Global population projected to reach 10 billion by 2050 emphasizing the need for sustainable food production. Traditional protein sources present environmental and scalability challenges, demanding the diversification of protein sources. Edible insects, such as house crickets (Acheta domesticus), emerged as a promising solution due to their high nutritional value, efficient feed conversion rates, and lower environmental impact compared to conventional protein sources. Incorporating insect powders into new food products can improve consumer acceptance, but often lead to poor functionality and/or undesirable organoleptic characteristics. However, protein isolates were revealed to be effective in enhancing functionality and consumer acceptance. This study aimed to develop and compare the yield of three different protein extraction methods, using sodium hydroxide, ascorbic acid or alcalase, from house crickets (Acheta domesticus) for food applications. Protein extraction was performed on cricket powder with a mean protein content of 57.95 g/100 g, and the results were evaluated. The enzymatic method showed the highest protein extraction rate of 85.97% with a mean protein content of 74.03 g/100 g, while extraction with NaOH or ascorbic acid resulted in rates of 74.32 and 56.99%, respectively. Further studies on the functionality and sensorial evaluation of products developed with this protein extract are recommended.

(1) Background: Glenohumeral internal and external rotational range of movement (GIRD and GERD) has been considered one of the primary outcomes to determine risk of injury in overhead athletes, as tennis players. Nevertheless, current testing position doesn’t consider than most tennis actions are repeated at 45of abduction, and actively. The aim of this study is establishing normative values of pathological GIRD and GERD in tennis players, to estimate the passive and active range of motion of the glenohumeral joint at 90° and 45º of abduction.; (2) Methods: 43 tennis players voluntarily participated in the study (19.1±2.75 years). Dominant and non-dominant shoulder range of motion (ROM), external rotation (ER), internal rotation (IR) at 90 and 45 in active and passive conditions were evaluated. GIRD and GERD were calculated in both positions and conditions; (3) Results: There were significant differences for all passive measurements between 45 and 90 testing position. ER and TROM at 90 and 45 showed significant differences in both passive and active conditions and in the dominant and non-dominant side. There were no significant differences in IR and TROM actively in both the dominant and the non-dominant at 90 or 45; (4) Conclusions: It is necessary to evaluate the ER under the same conditions at 90 or 45. Practitioners should consider assessing ER at the angle at which most actions are repeated in tennis (45°) as a method to monitor GERD. Evaluating GERD in asymptomatic tennis players could help avoid future biomechanical and GIRD problems. Both GIRD and GERD should be considered as a percentage of the athlete's own deficit in IR or ER, instead of having as a reference specific degree that have been observed in baseball pitchers.

This study investigates the use of two nanoclays, Laponite (Lap) and Montmorillonite (Mt), as eugenol slow release carriers for enhancing the functionality of low-density polyethylene (LDPE) active packaging films. Utilizing a vacuum-assisted adsorption method, eugenol was successfully intercalated into Lap and Mt nanoclays, forming EG@Lap and EG@Mt nanohybrids. The nanohybrids were subsequently incorporated into LDPE films at varying concentrations (5, 10, and 15 wt.%). Extensive characterization by a number of analytical techniques confirmed effective integration and dispersion of the nanohybrids within the LDPE matrix. EG@Lap and EG@Mt nanohybrids enhanced the films’ antioxidant and barrier properties, with EG@Lap demonstrating higher eugenol release rates, antioxidant activity, and barrier effectiveness. Packaging tests on minced pork revealed that the LDPE/15EG@Lap films extended shelf life by approximately two days more compared to the LDPE/15EG@Mt films, due to the higher eugenol release. These findings suggest that EG@Lap-based films offer improved active packaging capabilities, with potential applications in a number of areas where extending the freshness of perishable foods is needed.

In the bar of our Milky Way, there should be another supermassive black hole B which is with the mass almost 10−2~10−5 the Sgr A* and with a distance almost 10,000 lightyears from the Sgr A*. The bar is formed by the stars that are orbiting around the two black holes Sgr A* and B. And, in the bar, a lots of stars orbit around B while another lots of stars around Sgr A*. The two orbits form the peanut shape with “x-shaped structure” in the bar, while the boxy core is formed with all of the three orbits. And, the orbits of the stars out of the bar could be repulsed to a distant place by the black hole B to form the galactic spiral arms.The ultrafast bar should indicate that the conjectured black hole has been observed.

This study explores the impact and significance of implementing a comprehensive education system for children with special needs. The research aims to understand how inclusive practices influence the academic, social, and emotional development of these students. Using a mixed-methods approach that includes case study analysis, reviews of global education models, and theoretical frameworks, the study identifies key strategies for fostering inclusivity. The findings reveal that inclusive education not only enhances academic performance but also promotes social integration and emotional well-being. The analysis compares inclusive practices with traditional special education models and highlights the advantages of equity-driven, accessible learning environments. The study also addresses the challenges faced, such as inadequate resources and the need for continuous teacher training. Recommendations include policy reforms, increased funding, and the adoption of assistive technologies to support diverse learning needs. The research underscores the importance of a collaborative approach involving educators, parents, policymakers, and the community in creating an education system where every child has the opportunity to thrive.

Predicting the thermophysical properties of designed materials at the nanoscale is crucial. However, this remains a significant challenge despite numerous efforts by researchers and scientists. This paper presents a unified thermodynamic model to study the size and shape-dependent cohesive energy, as well as the melting temperature of semiconductor nanosolids, including InSb, ZnSe, CdS, and CdSe. The model provides insight into the thermal stability of nanosolid materials by considering the combined impact of size, shape, and packing density. The finding indicates that smaller nanoparticles exhibit a lower cohesive energy compared to larger ones. Our unified thermodynamic model yields results consistent with experimental values and demonstrates a closer agreement than those from the universal liquid drop model of size-dependent cohesive energy, which includes a shape factor of α = 2/3 for nanowires. According to the results, the melting temperature of nanoparticles drops as their size decreases. Below a diameter of 5 nm, this trend becomes more noticeable. In the nano-scale domain, the nanoparticle's shape also matters greatly. In particular, compared to tetrahedral nanoparticles, nanowires show a lesser reduction in melting temperature. Also, up to a diameter of roughly 10 nm, the melting temperatures of these nanoparticles rise with particle size. The search results show that, especially in the < 5 nm size range, the size and shape of nanoparticles significantly affect their melting point. The models accurately predict the relationship between particle size and melting point for various semiconductor nanomaterials, aligning with experimental findings. Interestingly, the information points to a potential preference for these nanomaterials to form nanowire structures in the < 5 nm size range. Our research offers helpful insights into the thermophysical properties of these semiconducting materials, which may have significant implications for their use in a diversity of technological applications.

The nature of multi-energy systems requires the integration of interdisciplinary methodologies and complementary tools in a coherent and systematic manner. Therefore, it is imperative to develop an efficient experimental environment to effectively implement the interaction between different tools for structural and parametric optimization (synthesis) of multi-energy systems and analysis of their operation. Due to the inherent complexity of the energy system optimization model, solving the synthesis problem often leads to significant challenges in terms of required computational resources and runtime. Known synthesis approaches do not fully address these challenges. In this context, we propose a subject-oriented environment for the multi-energy system synthesis in a reasonable time considering the available computational resources. The basis of the environment is a service-oriented application. The modeling and optimization of the studied systems is performed by means of scientific workflows. The execution of the specialized tools for modeling and optimization is done through web services. The pre-selection of these tools, depending on the level of model detail of the systems under study, and the data validation are performed on the specially developed testbeds. Such testbeds are constructed as system workflows. This is one of the distinguishing features of our approach. The application of the proposed environment is demonstrated through a case study on the synthesis of a microgrid (a special case of a multi-energy system). As a result of the study, an optimal microgrid configuration was proposed. It is based on the cogeneration of heat and electricity generation using natural gas.

The global industries are gradually moving towards the development of low-carbon, however, construction industry has significant energy consumption and carbon emissions. To making the promotion of industrialized construction with low-carbon and energy-efficient, prefabricated buildings are one of important ways to achieve environmental sustainability. This study first investigated the development and status of prefabricated concrete component factories in Fujian Province about regional distribution and production conditions and collecting data of carbon emissions, time and costs to create a multi-objective optimization model for intelligent and collaborative production. All while keeping costs as low as possible, using ant colony algorithms meets low-carbon and promotes sustainable development of prefabricated buildings. With the number of iterations, the overall cost shows a downward during the optimization process. According to optimization modes, the result of slabs indicates the minimum production cost (5.7023 million with carbon emissions 1154.85 tons), and a cost difference of 10000 can result in a maximum carbon emissions difference of 50 tons while minimizing costs to be the primary goal. Compared with general production, the optimization of collaborative production reduces both costs and carbon emissions. Meanwhile, focused on normal and rush mod, the cost reduced more than 20% can be down to 50% on carbon emissions. Therefore, effectively reducing carbon emissions in the component production will contribute to the sustainability of the construction industry.

