation of breastfeeding. In particular, our results show the importance of the work factor with particular emphasis on improving conciliation measures.

For most of its history, archaeology has taken an indulgent attitude toward looting and antiquities trafficking. The primary response to these dangers has been to publish the main findings made outside of academia. As a result of this approach and the prominent role played by police techniques in investigating such crimes, investigations are primarily based on documentary research. This approach makes it harder to determine such essential factors in this field as an object’s collecting history or discovery date. This paper offers an overview of the state of the research on the fight against antiquities trafficking. It then proposes new ways of studying collecting history, drawing on research projects on the use of archaeometry to shed light on cases of looting or trafficking involving police, court, or government intervention; hence, its qualification as “forensic.” Although the current state of knowledge does not enable the presentation of novel research, we believe that researchers and interested institutions should be made aware of the advisability of using archaeometry more directly in the fight against these scourges.

This paper investigates the use of Physics-Informed Neural Networks (PINNs) in active learning cycles. We defined two scenarios: one semi-supervised and the other fully supervised. PINNs emphasize the integration of physical laws into neural networks to improve the predictive performance of vanilla neural networks and to enhance the efficiency of traditional methods for solving partial differential equations (PDEs). Key contributions include adapting existing computational frameworks to enable the use of Graph Neural Networks for solving problems that require the calculation of gradients on unstructured triangle meshes, a query strategy focusing on the physical loss, and a comparative analysis of this strategy against random sampling across both defined scenarios. This work establishes a foundation for future research aimed at expanding the application of Physics-Informed Graph Neural Networks (PIGNN) using active learning and addressing real-world problems in fluid dynamics and electrodynamics.

As modern systems are becoming more complex their control strategy cannot longer relay only on measurement information that usually comes from probes but also from mathematical models. Those systems models can lead to unbearable computation times due to their own complexity, turning the control process non-viable, which leads to the implementation of surrogate models that enable to achieve estimates within acceptable time to take decisions. A control trained with Deep Reinforcement Learning algorithm, using a Physics-Informed Neural Network to obtain the temperature map on the following time step, replaces the need of running Direct Numerical Simulations. On this work we considered an 1D heat conduction problem, which temperature distribution feeds a control system to activate a heat source aiming to obtain a constant, previously defined, temperature value. With this approach, control training becomes much faster without the need of performing numerical simulations or laboratory measurements, as well the control is taken based on Neural Network enabling its implementation on simple processors to edge computing.

This article presents a detailed examination of the methodology and modeling tools utilized to analyze gas flow in pipelines, rooted in the fundamental principles of gas dynamics. The methodology integrates numerical simulations with modern neural network techniques, particularly focusing on the Physics-Informed Neural Network (PINN) method. This innovative approach combines artificial neural networks (ANNs) with physical equations, offering a more efficient and accurate way to model various complex processes and phenomena. The proposed mathematical model, based on the Euler equation, has been meticulously implemented using the Python language. Verification with analytical solutions ensures the accuracy and reliability of the computations. In the research, a comprehensive comparative analysis was conducted between results obtained using the PINN method and those from conventional Computational Fluid Dynamics (CFD) approaches. The analysis highlighted the advantages of the PINN method, which produced smoother pressure and velocity fluctuation profiles while reducing computation time, demonstrating its potential as a transformative modeling tool. The data derived from this study are of paramount importance for ensuring ongoing energy supply reliability and can also be used to create predictive models related to gas behavior in pipelines. The application of modeling techniques for gas flow simulation has the potential to revolutionize the integrity of our energy infrastructure and utilization of gas resources. However, it is crucial to emphasize that the effectiveness of such models relies on continuous monitoring and frequent updates to ensure alignment with real-world conditions. This research not only contributes to a deeper understanding of compressible gas flows but also underscores the crucial role of advanced modeling methodologies in the sustainable management of gas resources for both current and future generations.

This study aimed at exploring what role artificial intelligence techniques could play in the futural numerical analysis. In this paper, a convolutional neural network techniques based on modified loss function is proposed as a surrogate of finite element method(FEM). Several surrogate-based physics-informed neural networks(PINNs) are developed to solve representative boundary value problems based on elliptic partial differential equations (PDEs). Results from the proposed surrogate-based approach are in good agreement with ones from conventional FEM. It is found that modification of the loss function could improve the prediction accuracy of the neural network. It is indicated that to some extent the artificial intelligence technique could replace conventional numerical analysis as a great surrogate model.

Aim: The overall aim of the study was to assess the reasons and experiences of participants involved in Antibody Mediated Prevention (AMP) HIV prevention clinical trial at University of North Carolina (UNC) Project, Lilongwe, Malawi. We determined the participants’ reasons for participating in HIV Prevention clinical trials; and the experiences of participants in HIV Prevention clinical trials. Methods: We adopted the qualitative cross-sectional study method. Data were collected using in-depth interviews (IDIs). Purposive sampling was used to select 12 study participants who consented to take part in the study. All participants were the ones taking part in the AMP HIV prevention study at the UNC Project. Data analysis was done concurrently with data collection using content analysis. Results: Individuals were motivated to participate in HIV research due to a range of perceived benefits. These included personal, health, and financial benefits. Participants' research experiences and their continued participation in HIV research were influenced by the research clinic context and the nature of their interactions with research staff. Conclusion: When the clinical trial study participants’ expectations are met through what they experience in the study, the chances of them adhering to the study visits and procedures are high. Even for those who did not have any expectations prior to the study, feeling welcomed and being able to open up to the study staff encouraged their continued participation. In the end, this outweighed the negative comments made by the people in their communities or their friends

The therapeutic management of acquired thrombotic thrombocytopenic purpura (aTTP) has recently benefited from the introduction of caplacizumab, an agent directed to inhibition of platelet aggregation. This real-world analysis investigated the epidemiology, the demographic and clinical characteristics of aTTP patients in Italy before and after caplacizumab introduction in 2020. Hospitalized adults with aTTP were included using the administrative databases of healthcare entities covering 17 million residents. Epidemiological estimates of aTTP considered the 3-year period before and after caplacizumab introduction. After stratification by treatment or not with caplacizumab, aTTP patients were characterized for their demographic and clinical features. The annual incidence before and after 2020 was estimated in the range 4.3-5.8 cases/million and 3.6-4.6/million, respectively. From 2018 to 2022, 393 patients with aTTP were included, 42 of them treated with caplacizumab. Caplacizumab-treated patients were aged on average 46.8 years, 31% were males, and showed tendentially better clinical outcomes: no treated patients died at either 1 month or 3 months after caplacizumab treatment initiation, compared to 10.5% (1 month) and 11.1% (3 months) mortality among the untreated. Caplacizumab treatment was associated with a trend towards shorter hospital stays. These findings suggest that caplacizumab advent provided clinical and survival benefits for patients with aTTP.

Dietary guidance to recommend food patterns to deliver nutrients has been well accepted for the past century. Foods to deliver essential nutrients and energy are needed across the life cycle and are dependent upon local industries and accepted cultural practices to deliver essential nutrients to prevent nutrient deficiency diseases. Since the 1980s, dietary guidelines to prevent chronic diseases have relied on epidemiological research to predict what dietary patterns are linked to reduced risk of chronic disease or links to health outcomes. Dietary guidelines have been broad, typically recommending avoiding “too much sugar”, “too much saturated fat”, and “too much sodium”. Efforts to fine tune these recommendations have met with limited scientific data to support more specific recommendations across the life cycle. Consumers have become skeptical of dietary guidelines as media coverage of new studies is often in conflict with accepted nutrition dogma. We discuss whether we really have a science-based databank to support dietary guidelines, based on a scientific session at the 10th International Conference on Nutrition and Growth.