Background/Objectives: This study investigates the effectiveness of an herbal formulation, STRESSLESS (ST-65), which combines ashwagandha (Withania somnifera) and bacopa (Bacopa monnieri), on SH-SY5Y human neuroblastoma cells. Given the rising interest in natural compounds for neuroprotection and stress alleviation, we aimed to explore the cellular and molecular effects of this formulation. Methods: Utilizing a nuclear magnetic resonance (NMR) metabolomic approach and ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS), we identified key bioactive compounds in ST-65, including withanolides from ashwagandha and bacosides from bacopa. Results: Our findings indicate that ST-65 treatment significantly alters the metabolic profile of SH-SY5Y cells. Key changes included increased levels of metabolites linked to neuroprotection, energy metabolism, and antioxidant defense. Notable enhancements were observed in specific amino acids and neuroprotective compounds, suggesting activation of neuroprotective mechanisms and mitigation of stress-induced damage. Conclusions: The study reveals a complex phyto-chemical profile of ST-65 and underscores its potential as a natural active agent for addressing stress-related neurodegenerative conditions. These insights into neuronal mechanisms provide a foundation for further exploration of herbal formulations in neuroprotection.

The escalating issue of phenol dye-containing wastewater necessitates the development of efficient and sustainable treatment methods. In this context, we present a novel composite photocatalyst comprising ZnFe2O4 (ZFO) nanoparticles supported on nanocellulose(NC), aimed at addressing this environmental challenge. The synthesis involved a facile hydrothermal method followed by impregnation of ZFO nanoparticles onto the NC matrix. ZFO, as a weakly magnetic semiconductor catalytic material, was utilized in photocatalytic experiments under magnetic field conditions. By controlling the electron spin states through the magnetic field, electron-hole recombination was suppressed, resulting in improved photocatalytic performance.The resultant composite exhibited remarkable photocatalytic activity under visible light irradiation, owing to the synergistic effect between ZFO nanoparticles and NC. Through systematic evaluation, we demonstrated the high efficiency and stability of the composite photocatalyst in degrading phenol dye pollutants in aqueous solutions. Furthermore, the composite material exhibited promising recyclability, retaining its activity even after multiple usage cycles. This study underscores the potential of the ZFO/NC composite for sustainable wastewater treatment, offering a promising avenue for environmental remediation.

Background: In the UK pharmacy professionals (pharmacists and pharmacy technicians) contribute to the delivery of local and national public or population health interventions. Existing literature predominantly focuses on micro-level activities, primarily in community pharmacies typically delivering public health interventions to individuals. Despite their sizeable daily interaction, there is little-known evidence on pharmacy professionals’ (PPs) involvement at meso and macro levels nor to what extent pharmacy professionals have specialist/advanced roles within public health practice. This mixed method study presents recommendations for future action. The study explored pharmacy professionals’ specialist/advanced roles within public health as well as their opportunities and barriers to career development. Method: The study included two surveys (for pharmacy professionals and public health professionals (PhPs)), a call for evidence and two workshops to develop recommendations. Pharmacy professionals (n=128) and public health professionals (n= 54) across the UK participated in the survey. Results: The majority of PP respondents were females (70%); pharmacists (85%), working in primary (33%) or secondary (25%) care settings, mainly based in England (75%), most (63%) lacked formal public health qualifications, although they were involved in a diverse range of public health interventions. The PhPs were mostly females (67%), practicing in England (58%). Both professional groups identified opportunities and barriers to pharmacy professionals' involvement in public health. Almost half or the PhP respondents (44%) stated that they had a pharmacy professional working as part of their current public health teams. Eighty-seven per cent of PhP respondents (45/52) agreed that having pharmacists or pharmacy technicians specialising in public health would be beneficial or very beneficial. Most documents, reports, case histories provided through the call for evidence were unpublished. The workshops generated 94 recommendations, highlighting collaboration and the need to acknowledge pharmacy professionals' contributions to public health. Conclusion: The recommendations for action had three main themes: national strategic approach and commissioning, workforce development and further pharmaceutical public health evidence reviews research.

Background: Barrett’s Esophagus (BE) is the only known precursor for esophageal adenocarcinoma (EAC). Patients with multiple risk factors for BE/EAC are recommended for screening, however few eligible patients undergo evaluation by endoscopy. EsoGuard® (EG) is a commercially available biomarker assay used to analyze esophageal cells collected non-endoscopically with EsoCheck® (EC), for qualitative detection of BE/EAC. This study evaluates real-world clinical utility of EG on cells collected with EC in patients defined by U.S. gastroenterology societies to be at-risk for BE and EAC. Methods: This multi-center, observational CLinical Utility of EsoGuard (CLUE) study enrolled screening-eligible patients as defined by the American College of Gastroenterology (ACG) and the American Gastroenterological Association (AGA). Clinical utility was evaluated by the provider decision impact of EG, and additionally by assessing patient compliance outcomes with recommended follow-up testing. Results: 551 patients were enrolled, with a mean age of 62.0 ± 12.4 years and 56.1% (309/551) meeting ACG guideline criteria for BE screening. EC cell collection was successful in 97.1% (535/551), among which EG positivity rate was 27.3% (n = 146). The provider decision impact was high with 100% of EG positive patients being referred for esophagogastroduodenoscopy (EGD), while 98% of EG negative patients were not referred. Among EG positive patients, the overall compliance with follow-up EGD was 85.4%. Conclusions: Combining EC non-endoscopic esophageal cell collection with the EG biomarker assay is effective in guiding provider decision-making for detection of BE and EAC. Patients with positive EG results demonstrate high compliance with recommended follow-up EGD.

The positive relationship between species richness and area is a fundamental principle in ecology. However, this pattern deviates on small islands, where species richness either changes independently of area or increases at a slower rate—a phenomenon known as the Small-Island Effect (SIE). While the SIE has been well-documented in natural ecosystem, its presence in highly fragmented and disturbed urban ecosystem remains unexplored, posing challenges for urban vegetation conservation. Urban remnant vegetation, isolated by surrounding infrastructures, preserves intact zonal vegetation characteristics, serves as a benchmark for restoring near-natural habitats and offers ideal conditions to test the existence of SIE in human-altered landscapes. In this study, we surveyed 17 remnant vegetation patches in Qingdao City, China. In total of 331 plants attributed to 255 genus in 81 families been recorded. Firstly, by using six species-area relationship regression models tested SIE for remnant vegetation with different life form (i.e. annual herb, perennial herb, shrub and tree), we found SIE detected in only woody plants, with area threshold ranging from 6.38 ha (tree) to 11.91 ha (shrub). According to stepwise and generalized linear regression mode analysis we found SIE detected in only woody plants, with area threshold 6.38 ha for tree and 11.91 ha for shrub. Further analysis revealed that the SIE in shrub was driven by landscape shape index, perimeter-area ratio, and the proportion of sealed surface within patch. For trees, the SIE was influenced by the distance to the source of species, GDP, night light intensity and perimeter-area ratio. This finding justifies that conservation in urban planning, construction and development should focus not only on protecting large areas, but also on maintaining and promoting diverse habitats within these areas. At the same time, reducing anthropogenic disturbance and enhancing the connectivity of green spaces are important for the persistence of metacommunities and can contribute to the local species pool, thus potentially improving the ecological resilience of urban environments.

Cervical cancer remains a significant global public health challenge, affecting over half a million women annually, with a mortality rate of approximately 60%, especially in resource-limited regions. This study presents an advanced methodology for cervical cancer diagnosis through deep learning techniques. Utilizing a publicly available cervical cancer image dataset, the research introduces a novel classification framework that integrates a Neural Feature Extractor (NFE) based on a pre-trained VGG16 architecture and an AutoInt model for automatic feature interaction learning. The extracted features are processed through machine learning classifiers such as KNN, LGBM, Extra Trees, and others for classification tasks. Among these classifiers, KNN achieved the highest accuracy of 99.96%, followed closely by LGBM at 99.92%. The study also assesses the computational complexity of various models, demonstrating that simpler models like LDA exhibit faster prediction times, while more complex models, such as KNN and LGBM, provide higher accuracy. These findings highlight the potential of deep learning frameworks in improving cervical cancer classification accuracy, especially in resource-limited environments.

Ischemic stroke (IS) remains a leading cause of mortality and long-term disability worldwide, with limited therapeutic options available. Despite the success of early interventions such as tissue-type plasminogen activator administration and mechanical thrombectomy, many patients continue to experience persistent neurological deficits. The pathophysiology of IS is multifaceted, encompassing excitotoxicity, oxidative and nitrosative stress, inflammation, and blood-brain barrier disruption, all of which contribute to neural cell death, further complicating the treatment of IS. Recently, extracellular vesicles (EVs) secreted naturally by various cell types have emerged as promising therapeutic agents because of their ability to facilitate selective cell-to-cell communication, neuroprotection, and tissue regeneration. Furthermore, engineered EVs, designed to enhance targeted delivery and therapeutic cargo, hold potential to improve their therapeutic benefits by mitigating neuronal damage and promoting neurogenesis and angiogenesis. This review summarizes the characteristics of EVs, the molecular mechanisms underlying IS pathophysiology, and the emerging role of EVs in IS treatment at the molecular level. This review also explores the recent advancements in EV engineering, including the incorporation of specific proteins, RNAs, or pharmacological agents into EVs to enhance their therapeutic efficacy.

There are far more options to treat diabetes now, than about 15-20 years ago. Among the newer agents, glucagon-like peptide 1 receptor agonists (GLP-1RA), dipeptidyl peptidase-4 inhibitors (DPP-4i) and sodium-glucose cotransporter 2 inhibitors (SGLT-2i), are in common use. While their benefits extend far beyond the management of hyperglycemia, some pose concerns, and challenges to the anesthesia providers. Retained gastric contents due to slow gastric emptying is a significant drawback of GLP-1RA. It benefits the patient in losing weight; however, increases the risk of aspiration and can surprise an unsuspecting anesthesia provider. National associations have issued statements in an effort the mitigate the risk. Recommendations range from withholding of GLP-1RA for a predefined period of time, gastric ultrasound to evaluate the gastric contents and modification of anesthesia management especially with regards to the airway and cancellation of a scheduled (elective) surgery/procedure. SGLT-2i are known to increase the risk of euglycemic ketoacidosis. Duration of preprocedural withholding of SGLT-2i varies with the drug. Use of both GLP-1RA and SGLT-2i extends beyond the treatment of diabetes. While SGLT-2i are being extensively used to treat obesity, they are also used in the management of heart failure. A new insulin formulation of basal insulin that can be administered once weekly to achieve similar blood sugar control as once daily formulation has become available in some countries. Labelled as icodec, this is as yet not approved by the FDA because of severe hypoglycemia concerns. Imeglimin is a variant of metformin with unique mechanisms of action. It is approved for clinical use for type 2 diabetes in Japan and India. Another, as yet investigational product is IcoSema, a once-weekly combination of insulin icodec plus semaglutide. A discussion of all aspects of these drugs is warranted.

Dependence on seafood imports is growing for many nations, effectively exporting the environmental and social impacts from consuming nations to producers. While countries have commitments to national regulations and global sustainability targets, such as the United Nations Sustainable Development Goals, sustainability standards for imported seafood are lacking. We examine the sustainability implications of high-income countries’ reliance on seafood imports, using Australia as a case study. Australia imports around 60-70% of domestically consumed seafood, with 96.5% imported from 20 countries. Compared to Australia, these countries generally have lower environmental performance, higher vulnerability to slavery, and increased risk of illicit trade in their supply chains. Yet, biophysical limits on wild catch, low demand for underutilized species, social conflict, environmental concerns over aquaculture expansion, and low domestic production, suggest imports will likely remain an important source of seafood for Australian consumers. Other high-income countries in Europe and North America face similar challenges. These countries have a pivotal role in promoting responsible trade. Comprehensive sustainability assessments that integrate environmental and social considerations of production and trade, improved mapping of seafood production activities, and more granular trade data will be critical for informed and effective trade regulations that support sustainability commitments.

Background/Objectives: Brain-computer interfaces (BCIs) are a rapidly developing technology that captures and transmits brain signals to external sources, allowing the user control of devices such as prosthetics. BCI technology offers the potential to restore physical capabilities in the body and change how we interact and communicate with computers and each other. While BCI tech-nology has existed for decades, recent developments have caused the technology to generate a host of ethical issues and discussions in both academic and public circles. Given that media represen-tation has the potential to shape public perception and policy, it is necessary to evaluate the space that these issues take in public discourse. Methods: We conducted a structured review of media articles in English discussing ethical issues of BCI technology from 2013-2024 as indexed by LexisNexis. Our searches yielded 675 articles, with a final sample containing 182 articles. We assessed the themes of the articles and coded them based on the ethical issues discussed, ethical frameworks, recommendations, tone, and application of technology. Results: Our results showed a marked rise in interest in media articles over time, signaling an increased focus on this topic. The majority of articles adopted a balanced or neutral tone when discussing BCIs and focused on ethical issues regarding privacy, autonomy, and regulation. Conclusions: The growing recogni-tion of ethical considerations in BCI highlights the importance of ethical discourse in shaping the future of the field.

Background/Objectives: Venous ulcers is the most common complication of all ulcers of the lower limbs that occurs in patients with chronic venous insufficiency. The treatment of venous ulcers is multidisciplinary, with physiotherapy intervention serving as an adjuvant therapy in the management of these wounds. This study aims to investigate physiotherapeutic interventions for the management of venous ulcers; Methods: Exploratory and descriptive study using the Delphi method. The interventions were derived from a systematic review of the literature and presented to a panel of experts for evaluation. The panel comprised 12 experts in wound care, and two rounds of analysis were conducted. A quantitative analysis was performed to assess the level of agreement in responses, while qualitative analysis was applied to the experts' comments; Results: In the first round, the consensus varied between 80% to 100% within the answers, and in the second round it ranged from 83,3% to 100%. In the second round, all interventions obtained at least 80% consensus. The intervention includes the use of compression therapy and therapeutic exercise: resistance training, aerobic exercise, stretching, balance training, and the use of a vibrating platform. Conclusions: Physiotherapy can help as an adjuvant therapy to a specific local treatment of venous ulcers. The main intervention includes therapeutic exercise and the use of compression therapy.

Recently, the integration of neural networks into robotic exoskeletons for physical rehabilitation has become popular due to their ability to interpret complex physiological signals. Surface electromyography (sEMG), electromyography (EMG), electroencephalography (EEG), and other physiological signals enable communication between the human body and robotic systems. Utilizing physiological signals for communicating with robots plays a crucial role in robot assisted neurorehabilitation. This systematic review synthesizes 44 peer-reviewed studies, exploring how neural networks can improve exoskeleton robot assisted rehabilitation for individuals with impaired upper limbs. By categorizing the studies based on robot assisted joints, sensor systems, and control methodologies, we offer a comprehensive overview of neural network applications in this field. Our findings demonstrate that neural networks, such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Radial Basis Function Neural Networks (RBFNN), and other forms of neural network significantly contribute to patient specific rehabilitation by enabling adaptive learning and personalized therapy. CNNs improve motion intention estimation and control accuracy, while LSTM networks capture temporal muscle activity patterns for real-time rehabilitation. RBFNNs improve human-robot interaction by adapting to individual movement patterns, leading to more personalized and efficient therapy. This review highlights the potential of neural networks to revolutionize upper limb rehabilitation, improving motor recovery and patient outcomes in both clinical and home-based settings. It also recommends the future direction to customize existing neural networks for robot assisted rehabilitation applications.

ChatGPT, a state-of-the-art AI model created by OpenAI, is renowned for its ability to produce text responses that closely mimic human communication. Recently, it has broadened its scope to encompass multimodal interactions, rendering it particularly advantageous for medical applications. In this paper, we explore into the potential of ChatGPT in the realm of Ultrasound (US) imaging, Our investigation encompasses a series of case studies and examples, each tailored to demonstrate ChatGPT’s application in various aspects of US imaging. These include emphasizing its capacity for intricate analyses, including grayscale texture examination and perfusion metrics in contrast-enhanced ultrasound (CEUS). Moreover, its involvement in real-time imaging guidance, its role in streamlining workflow efficiencies, and its utility in the simplification of medical report generation to facilitate better patient communication. The case studies, while successful, unveil certain limitations and challenges, prominently featuring concerns regarding privacy and technical constraints. Despite these challenges, the evidence gathered from our examples indicates that ChatGPT's innovative features and its ability to operate across multiple modes significantly enhance the efficiency of quantitative image analysis in US imaging. By improving diagnostic precision and potentially elevating patient care outcomes, ChatGPT marks a significant stride forward in healthcare technology, though it necessitates careful consideration of its associated ethical and technical challenges.

Cardiovascular diseases (CVD) remain a leading cause of global morbidity and mortality. Recent advancements in high-throughput omics techniques have enhanced our understanding of the human microbiome's role in the development of CVD. Although the relationship between the gut microbiome and CVD has attracted considerable research attention and has been rapidly evolving in recent years, the role of the oral microbiome remains less understood, with most prior studies focusing on periodontitis-related pathogens. In this review, we summarized previously reported associations between the oral microbiome and CVD, highlighting known CVD-associated taxa such as Porphyromonas gingivalis, Fusobacterium nucleatum, and Aggregatibacter actinomycetemcomitans. We also discussed the interactions between the oral and gut microbes. The potential mechanisms by which the oral microbiota can influence CVD development include oral and systemic inflammation, immune responses, cytokine release, translocation of oral bacteria into the bloodstream, and the impact of microbial-related products such as microbial metabolites (e.g., short-chain fatty acids [SCFAs], trimethylamine oxide [TMAO], hydrogen sulfide [H2S], nitric oxide [NO]) and specific toxins (e.g., lipopolysaccharide [LPS]). The processes driven by these mechanisms may contribute to atherosclerosis, endothelial dysfunction, and other cardiovascular pathologies. Integrated multi-omics methodologies, along with large-scale longitudinal population studies and intervention studies, will facilitate a deeper understanding of the metabolic and functional roles of the oral microbiome in cardiovascular health. This fundamental knowledge will support the development of targeted interventions and effective therapies to prevent or reduce the progression from cardiovascular risk to clinical CVD events.