Diabetes mellitus, a metabolic disorder that is persistent in nature, is identified by an elevated concentration of glucose in the bloodstream. The disease has become a major public health problem, affecting millions of people globally, and is associated with significant morbidity and mortality. The increasing prevalence of diabetes and the lack of effective therapeutic interventions have led to the exploration of alternative and complementary forms of therapy, including ethnomedicine. Ethnomedicine is the study of traditional medical systems and the medicinal plants used by Indigenous communities. In other words, Ethnomedicine refers to the traditional medical practices and beliefs of a particular cultural or ethnic group. The most popular example of traditional medicine is the use of Aloe vera for the treatment of various diseases such as sunburn, cuts, burns, hypertension, diabetes, and jaundice for thousands of years [1]. In recent years, there has been increasing interest in the potential role of ethnomedicine in the management of diabetes, given its widespread use in many communities and its potential to offer cost-effective and culturally acceptable treatment options. It encompasses a range of traditional healing practices, including herbalism, acupuncture, and massage therapy. Despite the inconsistencies, there is growing evidence to suggest that certain ethnomedical practices may be effective in the management of diabetes [2,3]. For example, several studies have shown that certain herbal remedies, such as cinnamon, ginseng, and bitter melon, may have potential benefits in terms of improving glucose control and reducing the risk of complications associated with diabetes. Evidence-based ethnomedicine is an approach that applies scientific principles and methods to evaluate the efficacy and safety of traditional medical practices. This approach is crucial in the development of effective and safe ways to treat diabetes. To date, there have been numerous studies conducted to examine the efficacy and safety of various ethnomedical interventions for diabetes, including herbal remedies, dietary changes, and physical activity [4]. However, it is important to note that not all ethnomedical interventions for diabetes have been thoroughly evaluated, and many remain unproven. Furthermore, some interventions may be associated with significant risks, particularly in terms of drug interactions and adverse effects. As such, it is crucial that any ethnomedical interventions for diabetes be carefully evaluated using rigorous scientific methods and that patients be advised to consult with their healthcare provider before using any such interventions. It is important to note that the results of these studies have been mixed, with some showing promising results, while others have failed to demonstrate significant benefits. In this chapter, the evidence-based current state of ethnomedicine for diabetes was reviewed. We first provide an overview of diabetes and its pathophysiology, followed by a discussion of traditional medicinal practices used to treat the disease. We then present a systematic review of the scientific literature on the use of ethnomedicine for diabetes, focusing on herbal remedies and dietary interventions. Our review found that several medicinal plants offered promising results to treat all types of diabetes. For example, Gymnema sylvestre, a traditional Indian herb, has been shown to reduce blood glucose levels and improve insulin sensitivity [5]. Additionally, the use of bitter melon (Momordica charantia) has been shown to reduce fasting blood glucose levels and improve glucose tolerance in individuals with type 2 diabetes [6]. Another important aspect of ethnomedicine for diabetes is the use of dietary interventions [7]. Traditional diets, such as the Mediterranean diet, have been shown to have beneficial particularly in terms of improving glycaemic control, reducing the risk of cardiovascular disease, and insulin sensitivity [8]. Additionally, the use of low-carbohydrate diets, such as the ketogenic diet, has been shown to improve glycaemic control in individuals with type 2 diabetes [9]. Traditional medicine has a long history of treating the symptoms and managing the condition. However, the efficacy and safety of these traditional practices have not always been scientifically tested, and there is a lack of standardization and regulation in the production and use of traditional remedies for diabetes. This creates a challenge for healthcare providers and patients, who need to ensure that the treatments they use are safe and effective. Additionally, there is a need for further research to understand the mechanisms by which traditional remedies may be effective in managing diabetes and to determine the optimal dosage and administration methods [9]. Finally, it is also important to understand that the use of traditional remedies may interact with conventional medications, leading to adverse effects; for example, the use of Chinese traditional medicine, known as “herb ginseng,” for the treatment of various health conditions, including diabetes sometimes cause the side effect such as dizziness, confusion, and fainting while interacting with the anti-diabetic medications [10]. Therefore, it is important to approach the use of ethnomedicine for diabetes with caution and under the guidance of a qualified healthcare professional. In conclusion, evidence-based ethnomedicine offers a promising approach to the treatment of diabetes. While further research is needed to fully understand the mechanisms and safety of these interventions, the results to date suggest that traditional remedies and dietary interventions have the potential to be effective in the management of this disease. However, it is important to approach the use of ethnomedicine for diabetes with caution and under the guidance of a qualified healthcare professional and to continue to conduct rigorous scientific research to fully understand the benefits and risks associated with these interventions.

Systematic reviews of 591 primary studies of the modes of transmission for SARS-CoV-2 show significant methodological shortcomings and heterogeneity in the design, conduct, testing and reporting of SARS-CoV-2 transmission. While this is partly understandable at the outset of a pandemic, evidence rules of proof for assessing the transmission of this virus are needed for pre-sent and future pandemics of viral respiratory pathogens. We review the history of causality as-sessment related to microbial etiologies with a focus on respiratory viruses and suggest a hierar-chy of evidence to integrate clinical, epidemiologic, molecular and laboratory perspectives on transmission. The hierarchy, if applied to future studies, should narrow the uncertainty over the twin concepts of causality and transmission of human respiratory viruses. We attempt to address the translational gap between the current research evidence and the assessment of causality in the transmission of respiratory viruses with a focus on SARS-CoV-2. Experimentation, consistency and independent replication of research alongside our proposed framework provide a chain of evidence that can reduce the uncertainty over the transmission of respiratory viruses and increase the level of confidence in specific modes of transmission, informing the measures that should be undertaken to prevent transmission.

Introduction: Dementia is a cognitive decline with patients often exhibit behavioural and psychological symptoms, severely affecting the quality of life and placing a heavy burden on caregivers. Acupressure has reported benefits for dementia. This study aims to critically review the available evidence for its use as a non-pharmacological therapy. Methods: Systematic search of major research databases for human clinical trials using acupressure as an intervention for dementia patients was conducted. Results were synthesised for the effects of acupressure on various outcome measures of interest for dementia.Results: Twelve clinical trials (N=973), including eight randomised control studies, were included in this review. The study sample was predominantly institutionalised residents with moderate to severe dementia. Baihui (GV20), Shenmen (HT7), Fengchi (GB20), Neiguan (PC6), Sanyinjiao (SP6), and Yingtang (EX-HN3) were the most used acupoints for intervention. Acupressure techniques employed in these clinical trials vary greatly with no standardised approach. This review finds inconsistent evidence in the effectiveness of acupressure in reducing agitation and behavioural disturbances. However, the treatment appears to improve their ease of care and reduce physical stress. Affixing acupressure devices on selected acupoints can also potentially improve psychiatric pain, anxiety, and depression. Long-term (6 months) treatment can potentially improve the cognitive function, activities of daily living, and quality of life of patients with mild to moderate dementia. The effect of acupressure on sleep disturbances remains unclear. Conclusion: More high-quality research on acupressure is needed to fill the gaps in knowledge and inform better care for dementia patients in the future.

This survey paper discusses the Cointegrated VAR methodology and how it has evolved over the last 30 years. The first section is a description of major steps in the econometric development of the CVAR model that facilitated serious real world applications. The next three sections are primarily methodological and discuss (i) difficulties and puzzles when confronting theory with the data, (ii) the formulation of a viable link between theory and the data, a so called theory-consistent CVAR scenario, and (iii) how all this was inspired by Trygve Haavelmo and his Nobel prize winning monograph "The Probability Approach to Economics". The next two sections discuss early applications of the Cointegrated VAR model to monetary transmission mechanisms, international transmission mechanisms and wage, price and unemployment dynamics. They report puzzling evidence, discuss the need for new theory, and propose a method for combining partial CVAR analyses into a larger macroeconomic model. The following sections propose a new, empirically-based, approach to macroeconomics in which imperfect knowledge based expectations replace so called rational expectations and in which the financial sector plays a key role for understanding the long persistent movements in the data. The last section argues that the CVAR can act as a "design of experiment for passive observations" and illustrates with several applications including unemployment dynamics under crises periods and aid effectiveness in South Saharan African countries.

{This study investigates the coordinated behaviors and the underlying collective intelligence in biological groups, particularly those led by informed leaders. By establishing new convergence criteria based on real biological group experiments, this research introduces the concept of a volitional term and heterogeneous networks, constructing a coupled force Cucker-Smale model with informed leaders. The inclusion of the informed leader aspect distinctively characterizes the dynamics of leaders and followers, enabling a more accurate representation of biological group behavior. The paper then proposes the Flock Leadership Hierarchy Network (FLH), a model reflecting real biological interactions. By employing time slicing and rolling time windows, the study methodically analyses group behavior stages, using volatility and convergence time as metrics to examine the relationship between group consistency and interactions. Comparative experiments show the FLH network's superior performance. The Kolmogorov-Smirnov test indicates that the FLH network follows a power-law distribution, highlighting the evolutionary intelligence of biological groups. This research offers new perspectives on the evolution of biological groups, contributing to our understanding of both natural and artificial systems, such as animal migration and autonomous drone operations.

Partial differential equations (PDEs) are used to describe a wide range of phenomena, such as heat transfer, fluid dynamics, and quantum mechanics. By solving PDEs, we can ob- tain insights into the behavior of the system and make predictions about its future evolution. Conventional numerical methods for obtaining the approximate solutions of PDEs may re- quire extensive computational resources and time, especially for complex PDEs and large- scale problems. The recently developed physics-informed neural network (PINN) has shown promise in a variety of scientific and engineering fields by incorporating physical laws into the loss functions of the neural network (NN). In addition, the training of PINN does not require ground truth data, but it has poor generalization ability to unseen domains. On the other hand, a convolutional neural network (CNN) has fast inference and better generalization ability, but it requires a large amount of training data. Taking the advantages of PINN and CNN by using Legendre multiwavelets (LMWs) as basis functions, we introduce a new method to approach the PDEs in this paper, namely Physics-Informed Legendre Multiwavelets CNN (PiLMWs-CNN), in order to continuously approximate a grid-based state representation that can be handled by a CNN. PiLMWs-CNN enable us to train our models using only physics-informed loss functions without any pre- computed training data, simultaneously providing fast and continuous solutions that gener- alize to previously unknown domains. In particular, the LMWs can simultaneously possess compact support, orthogonality, symmetry, high smoothness, and high approximation order. Compared to orthonormal polynomial (OP) bases, the approximation accuracy can be greatly increased and computation costs can be significantly reduced by using LMWs. We applied PiLMWs-CNN to approximate the damped wave equation, incompressible Navier-Stokes (N- S) equation, and two-dimensional heat conduction equation. The experimental results show that this method provides more accurate, efficient, and fast convergence with better stability when approximating the solution of PDEs.

Despite the recognition of the importance of stakeholder inclusion into decisions about new solutions offered to society, responsible innovation (RI) has stalled at the point of articulating a process of governance with a strongly normative loading, without clear practical guidelines toward implementation practices. The principles of RI direct us to involve the user early in the innovation process. However, it lacks direction of how to involve users and stakeholders into this process. In this article, we try to understand how to empower users to become a part of innovation process though empirical cases. Based on 11 cases of firms innovating in digital health and welfare services, we look on firm practices for user integration into their innovation process, as well as how user’s behavior is changing due to new trends such as availability of information and digitalization of services. We try to explore this question through lenses of responsible innovation in the emerging field of digital healthcare.