Leishmaniasis is a serious infectious disease caused by Leishmania parasites, predominantly affecting tropical and subtropical regions. These parasites replicate within macrophages, manipulating the host immune response and facilitating infection progression. This study examined the expression profiles of long non-coding RNAs (lncRNAs), potential cis-target genes, and hub genes at different time points in human macrophages infected with Leishmania major. RNA-Seq analysis identified 39,828 lncRNAs, with 2,903 showing differential expression at one or more time points. As the infection progressed (4, 24, 48, and 72 hours), the number of up- and down-regulated lncRNAs gradually decreased. Six lncRNAs (lnc-UNC5D-8, lnc-TENM3-1, DIRC3-1, lnc-MTRNR2L12-10, lnc-FAM43A-6, and AKAP2-1) were consistently differentially expressed across all time points, potentially playing key roles in regulating the host immune response. Time-specific hub genes were also identified, regulating critical processes such as keratinization, epigenetic modifications, and immune responses. In particular, these genes were pivotal during the later stages of infection in maintaining tissue integrity and regulating immune responses. Early immune responses were dominated by immunoglobulin receptor activity and adaptive immune system activation. These findings highlight the critical roles of lncRNAs and hub genes in macrophage responses to Leishmania infection, offering potential targets for future therapeutic strategies.

This article presents a radical reassessment of scientific validation processes, arguing that traditional peer review has become an outdated, inefficient, and ultimately flawed mechanism for ensuring research quality. Modern artificial intelligence systems demonstrate superior capabilities in analyzing methodological rigor, statistical validity, and literature comprehensiveness, while being free from human cognitive biases, professional rivalries, and institutional politics. Through examination of empirical evidence, we demonstrate how AI systems consistently outperform human reviewers in speed, accuracy, and comprehensiveness of research evaluation. The current peer review system, characterized by months-long delays, substantial costs, and demonstrable biases, actively impedes scientific progress. We propose a fully automated AI-driven validation framework that can evaluate research in real-time, identify methodological flaws, verify statistical analyses, and assess significance within the broader scientific context. This transformation would democratize research validation, eliminate publication bottlenecks, and accelerate scientific progress while maintaining higher standards of methodological rigor than currently possible under human review.

Artificial intelligence techniques including machine learning models have shown success in variety of domains. This is more evident with complex models including deep learning. However, such success accompanied by vagueness around how the models work, the internal mechanism and making a decision. Explainable Artificial intelligence (XAI) emerged as a new field of research to uncover the mystery around how the complex models work. The ultimate aim of XAI is to make the complex models more transparent, trustworthy and understandable even by lay-persons with no technical background. XAI could come in different forms including heatmaps applied to images, significant concepts to the model when making a decision, informative features with tabular data, one-feature effect on the outcome, fuzzy logic rules, textual explanation through images captioning, uncertainty quantification and much more. There are several factors affect the XAI forms including the used data and the model. This paper is dedicated to review and group the current XAI methods in the literature based on the outcome form. In addition, the paper discusses the XAI groups, how they work, strengths and weaknesses. Our paper shows that although e XAI methods have been used extensively within the research context, their employability in real life problems especially in sensitive domains might be unreasonable in the current stage.

Background: Cognitive decline (CD) is a significant concern in Parkinson's disease (PD), highlighting the need for reliable risk prediction models for early intervention. This study used machine learning (ML) techniques to predict the CD risk over five-year in early-stage PD. Methods: Data from the Early Parkinson's Disease Longitudinal Singapore (2014 to 2018) was used to predict CD defined as a one-unit annual decrease or a one-unit decline in Montreal Cognitive Assessment (MoCA) over two consecutive years. Four ML methods—AutoScore, Random Forest, K-Nearest Neighbors and Neural Network—were applied using baseline demographics, clinical assessments and blood biomarkers. Model performance was evaluated using area under the curve (AUC), sensitivity and specificity. Results: Variable selection identified key predictors of CD, including education year, diastolic lying blood pressure, diastolic standing blood pressure, systolic lying blood pressure, Hoehn and Yahr scale, body mass index, phosphorylated tau at threonine 181, total tau, Neurofilament light chain and suppression of tumorigenicity 2. Random Forest was the most effective, achieving an AUC of 0.93 (95% CI: 0.89, 0.97), using 10-fold cross-validation. Conclusion: ML-based models offer potential for early identification of patients at high risk of CD, facilitating targeted interventions and improving patient outcomes in PD management.

The aim of the study was to evaluate two widely studied biomarkers: MMP-9 and S100B in blood serum in healthy people after physical exercise mimicking physical activity during bi-lateral tonic-clonic seizures. Both biomarkers have been shown to increase after seizures, and their serum levels tend to be higher in epilepsy patients compared to individuals without epi-lepsy. To achieve this goal, we have developed two sets of exercises, each consisting of a tonic contrac-tion phase and a phase of alternating muscle work, lasting a total of approximately 5 minutes. Both sets of motor exercises were performed in the same group of healthy volunteers 6 months apart. Serum samples were collected before exercise, 3 hours after exercise, and the next day (time points 0, 3 hours, and 24 hours). Our results demonstrated that both sets of motor exercises led to a similar increase in MMP-9 levels, while neither while neither affected S100B levels. No significant differences in MMP-9 levels were observed due to muscle failure. We propose that the increase in MMP-9 is primarily induced by muscle contraction and additional mechanisms observed during exercise, such as neutrophil degranulation. S100B appears to be a promising biomarker in epilepsy, as it is not induced by physical activity but does increase following seizures.

Since the 1960s, Iran’s major cities have experienced significant migration from the country’s rural areas and from overseas. Although many urban planning and design concepts can be traced back to Iran, the coun-try’s planning machinery has failed to effectively regulate urban growth, notably in the city peripheries, where land use has changed radically as semi-rural areas have been developed in a haphazard fashion with scant adherence to existing plans and planning regulations. Farahzad is one such area in the urban periph-ery of Tehran, where a range of sub-standard dwellings have been built and urban services are deficient in many regards. This article uses secondary documentation, images developed from GIS data, and interview material with NGOs and other local authorities to explore the growth of the neighbourhood in recent dec-ades, and assess the current problems confronting both residents and local authorities. The article examines how the urban landscape has evolved, how the resident population has grown, and the nature of the social and economic issues that persist today. Findings suggest that the planning machinery, and land use zoning in particular, have played little part in guiding or controlling urban development in this peripheral neigh-bourhood, and that identifying urban fabrics may prove a useful way of assessing socio-economic and physical development needs in such circumstances. The article makes a small contribution to our under-standing of the change dynamics in a peripheral neighbourhood of a major city in the developing world.

The safe transition of older adults from hospital to home or other care settings is a critical aspect of healthcare that significantly impacts patient outcomes. Barriers to safe social disposition may include clinical, healthcare system, or social factors. Despite advances in medical care, many older adults face these barriers during the transition process, which can lead to adverse health events, re-admission, and decreased quality of life. This review explores the obstacles to safe disposition for older adults post-hospitalization and discusses potential strategies to mitigate these issues.

Soil-borne diseases threaten sustainable agriculture, traditionally managed by chemical fumigants, whose use is now restricted due to environmental and health concerns. This study evaluates the biofumigation potential of Brassicaceae species, specifically Brassica carinata A.Braun., Brassica juncea (L.) Vassiliĭ Matveievitch Czernajew., Raphanus sativus L., and Sinapis alba L., cultivated in central Spain. Field trials across two growing cycles assessed biomass production, glucosinolates (GSLs) concentration, photosynthetically active radiation (PAR) interception, and radiation use efficiency (RUE). Biomass production varied across species and sampling dates, with S. alba and R. sativus outperforming other species in shorter cycles, while B. juncea and B. carinata showed more efficient GLS profile to soil-borne diseases control, particularly in aliphatic GSLs like Sinigrin. Results highlight B. juncea and B. carinata as potent biofumigants due to their high GSL levels, whereas S. alba and R. sativus are more suited to early biomass production. The study also explores the Chlorophyll Content Index (SPAD) as a potential field indicator of GSL concentration, providing a practical approach for optimizing biofumigation timing. These findings support the selection of specific Brassicaceae species adapted to climatic conditions and crop cycles for effective biofumigation in sustainable agricultural practices.