Canada is known to have a complex pathway for new drug approval and reimbursement resulting in delayed access to patients with serious and life-threatening diseases such as cancer. Several recent publications from key stakeholders including patients, physicians and policymakers highlight patient helplessness, physician frustrations and policymakers entangled in a massive network of bureaucracy unable to make headway. Several quantitative assessments using time from regulatory approvals to successful reimbursements confirm long review times and high rejection rates for oncology drugs especially those receiving conditional approvals. A consensus forum of 18 Canadian oncology clinicians recently voiced frustration with the process and inability to deliver guideline-supported efficacious therapies to their patients. This manuscript compares data extracted from publicly available data sources from 2019 to June 2024 to previous publications. Methods: Public databases from Health Canada, the Canadian Agency for Drugs and Technologies in Health (CADTH), which is in the process of changing to Canada’s Drug Agency, and the pan-Canadian Pharmaceutical Alliance (pCPA) were reviewed and data collected were analyzed with descriptive statistics. Results: From the data, three trends emerge, i) an increasing number of oncology drugs are receiving conditional approvals from Health Canada, ii) the percentage of conditionally approved oncology drugs receiving positive reimbursement recommendations from CADTH is still low but appears to be improving, but delays in access are now contingent upon pCPA deciding whether to negotiate price and then the duration of any negotiation, and iii) real-world evidence is no longer part of decision-making for conditional approvals. A slight increase in positive endorsement of RWE used to support CADTH recommendations was observed. Conclusion: Lack of timely access to oncology drugs hurts Canadian patients. While a small trend of improvement appears to be emerging, longer-term data collection is required to ensure sustained patient benefits.

Tunnel fires are generally detected using various sensors, including measuring temperature, CO concentration, and smoke concentration. To address the ambiguity and inconsistency in multi-sensor data, this paper proposes a tunnel fire detection method based on an improved Dempster-Shafer (DS) evidence theory for multi-sensor data fusion. To solve the problem of evidence conflict in DS theory, a two-level multi-sensor data fusion framework is adopted. The first level of fusion involves feature fusion of the same type of sensor data, removing ambiguous data to obtain characteristic data, and calculating the basic probability assignment (BPA) function through the feature interval. The second level fusion derives basic probability numbers from the BPA, calculates the degree of evidence conflict, normalizes to obtain the relative conflict degree, and optimizes the BPA using the trust coefficient. The classical DS evidence theory is then used to integrate and obtain the probability of tunnel fire occurrence. Different heat release rates, tunnel wind speeds, and fire locations are set, forming four fire scenarios. Sensor monitoring data under each simulation condition are extracted and fused using the improved DS evidence theory. The results show that the proposed improved DS evidence theory method detects the probability of fire occurrence in the four scenarios as 67.5%, 71.0%, 82.8%, and 83.5%, respectively, and identifies fire occurrence in approximately 2.4 seconds, an improvement of 64.7% to 70% over traditional methods. This demonstrates the feasibility and superiority of the proposed method, highlighting its significant importance in ensuring personnel safety.

Lung cancer, globally recognized as the leading cause of cancer-related mortality, presents a pressing need for innovative treatment strategies. Increasing evidence points towards the potential of monoamine oxidase (MAO) inhibitors in modulating cancer progression, primarily due to their role in regulating cellular metabolism and apoptotic pathways. This extraordinary study delves into the multifaceted therapeutic implications of MAO inhibitors in lung cancer management, elucidating their ability to pave the way for clinical advancement via novel targets and pathways. Capitalizing on recent advances in molecular biology, this research explores the molecular mechanisms underlying the anticancer actions of MAO inhibitors. By examining their impact on cell proliferation, migration, invasion, and apoptosis in lung cancer cell lines, we provide insight into the intricate signaling pathways and biochemical processes modulated by these compounds. The study also investigates the synergistic effects of MAO inhibitors in combination with established chemotherapeutic agents, elucidating their potential to enhance treatment efficacy and reduce cytotoxicity. Furthermore, we delve into the implications of MAO inhibitors on the tumor microenvironment, evaluating their influence on angiogenesis, immune regulation, and the epithelial-mesenchymal transition. This comprehensive analysis enables the identification of novel molecular targets, which hold promise for the development of advanced therapeutics tailored for specific lung cancer subtypes. To substantiate our findings, in this review, we thoroughly analyze the existing literature using state-of-the-art bioinformatics tools." in vivo orthotopic lung cancer models, and clinical patient cohorts. This robust and multidisciplinary approach permits the generation of clinical evidence supporting the integration of MAO inhibitors in lung cancer management. Conclusion: This ground-breaking study establishes the therapeutic potential of monoamine oxidase inhibitors in lung cancer management by unearthing novel pathways and targets for clinical advancement. The outcomes open doors for the development of innovative treatment strategies and inspire continued research efforts to combat the global burden of lung cancer.

Evidence-based data for endometriosis management are limited. Experiments are excluded without adequate animal models. Data are limited to symptomatic women and occasional observations. Hormonal medical therapy cannot be blinded if recognised by the patient. Randomised controlled trials are not realistic for surgery since a variable disease with low numbers. Each diagnosis and treatment is an experiment with an outcome and experience is Bayesian updating from the past. If the experience of many is similar, this has more value than an opinion. The combined experience of a group of endometriosis surgeons was used to discuss problems in the management of endometriosis. Considering endometriosis as several genetically/epigenetically different diseases is important for medical therapy. Imaging cannot exclude endometriosis and diagnostic accuracy is limited for superficial lesions, deep lesions, and cystic corpora lutea. Surgery should not be avoided for emotional reasons. Shifting infertility treatment to IVF without considering fertility surgery is questionable. The concept of complete excision should be reconsidered. Surgeons should introduce quality control and teaching should move to explain why it is done. The perception of information has a personal bias. These are the major problems in the management of endometriosis identified by the combined experience of endometriosis surgeons.

Acute myeloid leukemia (AML) is a hematological malignancy that predominantly affects the elderly. Prognosis declines with age. For those who cannot tolerate intensive chemotherapy, historically established treatment options have been hypomethylating agents (HMAs), low dose cytarabine (LDAC), and best supportive care (BSC). As the standard of care evolves for those unfit for intensive chemotherapy, there is a need to understand established treatment pathways, clinical outcomes and healthcare resource utilization in Canada. The CURRENT study was a retrospective chart review of AML patients not eligible for intensive chemotherapy who initiated first-line treatment between 1 January 2015 and 31 December 2018. Data were collected from 170 Canadian patients treated at six hematology centers, of whom 118 received systemic therapy and 52 received BSC as first-line treatment. Median overall survival was 8.58 months and varied from 2.96 months for BSC to 13.31 months for HMAs. Over 80% of patients had at least one outpatient visit, and 67% of patients receiving systemic therapy and 71% of those receiving BSC had at least one admission to hospital, during their first line of therapy. A total of 96 (81.4%) patients receiving first line systemic therapy and 39 (75.0%) of those receiving first line BSC had at least one red blood cell or platelet transfusion. These findings highlight the unmet need for novel therapies for patients ineligible for intensive chemotherapy.

Background/Objectives: Informed consent for anesthesia poses both legal challenges and problems of understandable communication. Fulfilling all requirements through anesthesiologists directly interacting with patients is a time- and staff-consuming strategy. Given today's smart technologies, notably including virtual reality (VR), we explored in a prospective randomized study whether 'VR-assisted informed consent' could improve this situation. Methods: Fifty patients scheduled for orthopedic surgery were randomized. In the control group, informed consent was obtained via patient-specialist dialogs only. The patients in the study group, wearing a head-mount display, watched an 8-minute immersive 3D movie with standard explanations of general anesthesia, followed by a patient-specialist dialog to address open question. The time spent on the dialogs in both groups was evaluated as primary outcome variable. As secondary variables, we analyzed both a three-item Likert scale on patient satisfaction with the VR experience and cost differences between both groups. Results: Patient-specialist dialogs were carried on for median (IQR) durations of 93 (20–182) seconds in the study group versus 665 (261–829) seconds in the control group (P&lt;0.001). All patients exposed to VR rated this experience as favorable (87.5%) or neutral (12.5%). Based on anesthesiologists' incomes in the US and UK, our approach would reduce the staff expenditure for each patient-specialist dialog by median amounts of ≈40 or ≈11 US dollars, respectively (2× P&lt;0.001). Conclusions: 'VR-assisted informed consent' for anesthesia is well accepted by patients and reduces the time requirements for patient-specialist dialogs, thus pointing out a potential avenue towards increasing the work-time efficiency of anesthesiologists.

This paper presents machine learning methods for approximate solutions of reaction-diffusion equations with multivalued interaction functions. This approach addresses the challenge of finding all possible solutions for such equations, which often lack uniqueness. The proposed method utilizes physics-informed neural networks (PINNs) to approximate generalized solutions.

From 2008 to 2024, the Newborn Screening Translational Research Network (NBSTRN), part of the National Institute of Child Health and Human Development (NICHD) Hunter Kelly Newborn Screening Program, served as a robust infrastructure to facilitate groundbreaking research in newborn screening (NBS), rare disease, and genomics. Over fifteen years, NBSTRN developed into a significant international network, supporting innovative research on novel technologies to screen, diagnose, treat, manage, and understand the natural history of more than 280 rare diseases. A key component of these efforts has been the development of resources and expertise for the newborn screening community in ethical, legal, and social issues (ELSI). This paper presents findings from a survey conducted by the NBSTRN team with input from one of theNBSTRN's expert workgroups, the Bioethics and Legal Workgroup. With responses from 88 members of the NBS community including researchers and state NBS programs, the survey found that individuals rely on internal and collegial ELSI resources and highlighted a substantial demand for structured, accessible training and resources. The survey’s insights emphasize the importance of privacy and informed consent in NBS research, and suggested a need for improved collaborative tools and educational programs focused on ELSI for the NBS community. The survey results may inform future efforts in ELSI and NBS research including the development of expanded ELSI tools that leverage digital platforms and consultative services.