Recent research has demonstrated that endophytes, or non-pathogenic fungi found in plant bi-omes, can dramatically alter the expression of host plants. Our natural ecology is burdened by an increasing amount of chemicals owing to the rapid modernization of agricultural sector. This study examined the use of natural pesticides in place of chemical pesticides to eradicate patho-genic fungi that destroy cabbage crops every year. Agrochemicals-, or pesticides-, are used in ag-ricultural land and crops to protect plants from various diseases. The cabbage crop was treated using the appropriate methodology. In an effort to identify endophytic fungi that can stop the growth of pathogenic fungi, samples are gathered from markets and farms to find solutions at the source of the issue. The outcome revealed a number of fungal endophytes that have the ability to suppress fungal infections and act as a defense against crops. Because plants have endophytic fungi, which act as a barrier to protect plants, in addition to harmful fungi, fungi play a critical role in the discussion of natural pesticides. These findings also indicate that market samples were more contaminated with microorganisms. These endophytes can be the main source of materials for producing natural insecticides for crops;-, thus, further research on them can be conducted. Furthermore, farmers may be able to overcome obstacles and find a potential solution with the use of numerous more potent strategies in the field of microorganism development.

In an era of unprecedented global challenges, the limitations of traditional, linear models of innovation have become increasingly apparent. This pioneering work introduces the revolutionary "decuple helix" framework - a comprehensive, multistakeholder approach to driving transformative change through collaborative innovation. By expanding the scope of stakeholder engagement to include a diverse array of actors, from academia and industry to marginalized communities, the natural environment, and international organizations, the decuple helix model unlocks new frontiers of collective creativity, values-driven problem-solving, and systemic regeneration. Drawing on cutting-edge theoretical foundations and a wealth of empirical evidence, this groundbreaking contribution demonstrates how the strategic integration of these ten key stakeholders can catalyze a profound paradigm shift in the way we conceptualize, organize, and mobilize innovation to address the complex, interconnected crises facing our world. Challenging dominant narratives of technocratic, top-down innovation, this article unveils a radically reimagined vision - one in which diverse epistemologies, holistic well-being, and ecological restoration converge to unlock a thriving, equitable, and sustainable future for all.

This paper explores the relationships between differential topology and general relativity, examining the classical framework's applicability in modelling gravitational fields and the limits encountered with topological defects such as cosmic strings. By illustrating smooth metric structures, holonomy, and geodesic completeness, we reveal the foundational role of differential topology in describing spacetime curvature without quantum corrections. The Einstein field equations and global topological invariants are discussed in contexts where continuous manifolds hold, while highlighting how phenomena like cosmic strings challenge these assumptions. We propose that such topological defects introduce singular behaviours where traditional differential topology reaches its boundaries, potentially necessitating quantum gravitational models for a complete description. Visualizations of Schwarzschild curvature, holonomy in spherical surfaces, and geodesic paths provide insights into gravitational field variability and topological constraints in classical models, underscoring the mathematical and physical principles at play.This paper explores the relationships between differential topology and general relativity, examining the classical framework's applicability in modelling gravitational fields and the limits encountered with topological defects such as cosmic strings. By illustrating smooth metric structures, holonomy, and geodesic completeness, we reveal the foundational role of differential topology in describing spacetime curvature without quantum corrections. The Einstein field equations and global topological invariants are discussed in contexts where continuous manifolds hold, while highlighting how phenomena like cosmic strings challenge these assumptions. We propose that such topological defects introduce singular behaviours where traditional differential topology reaches its boundaries, potentially necessitating quantum gravitational models for a complete description. Visualizations of Schwarzschild curvature, holonomy in spherical surfaces, and geodesic paths provide insights into gravitational field variability and topological constraints in classical models, underscoring the mathematical and physical principles at play.

Estrogen receptor α (ERα) drives 2 out of 3 breast cancers and therefore ERα is a major therapeutic target for ER-positive breast cancer patients. Drugs that inhibit ERα activity or block estrogen synthesis in the body are currently being used in the clinic to treat ER-positive breast cancer and have been quite successful in controlling breast cancer progression for the majority of patients. However, ER-positive breast cancer often becomes resistant to these endocrine therapies, leading to endocrine-resistant metastatic breast cancer, a very aggressive cancer that leads to death. Recent large-scale genomic studies have revealed a series of activating somatic mutations in the ERα gene (ESR1) in endocrine-resistant metastatic breast cancer patients. Of these, Y537S and D538G mutations are found at a much higher rate in patients with metastatic breast cancer. Remarkably, these mutations produce an ERα with much higher transcriptional activity than wild type in the absence of estradiol, and traditional endocrine therapy has poor efficacy against ER mutants. Therefore, the development of new drugs that target ER mutants is an unmet clinical need for endocrine-resistant metastatic breast cancer. This review summarizes the recent preclinical and clinical trials targeting estrogen receptor mutant breast cancer.

Enzyme immobilization has emerged as an essential technique in industrial applications of enzymes. Silica (SiO2) serves as a prominent support material for enzyme immobilization. Recent advancements have led to the development of various silica-binding proteins (SBPs) and silica-forming proteins (SFPs) that are invaluable tools in immobilizing enzymes on siliceous materials. SBPs facilitate the controlled orientation of enzymes on silica surfaces, while SFPs enable the biomimetic synthesis and encapsulation of enzymes within silica particles. This review categorizes SBPs and SFPs into naturally occurring and artificially engineered types, providing a comprehensive overview of their mechanisms and characteristics relevant to enzyme immobilization.

Background: Ulcerative colitis is a chronic inflammatory bowel disease of the colon mucosa associated with a higher risk of colorectal cancer. Objective: This study is a proof-of-concept analysis aimed to test the feasibility of classifying hematoxylin and eosin (H&E) histological images of ulcerative colitis, normal colon, and colorectal cancer using Artificial Intelligence (AI); Methods: A convolutional neural network (CNN) was designed and trained to classify the 3 types of diagnosis, including 35 cases of ulcerative colitis (n = 9,281 images), 21 colon control (n = 12,246), and 18 colorectal cancer (n = 63,725). The data were partitioned into training (70%) and validation sets (10%) for training the network, and a test set (20%) to test the performance on the new data. Explainable artificial intelligence for computer vision was performed using gradient-weighted class activation mapping (Grad-CAM) to understand the classification decisions; and additional LAIR1 and TOX2 immunohistochemistry was performed in ulcerative colitis to analyze the immune microenvironment; Results: The trained network classified the 3 diagnoses with high performance. The model classified ulcerative colitis with an accuracy of 99.1%, precision of 97.1%, recall of 94.8%, an F1-score of 95.9%, and specificity of 99.6%. For colorectal cancer, the performance was as follows: accuracy, 99.8%; precision, 99.9%; recall, 99.8%; F1-score 99.9%; and specificity, 99.7%. The Grad-CAM heatmap confirmed which parts of the image were most important for classification. The CNN also managed to differentiate between steroid-requiring (SR) and nonsteroid requiring (non-SR) ulcerative colitis based on H&E, LAIR1 and TOX2 staining; Conclusions: Convolutional neural networks are essential tools for deep learning and are especially suited for image recognition in conditions such as ulcerative colitis and colorectal cancer; LAIR1 and TOX2 are promising immuno-oncology markers in ulcerative colitis.

There is growing interest in understanding the connection between concussions on physical and mental health acutely and, in retirement, on neurodegenerative diseases. In this study, we utilised a “high-impact trauma” (HIT) device to investigate the effects of multiple concussions on the motor activity, and lifespan of the adult female Drosophila melanogaster as well as reactive oxygen and nitrogen species (RONS) levels in the fly brain and body. We found that repetitive hits, while not having acute physical effects, significantly increased long-term mobility deficits, and shortened lifespan, and exacerbated oxidative stress in both the brain and body. Notably, the novel CONKA product (Withania somnifera, Curcuma longa, Melissa officinalis, Rhodiola rosea, Vaccinium myrtillus) demonstrated promising protective effects, including mitigation of motor deficits, extension of lifespan, and reduction of oxidative stress in both the brain and body of the flies. When evaluating the contributions of individual components within the CONKA formulation, Curcuma longa, despite extending lifespan, did not contribute to mobility improvement or oxidative stress amelioration. This suggests that the benefits of CONKA are largely driven by its other four components, which displayed all the positive effects evaluated. The exclusion of Curcuma longa may streamline the formulation without diminishing its brain and body effects following a history of repetitive concussions, although this would require further study to confirm. Oral bioavailability may be an issue with Curcuma longa. Taken together, the findings validate that Drosophila melanogaster is a suitable system to mimic and investigate the effects of repetitive concussions on bodies and brains and assess the effects of health products and drug therapies.