The deep energy method (DEM), a type of physics-informed neural network, is evolving as an alternative to finite element analysis. This method employs the principle of minimum potential energy to predict deformations under static loading conditions. However, the model’s accuracy is contingent upon choosing the appropriate architecture for the model, which can be challenging due to the high interactions between hyperparameters, large search space, difficulty in identifying objective functions, and non-convex relationships with the objective functions. To improve DEM’s accuracy, we first introduce random Fourier feature (RFF) mapping. RFF mapping helps with the training of the model by reducing bias towards high frequencies. The effects of six hyperparameters are then studied under compression, tension, and bending loads in planar linear elasticity. Based on this study, a systematic automated hyperparameter optimization approach is proposed. Due to the high interaction between hyperparameters and the non-convex nature of the optimization problem, Bayesian optimization algorithms are used. The models trained using optimized hyperparameters and having Fourier feature mapping can accurately predict deflections compared to finite element analysis. Additionally, the deflections obtained for tension and compression load cases are more sensitive to variations in hyperparameters than bending.

Public trust in science was tested and relied on during the SARS-CoV-2 pandemic, which has shaped global events since the WHO declaration in March 11, 2020. Public trust has been impacted through the government recommendations and mandates informed by public health guidance, including non-pharmaceutical and pharmaceutical interventions. The free-flow of ideas and in-formation so essential to the functioning of science has faced unprecedented challenge from widespread censorship in both the media and in scientific journals. This has created a poisoned environment for the building of trust between science and society. Scientific norms and ac-countability must be restored in order to rebuild the vital relationship between scientists and the public they serve.

Throughout the COVID-19 pandemic, masks have been promoted as an important tool for reducing SARS-CoV-2 transmission. However, even though masks are plausible experimental interventions, their mechanistic effectiveness has not been validated by randomized clinical trials (RCTs), with reduction risks of just 1.1-fold (11%) or less. Several meta-analyses have suggested that masks effectively reduce viral transmission, but all of them rely heavily or exclusively on observational data. Indeed, there is a strong inverse correlation between the effectiveness of masks and the number of RCTs included in the meta-analysis. RCTs on alternative therapies often show similar low levels of effectiveness. Thus, masks can be compared with potential new drugs or therapies that failed clinical trials despite showing mechanistic evidence.

Introduction: The academic landscape is increasingly recognizing the complex interplay of personal and professional responsibilities on scholarly productivity. For women scholars, particularly those with significant parenting duties, this balance is even more precarious. This study aims to explore how family responsibilities, personal vulnerabilities, and motivational theories such as Imposter Syndrome and Self-Determination Theory (SDT) influence the motivation to publish scholarly work. Understanding these dynamics is crucial for developing supportive mechanisms that can enhance academic productivity and personal fulfilment. Methods: Utilizing a Qualitative Evidence Synthesis (QES) framework, this study will systematically review and synthesize qualitative research on women scholars' experiences in academia, focusing on the challenges and motivators related to publishing. Data sources will include academic databases, grey literature, and first-hand narratives. Thematic analysis will be employed to identify key themes and patterns across the collected data. Results/Findings: Preliminary findings suggest that the dual demands of parenting—especially in extreme cases such as assuming responsibility for seven children—and academic work significantly impact women scholars' publishing activities. Personal vulnerabilities, including imposter syndrome and low self-efficacy, further exacerbate these challenges. However, the data also highlight resilience strategies and the potential for intrinsic and extrinsic motivational factors to mitigate these effects. Discussion: The study underscores the nuanced impact of familial and personal factors on academic motivation. The preliminary findings suggest that addressing imposter syndrome and enhancing self-determination could be pivotal in supporting women scholars. The role of institutional policies and academic culture in either exacerbating or alleviating these challenges is also discussed. Significance: By elucidating the specific challenges and motivators for women scholars in publishing, this study will contribute to the development of targeted interventions and policies that support work-life integration and promote academic productivity. The insights that will be gained could inform mentorship programs, institutional support systems, and policy reforms aimed at fostering a more inclusive and supportive academic environment.

Background: Citicoline, a medical food, lacks proven efficacy for ischemic stroke and has uncertainties around its harms. The continued prescription should be reevaluated given the more robust, high-quality Evidence against that medical food. This essay is based on a recent Cochrane review and uses only one all-cause mortality as the primary outcome. Therefore, the essay should be considered as something other than an updated Cochrane review but like a reflection based on Evidence-based Medicine and Philosophy of Science analysis. Question Research: Why citicoline (a medical food) should not be prescribed to treat people with acute ischemic stroke: The certainty of the Evidence.? Objective: Demonstrate from evidence-based medicine and philosophy of science perspective that citicoline should not be prescribed for acute ischemic stroke due to lack of efficacy and harm uncertainties. Search publications: We searched in PubMed and Cochrane Library from 2020 until 30 October 2023. We, furthermore, used engineering machines Bing and Google Scholar to detect additional papers. Additionally, we also reviewed reference lists of the retrieved publications and review articles and searched the websites of the U.S. Food and Drug Administration (FDA) and European Medicines Agency (EMA). Selection criteria: We included systematic reviews, meta-analyses, randomized clinical trials, clinical guidelines focused on acute ischemic stroke and comparing citicoline versus placebo or no intervention. We excluded narrative reviews, observational studies and ongoing trials. Data collection and analysis: We identified only new randomized clinical trials and assessed the risk of bias in seven domains. The other eight trials were already included in the mentioned Cochrane review. The systematic reviews with or without meta-analyses were assessed using McMaster University guidelines. We estimated risk ratios (RRs) for that outcome. We measured statistical heterogeneity using the I² statistic. We conducted our analyses using the fixed effect model. We did not use the GRADE approach due to what is shown in a Cochrane review published in 2020 by Martí-Carvajal et al. We used the RevMan 5.4 software from Cochrane Collaboration to conduct the forest plot. We used a Trial Sequential Analysis with Copenhagen Trial Unit Software. We estimated a Bayes factor from the relative risk and 95% confidence interval. Results: We identified only one new RCT (N = 99) reported mortality data and three clinical guidelines. We conducted a new meta-analysis with nine trials (N = 4461) having a high risk of bias and showing little to no difference in mortality between citicoline and placebo (17.1% vs 18.4%; RR 0.94, 95% CI 0.82 to 1.06; I2 = 0%). The Bayes factor was 0.7, indicating weak Evidence for the null over the alternative hypothesis. Trial sequential analysis suggested sufficiency of Evidence for mortality. No guidelines recommend citicoline. Conclusions: This essay reassessed citicoline for acute ischemic stroke after the 2020 Cochrane review. Adding a new RCT further supported the lack of mortality benefit with citicoline. The overall evidence quality could be better. Analyses using evidence-based medicine and philosophy of science approaches do not support prescribing citicoline due to a lack of efficacy substantiation and potential harms.

Genomic medicine is moving from research to the clinic. There is a lack of evidence about the impact of genomic medicine interventions on health outcomes. This is due in part to a lack of standardized outcome measures that can be used across different programs to evaluate the impact of interventions targeted to specific genetic conditions. The eMERGE Outcomes working group (OWG) developed measures to collect information on outcomes following the return of genomic results to participants for several genetic disorders. These outcomes were compared to outcome intervention pairs for genetic disorders developed independently by the ClinGen Actionability working group (AWG). In general, there was concordance between the defined outcomes between the two groups. The ClinGen outcomes tended to be higher level and the AWG scored outcomes represented a subset of outcomes referenced in the accompanying AWG evidence review. eMERGE OWG outcomes were more detailed and discrete, facilitating collection of relevant information from health records. This paper demonstrates that common outcomes for genomic medicine interventions can be identified. Further work is needed to standardize outcomes across genomic medicine implementation projects and make these publicly available to enhance dissemination and assist in making precision public health a reality.

In metal additive manufacturing (AM), precise temperature field prediction is crucial for process monitoring, automation, control, and optimization. Traditional methods, primarily offline and data-driven, struggle with adapting to real-time changes and new process scenarios, which limits their applicability for effective AM process control. To address these challenges, this paper introduces the first physics-informed (PI) online learning framework specifically designed for temperature prediction in metal AM. Utilizing a physics-informed neural network (PINN), this framework integrates a neural network architecture with physics-informed inputs and loss functions. Pretrained on a known process to establish a baseline, the PINN transitions to an online learning phase, dynamically updating its weights in response to new, unseen data. This adaptation allows the model to continuously refine its predictions in real-time. By integrating physics-informed components, the PINN leverages prior knowledge about the manufacturing processes, enabling rapid adjustments to process parameters, geometries, deposition patterns, and materials. Empirical results confirm the robust performance of this PI online learning framework in accurately predicting temperature fields for unseen processes across various conditions. It notably surpasses traditional data-driven models, especially in critical areas like the Heat Affected Zone (HAZ) and melt pool. The PINN’s use of physical laws and prior knowledge not only provides a significant advantage over conventional models but also ensures more accurate predictions under diverse conditions. Furthermore, our analysis of key hyper-parameters—learning rate and batch size of the online learning phase—highlights their roles in optimizing the learning process and enhancing the framework’s overall effectiveness. This approach demonstrates significant potential to improve the online control and optimization of metal AM processes.