Plastic polymers derived from petroleum exhibit diverse physical properties, ranging from flexible to rigid, permeable to impermeable, and hydrophilic to hydrophobic. However, when discarded improperly, they become a significant problem. Plastic pollution poses a threat to the planet, affecting various ecosystems, including terrestrial environments, rivers, lakes, oceans, and underground freshwater sources. These ecosystems are polluted with ropes, fishing nets, syringes, plastic bottles, and garbage bags. This issue is closely linked to the incredible versatility of these materials, whose continuous growth in production is driven by their market value. The current linear production and consumption of plastics are unsustainable and result in excessive waste. This waste is poorly managed, leading to pollution and carbon dioxide emissions that undermine global climate goals and the Sustainable Development Goals (SDGs). While some macroplastics are mechanically recycled, others are incinerated, and a significant portion ends up in the environment. Strategies for transforming and depolymerizing plastics offer potential pathways for future reuse. In this article, we explore the latest alternatives for the transformation and depolymerization of macroplastics through processes such as pyrolysis and hydrogenolysis.

For many years, high-power laser technology has been divided between industrial applications, which prioritize higher average powers and repetition rates, and academic research, which focuses on achieving higher peak powers and ultrashort pulse durations. The introduction of Yb-doped crystals in laser technology has paved the way for a new generation of laser sources that bridge the gap between industrial and academic requirements, combining high average power with ultrashort pulse capabilities. These advancements enable the integration of compact, adaptable front-end stages, making such lasers versatile for scientific applications. In Lisbon, the Laboratory of Intense Lasers leverages this technology with a system that combines commercial and custom-built front-end stages to enhance operational flexibility. In this paper, we present the current status of this facility and outline upcoming upgrades. We also showcase applications enabled by these high-power laser sources, including semiconductor studies, nitrogen-vacancy generation, bi-photonics, and time-resolved spectroscopy.

Schizophrenia is a severe neuropsychiatric illness of uncertain etiopathogenesis in which antipsychotic drugs can attenuate the symptoms, but patients rarely return to the premorbid level of functioning. In fact, with each relapse, people living with schizophrenia progress toward disability and cognitive impairment. We hypothesize that poor outcomes in schizophrenia reflect gray matter volume reduction, which continues despite treatment. We further hypothesize that aryl hydrocarbon receptor phosphorylates signal transducer and transcription-three protein, suppressing the gut barrier protectors, including brain-derived neurotrophic factor and interleukin-22. We construe that recombinant human IL-22 can improve schizophrenia outcome by the following actions: 1. Averting microbial translocation outside the gastrointestinal tract by optimizing the gut barrier permeability. 2. Upregulation of BDNF, a neurotrophin lowered in schizophrenia. 3. Downregulation of interferon-gamma, a cytokine elevated in patients with schizophrenia. 4. Acceleration of wound healing and long-term memory formation via STAT3-induced inflammatory responses. This short review examines the influence of IL-22 and BDNF on the STAT3 /AhR axis. Based on the hypothesis described here, we discuss alternative SCZ interventions, including aryl hydrocarbon receptor antagonists, mitochondrial transplant, membrane lipid replacement, and recombinant human IL-22.

We describe the duality of incompressible Navier-Stokes fluid dynamics in three dimensions, leading to its reformulation in terms of a one-dimensional momentum loop equation. The momentum loop equation does not have finite-time blow-up solutions. The decaying turbulence is a solution of this equation equivalent to a string theory with discrete target space made of regular star polygons and Ising degrees of freedom on the sides. This string theory is solvable in the turbulent limit, equivalent to the quasiclassical approximation in a nontrivial calculable background. As a result, the spectrum of decay indexes is analytically computed, and it agrees very well with real and numerical experiments. Among the decay indexes there are complex conjugate pairs related to zeros of the Riemann zeta function. The Kolmogorov scaling laws are replaced by certain number theory functions, nonlinear in log-log scale.

Volatile organic compounds, colloquially referred to as “terpenes”, are proposed to impact the therapeutic qualities that are traditionally ascribed to cannabis. However, the contribution of these terpenes in anxiety, at relevant levels and exposure methods common with cannabis use, is lacking empirical assessment. We tested the anxiolytic properties of two prominent cannabis terpenes, linalool and β-myrcene, in male and female mice using short duration vapor pulls to model human inhalation when combusting flower or vaping cannabis oil. We observed sex differences in the locomotor effects and anxiolytic properties of these terpenes that depended on their exposure characteristics. Both linalool and β-myrcene had anxiolytic effects in female mice when delivered in discrete vapor pulls over the course of 30 minutes. In male mice, only a single vapor hit containing linalool or β-myrcene had anxiolytic effects. The combination of sub-effective levels of linalool and the phytocannabinoid, cannabidiol (CBD), had synergistic anxiolytic effects in females, but these Entourage Effects between CBD and terpenes were absent with β-myrcene for females and for either terpene in males. Together, our findings reveal sex differences in the anxiolytic properties of common cannabis terpenes and highlight the benefits of unique combinations of CBD and terpenes in expanding the therapeutic dose window.

Mobile Ad Hoc Networks (MANETs) are decentralized wireless networks, characterized by their dynamic topologies and node mobility. In the era of cutting-edge technologies, integrating Software-Defined Networking (SDN) with MANETs offers a promising solution to manage these challenges more efficiently. This paper presents a balanced discussion of MANETs and SDN, demonstrating how SDN principles, such as centralized control and network virtualization, can optimize MANET performance in terms of scalability, cost-efficiency, and security. A mathematical model is developed to analyze CAPEX, OPEX, and network efficiency.

Congenital heart defects (CHD) are the most common congenital defect, occurring in approximately 1 in 100 live births and being a leading cause of perinatal morbidity and mortality. Of note, approximately 25% of these defects are classified as critical, requiring immediate postnatal care by pediatric cardiology and neonatal cardiac surgery teams. Consequently, early and accurate diagnosis of CHD is key to proper prenatal and postnatal monitoring in a tertiary care setting. In this scenario, fetal echocardiography is considered the gold standard imaging ultrasound method for the diagnosis of CHD. However, the availability of this examination in clinical practice remains limited due to the requirement for a qualified specialist in pediatric cardiology. Moreover, in light of the relatively low prevalence of CHD among at-risk populations (approximately 10%), ultrasound cardiac screening for potential cardiac anomalies during routine second-trimester obstetric ultrasound scans represents a pivotal aspect of diagnosing CHD. In order to maximize the accuracy of CHD diagnosis, the views of the ventricular outflow tract and the superior mediastinum were added to the four-chamber view of the fetal heart for routine ultrasound screening according to international guidelines. In this context, four-dimensional spatio-temporal image correlation software (4D-STIC) were developed in the beginning 2000s. Some of the advantages of 4D-STIC in fetal cardiac evaluation include the enrichment of anatomical details of fetal cardiac images in the absence of the pregnant woman and the ability to send volumes for analysis by an expert in fetal cardiology by an internet link. Sequentially, new technologies have been developed such as Fetal Intelligent Navigation Echocardiography (FINE), also known as "5D-heart", in which the nine fetal cardiac views recommended during a fetal echocardiogram are automatically generated from the acquisition of a cardiac volume. Furthermore, artificial intelligence (AI) has recently emerged as a promising technological innovation, offering the potential to warn of possible cardiac anomalies and thus increase the ability of non-cardiology specialists to the diagnose of CHD. In the early 2010s, the advent of 3D reconstruction software combined with high-definition printers enabled virtual and 3D physical reconstruction of the fetal heart. The 3D physical models may improve parental counseling of fetal CHD, maternal-fetal interaction in cases of blind pregnant women, and interactive discussion by multidisciplinary health teams. In addition, the 3D physical and virtual models can be an useful tool for teaching of cardiovascular anatomy and to optimize surgical planning, enabling simulation rooms for surgical procedures. Therefore, in this review the authors discuss advanced image technologies aiming that may optimize the prenatal diagnosis of CHD.

TikTok has rapidly become one of the most popular social media platforms, and, thus, scholars should pay attention to its association with users’ mental health. Our aim was to synthesize and evaluate the association between problematic TikTok use and mental health. We applied the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines in our review. The review protocol was registered with PROSPERO (CRD42024582054). We searched PubMed, Scopus, Web of Science, PsycINFO, ProQuest, and CINAHL until September 02, 2024. We identified 16 studies with 15,821 individuals. All studies were cross-sectional and were conducted after 2019. Our meta-analysis showed a statistically significant positive association between TikTok use and depression (β = 0.321, 95% confidence interval [CI]: 0.261 to 0.381, p &lt; 0.001), and anxiety (β = 0.406, 95% CI: 0.279 to 0.533, p &lt; 0.001). Also, we found a positive association between TikTok use and body image issues, poor sleep, anger, distress intolerance, narcissism, and stress. Our findings suggest that problematic TikTok use has a negative impact on several mental health issues. Given the high levels of TikTok use especially among young adults, our findings are essential to further enhance our understanding of the impact of TikTok use on mental health. Finally, there is a need for further studies of better quality to assess the impact of problematic TikTok use on mental health in a more valid way.

This paper presents an economic feasibility analysis for constructing a 100 MW floating solar power plant in Korea, aimed at utilizing idle water spaces to mitigate geographical limitations for large-scale renewable energy projects. Employing both Discounted Cash Flow (DCF) analysis and Real Options Analysis (ROA), the study evaluates the economic viability and strategic flexibility of the project under future market uncertainties. The DCF method reveals an NPV of 11,863 thousand USD and a B/C ratio of 1.1, indicating baseline feasibility. However, the ROA demonstrates the value of adaptive strategies, with real option values showing that plant expansion (to 200 MW) is economically advantageous under favorable conditions. This approach supports strategic decision-making aligned with Korea’s renewable energy targets. The study emphasizes the importance of incorporating market uncertainties into feasibility assessments and proposes detailed strategies for expanding renewable energy generation.

This study introduces a novel calibration method for accurate external wrench measurement using a 6-axis FT sensor, designed to mimic human-like capabilities in robots. While current approaches for FT sensor calibration often rely on fixed parameters, our method models and calibrates essential parameters: bias, crosstalk, CoM, and inclination, to ensure reliable force measurements in mobile and inclined environments. A mobile manipulator installed with an FT sensor and a gripper is used to demonstrate calibration effectiveness across varying postures and inclined conditions, with non-linear optimization applied to minimize sensor-data errors. The proposed method addresses typical calibration challenges, including the effects of the end tool and base inclined, which are not commonly covered in existing methods. Results show that, on a non-inclined base, crosstalk and CoM calibration reduce average error by approximately 14% and variance by 38%. On an inclined base, our full calibration process reduces mean error by 42% and variance by 65%. These findings highlight the importance of inclined calibration for achieving accurate external force estimations, especially for mobile manipulator applications where the environment changes often.

Brazil harbors one of the world’s richest amphibian faunas, with over 1,000 described species exhibiting remarkable morphological diversity. This review synthesizes current knowledge of amphibian morphological variation across Brazilian biomes, analyzing patterns of phenotypic diversity in relation to habitat type, life history strategies, and phylogenetic relationships. Our analysis reveals distinct morphological adaptations associated with specific microhabitats within major biomes, from the arboreal specialists of the Atlantic Forest to the fossorial species of the Cerrado. We identify several morphological convergence patterns among distantly related taxa occupying similar ecological niches, particularly in traits related to locomotion and reproductive modes. The study highlights previously undocumented morphological variations in several species complexes and describes novel anatomical features in recently discovered taxa. These findings contribute to our understanding of amphibian evolution in megadiverse tropical regions and have important implications for conservation strategies targeting morphologically unique populations.

Current industrial robotic manipulators have made evident their lack of flexibility. The systems must know beforehand the piece and it’s position. To address this issue, contemporary approaches typically employ learning-based techniques, which rely on extensive amounts of data. To obtain vast data, an often sought tool is an extensive grasp dataset. This work introduces our Physics-Based Self-Supervised Grasp Pose Detection (PBSS-GPD) pipeline for model-based grasping point detection, useful to generate grasp pose datasets. Given a gripper-object pair, it samples grasping pose candidates using a modified version of GPD (implementing inner-grasps, CAD support...) and quantifies their quality using the MuJoCo physics engine and a grasp quality metric that takes into account the pose of the object over time. The system is optimised to run on CPU in headless-parallelised mode, with the option of running in a graphical interface or headless and storing videos of the process. The system has been validated obtaining grasping poses for a subset of Egad! objects using the Franka Panda two-finger gripper, compared with state of the art grasp generation pipelines and tested in a real scenario. While our system achieves similar accuracy compared to a contemporary approach, 84% on the real world validation, it has proven to be effective at generating grasps with good centering 18 times faster than the compared system.

Background/Objectives: Despite the widespread use of oral anticoagulants (OACs), acute ischemic stroke (AIS) remains a significant risk for patients with atrial fibrillation (AF). The real-world effectiveness of OACs in preventing recurrent strokes, particularly following an initial stroke of cardioembolic (CE) origin, continues to be a major challenge for clinicians managing AF patients. This study evaluates the efficacy of OACs in secondary stroke prevention and investigates the influence of anticoagulation type and quality on recurrence risk. Methods: We analyzed data from 128 AF patients in the prospective Transzlációs Idegtudományi Nemzeti Laboratórium (TINL) STROKE-registry, admitted with CE stroke between February 2023 and September 2024. Patients were categorized by anticoagulation status at admission (OAC-users, n = 89; anticoagulation-naïve, n = 39). Recurrence rates were assessed using logistic regression models, adjusted for age, sex, hypertension, diabetes, and pre-stroke disability. Subgroup analyses explored the effects of anticoagulation type and quality. Results: Recurrence rates were similar between OAC-treated and anticoagulation-naïve patients after adjusting for confounders (19.10% vs. 17.95%, p = 0.870). Among anticoagulated patients, neither anticoagulation type nor quality alone significantly influenced recurrence risk. However, their interaction was statistically significant (p = 0.049), suggesting that the effectiveness of anticoagulation in preventing strokes is strongly affected by treatment quality. Conclusions: Although OACs are a cornerstone of stroke prevention in patients with AF, their efficacy in reducing recurrence depends on optimal management. These findings highlight that adequate anticoagulation, not just its use, is critical to minimize recurrence risk. To effectively prevent strokes in high-risk AF patients, future strategies must focus on standardized protocols, tailored monitoring, and individualized dosing regimens.

Bulk flax and hemp oilseeds were evaluated under oven and vacuum heating temperatures con-cerning the percentage oil yield and energy demand under linear compression approach. The heating temperatures ranged from 40 oC to 90 oC at a constant drying time of 60 min. The oven pretreatment increased the oil yield of bulk flax oilseeds by 1.02% compared to the vacuum method. This increment resulted in a higher energy requirement of 23.06 J. The oil yield of bulk hemp oilseeds increased by 0.49 % resulting in a higher energy usage of 65.03 J. It was found that bulk hemp oilseeds produced a higher percentage oil yield of 23.86 ± 0.11% with a higher energy demand of 1641.66 ± 35.66 J than bulk flax oilseeds producing an oil yield of 17.52 ± 0.29 % and energy need of 1014.84 ± 52.26 J under both drying conditions. The established regression models for predicting the percentage oil yield and energy demand of the examined oilseeds dependent on heating temperature under both drying conditions were significant based on the P-value of the lack of fit which was greater than 0.05 indicating the reliability of the established regression models.

There is much confusion about the apparent opposition between Darwinian evolution and the second law of thermodynamics. Entropy and entropy production play a more important role in the origin of life and Darwinian evolution than is generally recognized. Here I argue that (i) Darwinian evolution is a product of the second law, in that life forms are far from equilibrium dissipative systems that have come into existence because of gradients in the universe, and that (ii) life enhances the 2nd law by increasing entropy production. Since the 2nd law involves entropy increase, we go beyond it with an hypothesis that involves the increase of entropy production. Gould proposed a wall of minimal complexity to explain an apparent increase in biological complexity. I show that a tendency for entropy production to increase offers a better explanation for the simultaneous increase of both biological complexity and simplicity. I review a simple universe-in-a-cup-of-coffee model and propose an alternative operational definition of complexity that it is proportional to entropy production. Finally, I sketch the cosmic history of entropy production and use it to evaluate the effectiveness of the hypothesis that there is a tendency for entropy production to increase.

Background: While the prevalence of obesity and its negative effects on graft steatosis in liver transplantation are well recognized, the impact of obesity in the absence of hepatic steatosis, fibrosis, and inflammation in deceased donor livers remains unclear. This knowledge gap may affect the decision to adopt a transplant strategy and influence the following outcomes. Methods: A donor population-based cohort study was performed on 35,529 donors who received liver biopsy from 1987 to 2024. Donor BMI was categorized and assessed for its association with liver conditions, including macrovesicular steatosis, microvesicular steatosis, liver fibrosis, and portal infiltrates. Multivariable logistic regression and restricted cubic spline (RCS) regression models were employed to explore both linear and nonlinear relationships between BMI and the specified liver conditions. Results: In a cohort of 35,529 donors, donor livers from higher BMI groups exhibited higher percentages of macro-steatosis, micro-steatosis,advanced fibrosis and portal infiltrate.Logistic regression indicated obesity as an independent predictor of liver histology assessment: for higher risk with moderate-servere macro-steatosis (OR 2.29, 95% CI 2.11–2.49, p < 0.001),moderate-servere micro-steatosis (OR 1.71, 95% CI 1.57–1.87, p < 0.001), portal infiltrate (OR 1.37, 95% CI 1.3–1.45, p < 0.001) and advanced fibrosis (OR 1.04, 95% CI 0.94–1.14, p < 0.001). Restricted cubic spline regression depicted J-shaped with moderate-servere macro-steatosis and portal infiltrate,a U-shaped advanced fibrosis, and a upside down U-shaped with moderate-servere micro-steatosis,P for for nonlinearity <0.0001),respectively. Subgroup analyses identified interactions with factors such as gender, hypertension, and hepatitis C, highlighting BMI’s complex influence on liver histology and reinforcing its role in liver donor evaluation. Conclusion: This donor population-based cohort study found a different association pattern between donor BMI and liver biopsy outcomes,integrating BMI into donor liver assessments could enhance decision-making during transplantation, potentially reducing organ discard rates and improving transplant success by identifying high-risk organs earlier.