The Physics-Informed Neural Networks (PINNs) improve the efficiency of data utilization by combining physical principles with neural network algorithms and ensure that the predictions are consistent and stable with the physical laws. PINNs opens up a new approach to address inverse problems in fluid mechanics. Based on the single-relaxation-time lattice Boltzmann method (SRT-LBM) with the Bhatnagar-Gross-Krook (BGK) collision operator, the PINN-SRT-LBM model is proposed in this paper for solving the inverse problem in fluid mechanics. The PINN-SRT-LBM model consists of three components. The first component involves a deep neural network that predicts the equilibrium control equations in different discrete velocity directions within SRT-LBM. The second component employs another deep neural network to predict non-equilibrium control equations, enabling inference of the fluid's non-equilibrium characteristics. The third component, a physics informed function translates the outputs of the first two networks into physical infor-mation. By minimizing the residuals of the physical partial differential equations (PDEs), the physics informed function infers relevant macroscopic quantities of the flow. The model evolves two sub-models applicable to different dimensions, named PINN-SRT-LBM-I and PINN-SRT-LBM-II models according to the construction of the physical informed function. The innovation of this work is the introduction of SRT-LBM and discrete velocity models as physical drivers into the neural network through the interpretation function. Therefore, PINN-SRT-LBM allows the neural network to handle inverse problems of various dimensions and focus on problem-specific solving. Experimental results confirm the accurate prediction of flow infor-mation at different Reynolds numbers within the computational domain. Relying on the PINN-SRT-LBM models, inverse problems in fluid mechanics can be solved efficiently.

Our objective in this work is to demonstrate how Physics-Informed Neural Networks, a type of deep learning technology, can be utilized to examine the mechanical properties of a helicopter blade. The blade is regarded as a prismatic cantilever beam that is exposed to triangular loading, and comprehending its mechanical behavior is of utmost importance in the aerospace field. PINNs utilize the physical information, including differential equations and boundary conditions, within the loss function of the neural network to approximate the solution. Our approach determines the overall loss by aggregating the losses from the differential equation, boundary conditions, and data. We employed a Physics-Informed Neural Network (PINN) and an Artificial Neural Network (ANN) with equivalent hyperparameters to solve a fourth-order differential equation. By comparing the performance of the PINN model against the analytical solution of the equation and the results obtained from the ANN model, we have conclusively shown that the PINN model exhibits superior accuracy, robustness, and computational efficiency when addressing high-order differential equations that govern physics-based problems. In conclusion, the study demonstrates that PINN offers a superior alternative for addressing solid mechanics problems with applications in the aerospace industry.

Background: Falls in older adults are prevalent and can result in an injury affecting their quality of life. Whether a fall is fatal or not, it has a high financial impact. Older adults with dementia are more likely to fall than their cognitively healthy counterparts, increasing their risk of serious injury or even death. This quality improvement project developed, implemented, and evaluated an evidence-based fall prevention bundle to reduce preventable falls in a long-term memory care unit.; Methods: The interventions that were implemented includes fall risk screening, physical exercise, vitamin D supplementation, purposeful hourly rounds, and post-fall huddles. Kurt Lewin’s force field analysis guided the project’s implementation. Nurses, healthcare providers, certified nursing assistants, restorative aides, and life enrichment staff from all shifts at a long-term memory care unit participated in the project. The participants implemented the evidence-based fall prevention bundle based on their scope of practice. They participated for 12 weeks. The frequency of falls was compared three months before, during, and after implementation. Paired t-test and independent samples t-test were used to analyze statistically significant differences between variables.; Results: Thirty staff members attended educational sessions, and 22 residents received interventions. Fall rates dropped 50% during the project's implementation but increased subsequently. The difference in the number of falls before, during, and after implementation was not statistically significant.; Conclusions: Staff working in a long-term care facility may help reduce fall rates using evidence-based interventions, but measures should be taken to sustain the initiative's implementation.

Background: Potential calcium-related adverse events of vitamin D supplement use have not been addressed in large-scale, real-world data so far. Methods: Leveraging data from the UK Biobank, encompassing 445,493 individuals aged 40-69, we examined associations of high 25-hydroxyvitamin (25[OH]D) levels ≥100 nmol/L and vitamin D supplementation with hypercalcemia (plasma calcium >2.6 mmol/L), kidney stones, and atherosclerosis assessments (pulse wave arterial stiffness index and carotid intima-medial thickness). Regression models were comprehensively adjusted for 49 covariates. Results: Approximately 1.5% of the participants had high 25(OH)D levels, 4.3% regularly used vitamin D supplements, and 20.4% reported regular multivitamin use. At baseline, the hypercalcemia prevalence was 1.6%, and 1.1% were diagnosed with kidney stones during follow-up. High 25(OH)D levels were neither associated with calcium-related adverse events nor atherosclerosis assessments. Vitamin D and multivitamin supplementation were associated with an increased prevalence of hypercalcemia (odds ratios and 95% confidence intervals: 1.46[1.32-1.62] and 1.11[1.04-1.18], respectively) but were neither associated with atherosclerosis nor future kidney stones. Conclusion: High 25(OH)D levels observable in routine care were not associated with any adverse outcome. Vitamin D users have a slightly higher prevalence of hypercalcemia, possibly due to co-supplementation with calcium, but without higher atherosclerosis prevalence or risk of kidney stones.

Carbapenem-resistant Acinetobacter baumannii (CRAB) infections represent the fourth-leading cause of death attributable to antimicrobial resistance globally, but a standardized therapy is still lacking. In this narrative review we focused the antibiotic treatment of CRAB infections in view of newly β-lactam agents (NBLs) like sulbactam/durlobactam and cefiderocol, discussing the main hot points such as the superiority of combination treatment over monotherapy and the best antibiotic partner to use in combination. Sulbactam/durlobactam seems to be the best candi-date to replace current back-bone agents. Cefiderocol, could play a crucial role in combina-tion-regimen. Due to toxicity and the PK/PD limitations, colistin (or polymyxin B) should be used as an alternative agent (when no other options are available). Tigecycline (or minocycline) and fosfomycin could represent suitable partners for both NBLs. Randomized clinical trials (RCTs) are needed to better evaluate the role of NBLs in CRAB infection treatment and to compare the ef-ficacy of tigecycline and fosfomycin as partner antibiotics. Synergism should be tested between NBLs and “old” drugs (rifampicin and trimethoprim/sulfamethoxazole). Huge efforts should be made to accelerate pre-clinical and clinical studies on safer polymyxin candidates with lung im-proved activity, as well as on the i.v. rifabutin formulation.

Although traditional automotive development has mainly focused on functional safety, as the number of automotive hacking cases has increased due to the growing Internet connectivity of automotive control systems, security is also becoming more important. Accordingly, various international organizations are preparing cybersecurity regulations or standards to ensure security in automotive development by emphasizing the concept of security-by-design(i.e. security engineering) which emphasizes trustworthiness from the beginning of development. The problem, however, is that no specific methodology has been suggested. In this paper, we propose a specific security-by-design methodology for automotive development based on Secure System Development Life Cycle (secure SDLC) standards and evidence-based standards. Our methodology could be easily used in the actual field as it is more general and detailed than existing secure SDLC standards and research. Also, since it satisfies all requirements of United Nations Economic Commission for Europe (UNECE) regulation, automobile manufacturers could respond to the upcoming cybersecurity regulation with our methodology.

BACKGROUND Epidemiological sciences have been evolving at an exponential rate paralleled only by the comparable growth within the discipline of data science. Digital epidemiological studies are playing a vital role in medical science analytics for the past few decades. To date, there are no published attempts at deploying the use of real-time analytics in connection with the disciplines of Dentistry or Medicine. AIMS AND OBJECTIVES We deployed a real-time statistical analysis in connection with topics in Dental Anatomy and Dental Pathology represented by the maxillary sinus, posterior maxillary teeth, related oral pathology. The purpose is to infer the digital epidemiology based on a continuous stream of raw data retrieved from Google Trends database. MATERIALS AND METHODS Statistical analysis was carried out via Microsoft Excel 2016 and SPSS version 24. Google Trends database was used to retrieve data for digital epidemiology. Real-time analytics and the statistical inference were based on encoding a programming script using Python high-level programming language. A systematic review of the literature was carried out via PubMed-NCBI, the Cochrane Library, and Elsevier databases. RESULTS The comprehensive review of databases of the literature, based on specific keywords search, yielded 491813 published studies. These were distributed as 488884 (PubMed-NCBI), 1611 (the Cochrane Library), and 1318 (Elsevier). However, there was no single study attempting real-time analytics. Nevertheless, we succeeded in achieving an automated real-time stream of data accompanied by a statistical inference based on data extrapolated from Google Trends. CONCLUSION Real-time analytics are of considerable impact when implemented in biological and life sciences as they will tremendously reduce the required resources for research. Predictive analytics, based on artificial neural networks and machine learning algorithms, can be the next step to be deployed in continuation of the real-time systems to prognosticate changes in the temporal trends and the digital epidemiology of phenomena of interest.

The objective of the study was to investigate the gap between data and evidence-based decisions among healthcare professionals considering the enormous amount of individual and aggregate data collected. Our study assessed the capacity, skills, and knowledge of the Ministry of Health leadership staff to understand data management, analysis, utilization, and dissemination. Three key components were assessed: 1) Knowledge through true/false questions, 2) Level of Skill (and Competency) using a Likert scale, and 3) Understanding of Key Concepts and Tools based on a Likert scale. The 183 study respondents were diverse healthcare professionals from Kenya, Tanzania, and Rwanda. Majority of respondents had not received any training on data management, analysis, interpretation, and utilization techniques, further there was a significant difference between those who had received training versus those who had not(p=0.005). The respondents were competent in work-related experiences but lacked skills and knowledge on: data concepts and tools, study designs, and types of data analysis. These findings explain the gap between data management, analysis, utilization, and dissemination among health professional’s cadre. To enhance service delivery and optimal provision of health care, it is imperative to have all health care professionals receive a well-designed training on data management, analysis, interpretation, and utilization.

Unsupervised physics-informed deep learning can be used to solve computational physics problems by training neural networks to satisfy the underlying equations and boundary conditions without labeled data. Parameters such as network architecture and training method determine the training success. However, the best choice is unknown a priori as it is case specific. Here, we investigated network shapes, sizes, and types for unsupervised physics-informed deep learning of the two-dimensional Reynolds-averaged flow around cylinders. We trained mixed-variable networks and compared them to traditional models. Several network architectures with different shape factors and sizes were evaluated. The models were trained to solve the Reynolds-averaged Navier-Stokes equations incorporating Prandtl's mixing length turbulence model. No training data were deployed to train the models. The superiority of the mixed-variable approach was confirmed for the investigated high Reynolds number flow. The mixed-variable models were sensitive to the network shape. For the two cylinders, differently deep networks showed superior performance. The best fitting models were able to capture important flow phenomena such as stagnation regions, boundary layers, flow separation, and recirculation. We also encountered difficulties when predicting high Reynolds number flows without training data

Serum neurofilament light chain (sNfL) levels have been proposed as a biomarker of the clinical activity, disability progression, and response to treatment of people with multiple sclerosis (PwMS); however, questions remain about its implementation in clinical practice. Ocrelizumab (OCR) has proven effective to improve clinical and radiological outcomes and to reduce sNfL levels. This real-life study followed the sNfL levels of 30 PwMS treated for 12 months with OCR and evaluated the usefulness of this biomarker for the short-term prognosis, considering expanded disability status scale (EDSS), annualized relapse rate (ARR), radiological activity, and NEDA-3 values. OCR reduced the ARR in 83% of PwMS and the radiological activity in 80%. Their EDSS was maintained, while NEDA-3 was achieved in 70% at 12 months. OCR produced an early reduction in sNfL levels (at 3 months). At baseline, greater MRI-evaluated radiological activity was associated with higher sNfL levels. sNFL levels over the first 12 months of treatment did not predict a suboptimal response or sustained control of the disease. Longer-term studies are needed to explore the predictive usefulness of sNfL levels in PwMS treated with high-efficacy drugs

N-of-1 data are unavoidable in zoological medicine. Accordingly, zoological medicine clinicians and investigators need research techniques that can make use of these data. This article reviews two methodologies for using both observational and experimental N-of-1 data: 1) systematic reviews and meta-analyses of case reports and 2) prospective N-of-1 trials. Systematic reviews of case reports and other observational evidence are formal, unbiased summaries of clinical characteristics of a particular disease-taxon combination. They offer advantages to narrative reviews by minimizing omission of relevant articles, thereby reducing the potential for mischaracterization of the literature. Meta-analyses are extensions of systematic reviews that quantitatively synthesize the data from the included articles. While valuable, systematic reviews and meta-analyses of case reports can have limited interpretations due to publication bias and confounding present in their source materials. In contrast to case reports, N-of-1 trials are prospective study designs that allow clinicians to make strong inferences about the effect of an intervention in a particular patient. They are double-blinded, single patient, multi-crossover studies that are of particular value in fields where it is difficult to recruit sufficient patients for conventional randomized control trials (RCTs), such as zoological medicine. Because they require multiple crossover periods, N-of-1 trials are ideal for evaluating short-acting interventions in patients with somewhat stable chronic diseases, such as osteoarthritis. More complex than conventional therapeutic trials, N-of-1 trials require prior consideration of how to achieve blinding, appropriate placebo controls, quantitative primary outcomes, analysis methods, and ethical approval. Aggregation of N-of-1 trials allows estimation of the average treatment effect across the population with fewer participants than a conventional RCT. While systematic reviews and meta-analyses of case reports can be used to synthesize the observational N-of-1 data already in existence, N-of-1 trials offer an exciting way to prospectively generate strong evidence that will be useful for evidence-based decision-making.

General Linear Modelling (GLM) has been widely employed to estimate the hemodynamic changes evoked by cognitive processing, which are more likely to be nonlinear than linear. First, this study re-analyzed the fNIRS data (N = 38, Mage = 5.0 years, SD = 0.69 years, 17 girls) collected in the Mixed-Order Design Dimensional Change Card Sort (DCCS) task. The results indicated that the quadratic equation was better than GLM to model HbO changes in this task. Second, analysis of a new set of data indicated that the Habit-DisHabit design of DCCS was more effective in identifying the neural correlates of cognitive shifting than the Mixed-Order Design. Third, this study found that the Non-users were more attentive and engaged than the Heavy-users, with a slower but more steady increase of brain activation in BA8 and BA9.

This study aims to examine the impact of tablet use on preschoolers’ executive function during the Dimensional Change Card Sort Task (DCCS) task using the functional near-infrared spectroscopy (fNIRS). Altogether 38 Chinese preschoolers (Mage = 5.0 years, SD = 0.69 years, 17 girls) completed the tasks before the COVID-19 lockdown. Eight children never used tablets, while 16 children were diagnosed as the ‘heavy-user'. The results indicated that: (1) the 'Non-user' outperformed the 'Heavy-user' with a significantly higher correct rate in the DCCS task; (2) the two groups differed significantly in the activation of the prefrontal cortex (BA 9): the 'Non-user' pattern is normal and healthy, whereas the 'Heavy-user' pattern is not normal and needs further exploration.

Bovine tuberculosis (bTB) outbreaks, caused by Mycobacterium bovis infection, are a costly animal health challenge. Understanding factors associated with the duration of outbreaks, known as breakdowns, could lead to better disease management policy development. We undertook a retrospective observational study (2012-2018) and employed Finite Mixture Models (FMM) to model the outcome parameter, and to investigate how factors were associated with duration for differing subpopulations identified. In addition to traditional risk factors (e.g. herd size, bTB history), we also explored farm geographic area, parcels/farm fragmentation, metrics of intensity via nitrogen loading, and whether herds were designated controlled beef finishing units (CBFU) as potential risk factors for increased duration. The final model fitted log-normal distributions, with two latent classes (k) which partitioned the population into a subpopulation around the central tendency of the distribution, and a second around the tails of the distribution. The latter subpopulation included longer breakdowns of policy interest. Increasing duration was positively associated with recent (<3 yrs) TB history and the number of reactors disclosed, (log) herd size, beef herd-type relative to other herd types, number of land parcels, area, and being designated a controlled finishing unit (“feedlot”), and having high annual inward cattle movements within the “tails” subpopulation. Breakdown length was negatively associated with year of commencement of breakdown (i.e. a decreasing trend) and non-significantly with the organic nitrogen produced on the farm (N kg/hectare), a measure of stocking density. The latter finding may be due to confounding effects with herd size and area. Most variables contributed only moderately to explaining variation in breakdown duration, that is, they had moderate size effects on duration. Herd-size and CBFU had greater effect sizes on the outcome. The findings contribute to evidence-based policy formation in Ireland.

More than 179,000 individuals have fallen ill of the Coronavirus disease (COVID-19) caused by the SARS-CoV-2 virus, which first emerged in China less than four months ago in December 2019. As of today, there exist no approved treatments against COVID-19. Vaccines are being developed, but they will take time, at least one year, to reach the population. Drug repositioning represents a fast track because already approved medicines have been broadly tested through multiple trials. We developed a repositioning strategy that mostly leads to candidates that are commonly used. The advantages are that they will facilitate proof of concept in humans through a “real-world evidence” approach and should be rapidly available to the population. We focus on the established research results that the unfolded protein response (UPR) and autophagy pathways of the host cells are essential to the life cycle of previously known coronaviruses. We performed the relevant bioinformatics analysis to understand and confirm if SARS-CoV-2 may interact with these druggable pathways. Based on these considerations, we prioritized two additional druggable pathways, which are important to the viral life cycle and tightly connected to UPR/autophagy signaling: the mitochondrial permeability transition pores (MPTP) and NLRP-3 inflammasome pathways. These four important pathways are perturbed in most major common diseases and have therefore been targeted by numerous broadly prescribed drugs. We have identified 97 approved drugs that are known to modulate these four identified pathways, and which represent, therefore, interesting repositioning candidates. Although it is indisputable that these drugs should never be used for immediate self-medication against COVID-19, we notice that some of them could also be prescribed to individuals who have COVID-19 comorbidities (e.g., hypertension). It is debated if these comorbidities are linked to the pathology itself (e.g., hypertension) or the drugs used to treat the pathology (e.g., sartans). Therefore, relevant preclinical tests and massive electronic health records (i.e., real-world evidence) must be used to pre-screen them and check the COVID-19 prognosis of individuals taking these drugs.

Background: Neurofeedback (NFB) or EEG-Biofeedback is a non-invasive therapeutic method, based on the theory of operating conditioning, and designed to change the functioning of the brain by sending feedback that provides positive or negative reinforcement. The goal is to teach the patient how to perceive specific cortical activation states and how to achieve them on purpose. NFB has been used to treat various mental disorders, including Post-traumatic Stress Disorder (PTSD). Traditional treatments for PTSD have several limitations; very few individuals are willing to relive the traumatic experience. Placing itself as an effective alternative, Neurofeedback allows the unresponsive patient to recourse to an alternative intervention to relieve their symptoms without reliving the anguish of trauma, consequently presuming a lower percentage of drop-out. Considering the importance of integrated approaches to clinical practice, NFB was integrated with Trauma-Informed Motivational Interviewing (TIMI). The TIMI approach is a new therapeutic paradigm for clinical interviews that aims to help users work through their trauma and transform it from a point of weakness to a point of strength. Integrating TIMI with NFB can help overcome the current trend of passive implementation of intervention tools, leading to greater user responsiveness and fewer residual symptoms. To improve treatment adherence, moreover, it has been suggested to incorporate Virtual Reality (VR) into the NFB system. VR, through the "sense of presence," provides a more immersive experience than 2D visualization, potentially resulting in faster cortical training. Lastly, the research protocol aims to focus on a population at high risk of PTSD that is often overlooked in research, such as workers who are victims of occupational PTSD.Methods: The study design involves in-depth analysis of a specific clinical case. Baseline measurements were taken for enrollment and at the following time points: T1 (pre-treatment), T2 (post-treatment), and T3 (follow-up). These measurements included various psychometric assessments to evaluate PTSD symptoms, treatment acceptability, safety, and adherence, as well as biological measures related to the brain's electrophysiological activity.Results and Conclusion: Our research reveals that the synergy between virtual reality-mediated Neurofeedback and trauma-motivational interviewing produces significant results. TIMINB stands out as a low-cost, effective, and completely risk-free procedure. This approach could represent a groundbreaking innovation in the field of Cyberpsychology and psychological therapies.

Motivation is essential in education, with highly motivated learners engaging more deeply with content and more ably transferring knowledge to new contexts. However, the validity of scales to measure motivation has been underexplored in pre-clerkship medical education. This study evaluates the validity of the Situational Motivation Scale (SIMS) for measuring motivation among pre-clerkship medical students in post-situational and short-term contexts. Using a sample of n = 156 students from the Frank H. Netter MD School of Medicine at Quinnipiac University, SIMS was tested for construct validity with attention paid to content, response process, internal structure, relationships to other variables, and consequences of use evidence. Small modifications from present to past tense in English were made for clarity following focus group feedback, and content validity was ensured via expert consultation. SIMS demonstrated strong internal consistency with a satisfactory Cronbach’s alpha for all subscales and anticipated patterns of correlations. Factor analysis confirmed appropriate factor loadings, with a stronger model fit for the short-term context, and no observed adverse effects on student engagement. These findings support the robustness of SIMS in capturing intrinsic, extrinsic, and amotivation in pre-clerkship medical students, highlighting its applicability for short-term and situational motivational assessment.

Artificial intelligence (AI), deep learning (DL) or machine learning (ML) is the ability of computer systems, machines and enginery to work devise procedures like humans. These technologies provide opportunities and possibilities to advance diagnostics and in planning also in the field of human medicine and dentistry. The purpose of this literature review was to ascertain the applicability and significance of AI, as well as to highlight its uses in maxillofacial surgery. The primary inclusion criterion for this publication was an original paper written in English focusing on the use of AI, DL or ML in maxillofacial surgery. The sources of information were PubMed, Scopus and Web of Science, and the queries were made on 31st of December 2023. The search strings used were “artificial intelligence maxillofacial surgery”, “machine learning maxillofacial surgery” and “deep learning maxillofacial surgery”. Following the removal of duplicates, all remaining publications that were returned by the searches and were screened by three independent operators to minimize the risk of bias. The analysis of publications from 1992 to 2023 identified certain records, of which 324 were finally selected. These were calculated according to the year of publication with a continuous increase (excluding 2012 and 2013) of R2 = 0.9295. Generally, in orthognathic dentistry and maxillofacial surgery, artificial intelligence and machine learning have gained popularity over the past few decades. When we included the keywords "planning in maxillofacial surgery" and "planning in orthognathic surgery", the set of published papers significantly increased to the number of 7535 publications. The first publication appeared in 1965, with an increasing trend (excluding 2014-2018), with an R2 value of 0.8642. These tools have been found useful for diagnosis, treatment planning in head and neck surgical oncology, cosmetic and aesthetic surgery, and in oral pathology. In orthognathic surgery, they have been utilised for diagnosis, treatment planning, assessment of treatment needs, cephalometric analyses, and orthognathic surgeries, among other applications. The review confirms that the current use of artificial intelligence and machine learning in maxillofacial surgery is focused mainly on the evaluation of digital diagnostic methods especially radiology, treatment plans and postoperative results. However, as these technologies are integrated in maxillofacial surgery and robotic surgery in head and neck region, it is expected that in the future they will be gradually utilized to plan and comprehensively evaluate the success of maxillofacial surgeries.

We assessed the use of Evidence-Based Practice (EBP) among pharmacists working in community pharmacies in France, and the factors linked to this practice. During 3 months in 2018 an online survey was sent to over 7,000 active pharmacists, and posted on pharmacist social media sites. The 40-item questionnaire described four clinical cases reflecting typical situations (conventional med-icine and Complementary and Alternative Medicine) encountered daily by community pharma-cists. Multiple-choice responses were proposed and scored according to whether they reflected EBP. A high total score indicated behavior in line with EBP. Univariate and multivariate analyses were used to evaluate factors that might explain adherence to EPB (pharmacy’s characteristics, the pharmacist’s status, mode of continuing education and sources of information). 595 pharmacists completed the questionnaire. The majority relied on pharmaceutical industry and other biased and/or non-evidence-based sources, particularly concerning information on homeopathic products. The consultation of independent reviews, health agency recommendations, and peer-reviewed scientific journals, was associated with evidence-based decisions. In contrast, reliance on phar-maceutical industry documents, personal experience and informal handbooks was linked to lower EBP scores. The level of EBP use by French community pharmacists needs to be improved to ensure that good quality scientifically-based advice is given to customers.

The enteric nervous system (ENS) is a neural network referred to as "the brain in the gut" because of its similarities to the central nervous system (CNS). The ENS consists of numerous types of neurons and glial cells distributed in two intramuscular plexuses that span the length of the intestine and control coordinated smooth muscle contractile activity and other intestinal functions. It is well-established that reciprocal communication exists between the brain and the gastrointestinal tract. Although the ENS can function independently, it is connected to the CNS through the afferent and efferent pathways of the parasympathetic and sympathetic nervous systems. In addition to regulating ENS function by the CNS, these connections are likely to be critically involved in the pathophysiology of Parkinson's disease (PD). PD is a common neurodegenerative disorder that presents with non-motor and motor symptoms. Surprisingly, ENS lesions have been shown to occur very early in the disease, even before CNS involvement. This has led to the postulation that the ENS may be central to the pathophysiology of PD. Autopsy studies have shown that α-synuclein (αS) aggregates in PD patients are found both in the substantia nigra (SN) and the ENS. Therefore, it has been hypothesized that the pathological process leading to PD may initially occur in the ENS years before the appearance of motor features. This process induces misfolding and aggregation of αS in specific subtypes of neurons in the CNS. Finally, it spreads retrogradely in the CNS through preganglionic vagal fibers to the dorsal motor nucleus of this nerve and to other central nervous structures. In addition to the presumed role in the spread of the disease process, it has also been suggested that pathological changes in the ENS might be involved in the gastrointestinal dysfunction frequently seen in Pd patients. Starting from the evidence in animal models and using a translational point of view, in this review, we aim to summarize the role of the ENS in the pathogenesis of PD and how this system could be modulated for a novel therapeutic approach. While acknowledging the presumed role of the microbiome in the gut-brain axis, we will shift the focus from this point of view to focus more on the neurons of the ENS.

Classical methods for inverse problems are mainly based on regularization theory. In particular those which are based on optimization of a criterion with two parts: a data-model matching and a regularization term. Different choices for these two terms and great number of optimization algorithms have been proposed. When these two terms are distance or divergence measures, they can have a Bayesian Maximum A Posteriori (MAP) interpretation where these two terms correspond, respectively, to the likelihood and prior probability models.

The nine plateau lake watersheds in Yunnan are important ecological security barriers for southwest China, and the prevention and control of landslides are important considerations in the management of these watersheds. Taking Dianchi Lake watershed as a typical study area, this paper puts forward an improved comprehensive process of landslide susceptibility evalua-tion, and discusses the model’s sensitivity regarding susceptibility to landslides. The compre-hensive process is based on the weight of evidence method (WoE) and integrates many analyti-cal techniques, such as cross-validation, multi-quantile cumulative Student’s comprehensive weight analysis, independence tests, step-by-step modeling, ROC analysis and ROC-based sus-ceptibility zoning. This paper established fourteen models, with high accuracy and validity AUC, reaching 0.83-0.87 and 0.85-0.88, respectively. Moreover, according to the susceptibility zoning map compiled using the optimal model, 80% of landslides can be predicted in the very high and high susceptibility zones, which only account for 19.58% of the study area. Finally, the strategies for geological disaster prevention and ecological restoration deployment were put forward, providing great guiding significance for deeply understanding the landslide susceptibility of lake watersheds (Dianchi Lake) in Yunnan Plateau and guiding the planning and deployment of landslide prevention and mitigation activities and ecological restoration in the watershed.

Young people living with perinatal infections of Human Immunodeficiency Virus (YLPHIV) face a chronic disease, with treatment including adherence to life-long antiretroviral treatment (ART). The aim of this QES was to explore adherence to ART for YLPHIV as an assemblage within the framework of the BPS model with a new materialist perspective. We searched up to November 2021 and followed the ENTREQ and Cochrane guidelines for QES. All screening, data extraction and critical appraisal was done in duplicate. We analysed and interpreted the findings innovatively, by creating images of meaning, a storyboard, and storylines. We then reported the findings in a narrative first person story. We included 47 studies and identified 9 storylines. We found that treatment adherence has less to do with humans’ preferences, motivations, needs and dispositions, and more to do with how bodies, viruses, things, ideas, institutions, environments, social processes, and social structures assemble. This QES highlights that adherence to ART for YLPHIV is a multisensorial experience in a multi agentic world. Future research into rethinking the linear and casual inferences we are accustomed too in evidence-based health care is needed if we are to adopt multidisciplinary approaches to address pressing issues such as adherence to ART.

Epistemological naturalism dismisses the notion that epistemology is a basis for the empirical sciences. In particular, it rejects the demand for a general justification of induction. Making inductive generalisations is a basic habit among humans. There is no such thing as a logic of inductive inference. The role of induction in science is heuristic; it is our way of inventing new theoretical predicates and developing theories. We discover new laws by applying inductive thinking; but this is not any kind of inference which can be evaluated as more or less rational.

Physics-informed neural networks can be trained to serve as surrogate models by learning how spatial physical fields develop with change of parameters. This is an advantage over traditional numerical methods that can only be used to calculate a single solution field for a single discrete setting of a parameter. While many studies report accurate results of physics-informed deep learning of laminar flows, evaluations of surrogate modeling of high Reynolds number flows remain sparse. To contribute to this question, we here explore the capabilities and limits of physics-informed deep learning to solve the Reynolds-averaged Navier-Stokes equations for the flow around two airfoils under variable angles of attack. No labeled training data were provided and a single network was trained to simultaneously learn several solution fields in a single training run. The network captured essential flow features such as boundary layers and high and low pressure regions. Moreover, the qualitative correlations of lift and drag with the angle of attack and the higher drag of the thicker airfoil were captured. However, comparisons with reference simulations revealed an underestimation of the shear layer gradients as well as deviations of the fields' extrema. Thus, we also debate potential future improvements to increase the accuracy of the predicted flow. This work offers a prospect on the capabilities and current limitations of physics-informed neural networks for surrogate modeling of high Reynolds number flows. We argue that the method is promising and this work is aimed to encourage further research and development of the method when applied to Reynolds-averaged flows at high Reynolds numbers.

Gas bubbles present in liquids underpin many natural phenomena and human-developed technologies that improve the quality of life. Since all living organisms are predominantly made of water, they may also contain gas bubbles—introduced both naturally and artificially—that can serve as biomechanical sensors operating in hard-to-reach places inside a living body and emitting signals that can be detected by common equipment used in ultrasound and photoacoustic imaging procedures. This kind of biosensors is the focus of the present article, where we critically review the emergent sensing technologies based on acoustically driven oscillations of gas bubbles in liquids and bodily fluids. This review is intended for a broad biosensing community and transdisciplinary researchers translating novel ideas from theory to experiment and then to practice. To this end, all discussions in this review are written in a language that is accessible to non-experts in specific fields of acoustics, fluid dynamics and acousto-optics.

The target of an antibody plays a significant role in the success of antibody-based therapeutics and diagnostics, and to an extent, that of vaccine development. This importance is focussed on the target binding site – epitope, where epitope selection as a part of design thinking beyond traditional antigen selection using whole cell or whole protein immunisation can positively impact success. With purified recombinant protein production and peptide synthesis to display limited/selected epitopes, intrinsic factors that can affect the functioning of resulting antibodies can be more easily selected for. Many of these factors stem from the location of the epitope that can affect accessibility of the antibody to the epitope at a cellular or molecular level, direct inhibition of target antigen activity, conservation of function despite escape mutations, and even non-competitive inhibition sites. Through the incorporation of novel computational methods for predicting antigen changes to model-informed drug discovery and development, superior vaccines and antibody-based therapeutics or diagnostics can now be more easily designed to mitigate failures. With detailed examples, this review highlights the new opportunities, factors and methods of predicting antigenic changes for consideration in sagacious epitope selection.

Increasingly large numbers of people are using digital weight-loss services (DWLSs) to treat overweight or obesity. Although it is widely agreed that digital modalities improve access to care in general, obesity stakeholders remain concerned that many DWLSs are not comprehensive or sustainable enough to deliver meaningful health outcomes. This study adopted a mixed-methods approach to assess why and after how long patients tend to discontinue Austral-ia’s largest DWLS, a program that combines behavioural and pharmacological therapy under the guidance of a multidisciplinary care team. We found that in a cohort of patients who commenced the Eucalyptus DWLS between January and June 2022 (n=5604), mean program adherence was 171.2 (±158.2). Inadequate supply of a patient’s desired Glucose-like peptide-1 receptor agonist medication was the most common reason for discontinuation (43.7%), followed by program cost (26.2%), result dissatisfaction (9.9%), and service dissatisfaction (7.2%). Statistical tests revealed that ethnicity and age both had a significant effect on patient adherence. These findings suggest that DWLSs have the potential to improve access to comprehensive, continuous obesity care, but that care models need to improve upon the one observed in the Eucalyptus Australia DWLS to maximise program adherence.

This article presents the development and validation of a new instrument measuring social, cultural, and behavioral factors influencing exposure to occupational zoonoses in agricultural settings. The Social Ecological Model (SEM) and the Standards for Educational and Psychological Testing were used as guiding frameworks to ensure the instrument's validity. The instrument's content was compiled by combining the results of a scoping literature review and an expert qualitative study. The instrument items were drafted, organized, and underwent a meticulous process of revision and adjustment. It was translated into Spanish and tested in one-to-one cognitive interviews with five volunteer agricultural workers. Exploratory factor analysis was conducted for construct discrimination, and bivariate regression analyses were conducted to explore the association with exposure indicators. Evidence of validity was obtained from four out of five sources of validity evidence according to the AERA/APA’s Standards of Educational and Psychological Testing. The Social Ecology of Occupational Zoonoses (SEOZ) was successfully used to determine social and behavioral factors associated with a higher risk of exposure to occupational zoonoses. Further use of the SEOZ can provide valuable insights into developing effective interventions to improve the health and well-being of agricultural workers.

Acute low back pain (LBP) stands as a leading cause of activity limitation and work absenteeism, and its associated healthcare expenditures are expected to become substantial when acute LBP develops into a chronic and even refractory condition. Therefore, early intervention is crucial to prevent progression to chronic pain whose management is particularly challenging and for which the most effective pharmacological therapy is still controversial. Current guideline treatment recommendations vary and are mostly driven by expertise with opinion differing across different interventions. Thus, it is difficult to formulate evidence-based guidance when relatively few randomized clinical trials did explore the diagnosis and management of LBP while employing different selection criteria, statistical analyses, and outcome measurements. This narrative review aims to provide a critical appraisal of current acute LBP management by discussing the unmet needs and areas of improvement from bench-to-bedside and proposes multimodal analgesia as the way forward to attain an effective and prolonged pain relief and functional recovery in patients with acute LBP.

Real-world evidence (RWE) is increasingly involved in the early benefit assessment of medicinal drugs. It is expected that RWE will help to speed up approval processes comparable to RWE developments in vaccine research during the COVID-19 pandemic. Definitions of RWE are diverse marking the highly fluid status in this field. So far, RWE comprises information produced from data routinely collected on patient’s health status and/or delivery of health care from various sources other than traditional clinical trials. These sources can include electronic health records, claims, patient-generated data including in home-use settings, data from mobile devices as well as patient, product and disease registries. The aim of the present update was to review the current RWE developments and guidelines mainly in the U.S., the UK, Europe and Germany field during the last decade. RWE has already been included in various approval procedures of regulatory authorities reflecting its actual acceptance and growing importance in evaluating and accelerating new therapies. However, since the RWE research is still in a transition process and since a number of gaps in this field have been explored, more guidance and a consented definition are necessary to increase the implementation of real-world data.

The popularity of electronic cigarettes in the United States and around the world has led to a startling rise in youth nicotine use. The Juul e-cigarette was introduced in the U.S. market in 2015 and had captured approximately 13% of the U.S. market by 2017. Unlike many other contemporary electronic cigarette companies, the founders behind the Juul® e-cigarette approached their product launch like a traditional high-tech start-up company, not like a tobacco company. This article presents a case study of Juul’s corporate and product development history in the context of US regulatory actions. The objective of this article is to demonstrate the value of government-curated archives as leading indicators which can (a) provide insight into emergent technologies and (b) inform emergent regulatory science research questions. A variety of sources were used to gather data about the Juul e-cigarette and the corporations that surround it. Sources included government agencies, published academic literature, non-profit organizations, corporate and retail websites, and the popular press. Data was disambiguated, authenticated, and categorized prior to being placed on a timeline of events. A timeline of four significant milestones, nineteen corporate filings and events, twelve US regulatory actions, sixty four patent applications, eighty seven trademark applications, twenty three design patents and thirty two utility patents related to Juul Labs and its associates is presented spanning the years 2004 thought 2020. This work demonstrates the probative value of findings from patent, trademark, and SEC filing literature in establishing a premise for emergent regulatory science research questions which may not yet be supported by traditional archival research literature. The methods presented here can be used to identify key aspects of emerging technologies before products actually enter the market, this shifting policy formulation and problem identification from a paradigm of being reactive in favor of becoming proactive. Such a proactive approach may permit anticipatory regulatory science research and ultimately shorten the elapsed time between market technology innovation and regulatory response.