Trunk-like robots have attracted a lot of attention in the community of researchers interested in the general field of bio-inspired soft robotics, because trunk-like soft arms may exhibit high dexterity and adaptability very similar to the elephants and potentially quite superior to traditional articulated manipulators. In view of practical applications, the integration of a soft hydrostatic segment with a hard-articulated segment, i.e. a hybrid kinematic structure similar to the elephant’s body, is probably the best design framework. It is proposed that this integration should occur at the conceptual/cognitive level before being implemented in specific soft technologies, including the related control paradigms. The proposed modeling approach is based on the Passive Motion Paradigm (PMP), originally conceived for addressing the degrees of freedom problem of highly-redundant, articulated structures. It is shown that this approach can be naturally extended from highly-redundant to hyper-redundant structures, including hybrid structures that include a hard and a soft component. The PMP model is force-based, not motion-based and is characterized by two main computational modules: the Jacobian matrix of the hybrid kinematic chain and a Compliance matrix that maps generalized force fields into coordinated gestures of the whole body-model. It is shown how the modulation of the compliance matrix can be used for the synergy formation process, that coordinates the hyper-redundant nature of the hybrid body-model and, at the same time, for the preparation of the trunk-tip in view of a stable physical interaction of the body with the environment, in agreement with the general impedance-control concept.

For studying of the finite-size behavior of the Ising model under different boundary conditions we propose an alternative to the standard transfer matrix technique approach based on Abel\`{e}s theorem and Chebyshev polynomials. Using it we easily reproduce the known for periodic boundary conditions results for Lee-Yang zeros, the exact position space renormalization group transformation, etc., and extend them deriving new results for antiperiodic boundary conditions. Note that in the latter case one has a nontrivial order-parameter profile, which we also calculate, where the average value of a given spin depends on the distance from the seam with opposite bond in the system. It is interesting to stress that under both boundary conditions, the one-dimensional case exhibits Schottky anomaly.

Sheep farming thrives in diverse climates, with the Awassi breed well-suited for arid environments, though it faces challenges that necessitate selective breeding and innovative management to improve productivity and weight performance. This study, conducted in 2023 at Al-Fjaj Station in Jordan, analyzed the weights of Awassi sheep managed under a semi-intensive system, examining 2,263 records to evaluate the environmental and genetic factors influencing growth and utilizing statistical models to estimate heritability and breeding values. Variance analysis revealed that birth type, lamb sex, parity, and ewe age at lambing significantly influence birth, weaning, 6-month, and annual weights, with single-born lambs generally heavier than twins, males surpassing females, and younger ewes producing lighter lambs at birth that catch up in weight later. Lamb weights are correlated positively across various stages, especially between weaning, six-month, and yearling weights, while the influence of birth weight on later weights is comparatively weaker. Analysis of Variance (Type 3) indicates that rams have the greatest influence on lamb weights at all ages, while ewes primarily affect early weight, and genetic factors play a moderate role in weight variation, with maternal effects being more significant than paternal influences. There are significant positive genetic correlations among lamb weight traits, indicating that improvement in one trait can benefit others, while mean breeding values decrease with age due to increasing environmental influences, suggesting the use of index selection to enhance multiple traits in breeding programs. High correlation coefficients between breeding values and mean weights of Awassi sheep indicate that higher breeding values for specific weight traits are associated with greater actual weights, demonstrating that selection based on breeding values can effectively enhance genetic potential and predict future performance. The genetic performance and growth traits of rams' progeny indicate that selecting rams with higher breeding values can enhance weight gain in future generations, while mean weights serve as performance benchmarks to help breeders improve flock genetics and profitability. In conclusion, this study identifies key factors influencing lamb weights, such as birth type, lamb sex, ewe parity, and ewe age, emphasizing the importance of single lambs and males in achieving higher growth, while suggesting that an index selection approach combining genetic merit with environmental factors is vital for optimizing sustainable lamb production in arid environments.

We studied the incentive-compatible selection mechanisms for directed forests. A directed forest is an acyclic directed graph with maximum out-degree equal to 1. A selection mechanism gives a probability distribution on each vertex in the forest. A selection mechanism is incentive-compatible (IC), if the probability assigned to a vertex does not change when it changes which it points to (or add, delete the edge). An IC mechanism is fair if the probability of a vertex is always larger then the one with less vertices with access to itself, and the probability of a vertex is always equal to the one with the same number of vertices with access to itself. An IC mechanism is exact if the sum of the probability of each vertex is always 1. The quality of a selection mechanism is the worst-case ratio between the expected number with access to each vertex and the maximum number with access to a vertex. The upper bound is decided by a special case and the lower bound is decided by a mechanism. In this paper, we focus on the local case for this question, i.e. $n = 2, 3$. We prove that for $n = 2$, the tight bound of quality for IC, fair and exact mechanisms are all $0.8$. For $n = 3$, IC mechanisms achieve a bound of quality $[7/9, 0.8]$. However, fair mechanisms achieve a bound of quality $[0.6, 0.8]$ and exact mechanisms achieve a tight bound $7/9$.

We calculate the second-order moments, the Robertson–Schrödinger uncertainty product and the Mandel factor for various superpositions of coherent phase states with opposite arguments, comparing the results with similar superpositions of the usual (Klauder–Glauber–Sudarshan) coherent states. We discover that the coordinate variance in the analog of even coherent states can show the most strong squeezing effect, close to the maximal possible squeezing for the given mean photon number. On the other hand, the Robertson–Schrödinger (RS) uncertainty product in superpositions of coherent phase states increases much slower (as function of the mean photon number) than in superpositions of the usual coherent states. A nontrivial behavior of the Mandel factor for small mean photon numbers is discovered in superpositions with unequal weights of two components. An exceptional nature of the even and odd superpositions is demonstrated.

Natural products play a crucial role in drug discovery, primarily due to their structural complexity. The prenylated flavanone, glabranin ((S)-5,7-dihydroxy-8-(3-methylbut-2-en-1-yl)-2-phenylchroman-4-one), isolated plant Dalea elegans, has demonstrated neuroprotective effects, attributed to its inhibition of GSK-3β as per our previous in silico studies. Given the enzyme's diverse functions and its potential as a target for neurodegenerative diseases, our group synthesized and evaluated an 11C-labeled derivative of glabranin. We present its in vitro biological activity, including IC50, neuronal uptake in Alzheimer’s-affected brain regions, key physicochemical properties, and animal studies. This study confirms [11C]FLA's interaction with GSK-3β in vitro, highlighting the potential of radiotracers in bioactive compound research.

The results of shaking table tests from previous studies on a one-story, two-bay reinforced concrete frame—exhibiting both shear and axial failures—were compared with nonlinear dynamic analyses using simplified models intended to evaluate the collapse potential of older reinforced concrete structures. To replicate the nonlinear behavior of columns, whether shear-critical or primarily flexure-dominant, a one-component beam model was applied. This model features a linear elastic element connected in series to a rigid-plastic, linearly hardening spring at each end, representing a concentrated plasticity component. To account for strength degradation through path-dependent plasticity, a negative slope model as degradation was implemented, linking points at both shear and axial failure. The shear failure points were determined through pushover analysis of shear-critical columns using the Phaethon software. Although the simplified model provided a reasonable approximation of the overall frame response and lateral strength degradation especially in terms of drift, its reduced computational demands led to some discrepancies between the calculated and measured shear forces and drifts during certain segments of the time-history response.

The synthetic aperture radar (SAR) mounted on unmanned aerial vehicles (UAVs) can efficiently and rapidly acquire high-resolution geographic remote sensing data. However, the limited payload capacity and power consumption of UAVs present a challenge for the application of traditional SAR real-time imaging processing platforms based on DSP or GPU. In this paper, a real-time processor that utilizes the Xilinx Zynq Ultrascale+ZU9EG MPSoC is proposed and successfully applied to a W-band Mini-SAR system. Several essential technologies, including SAR data preprocessing, high-energy-efficient real-time processing, and lightweight motion compensation, are utilized to develop the real-time processing Mini-SAR for use in small UAVs. The total weight of the entire system is only 724 grams, and its power consumption is 38.3 watts. During the UAV flight test, the system is capable of producing SAR images with a resolution of 0.1 meters at a size of 4K x 4K in just 4.5 seconds. The discrepancy between the imaging results and the ground post-processing is less than 10^-4.

Abstract

The morphological diversity of New World monkeys (Platyrrhini) across Brazilian biomes represents a remarkable example of adaptive radiation in Neotropical primates. This review synthesizes current knowledge about morphological adaptations and distribution patterns of platyrrhine primates across Brazil's major biomes, focusing on the relationship between form and function in different ecological contexts. Through analysis of recent geometric morphometric studies, ecological surveys, and comparative anatomical research, we identify several key patterns of morphological variation, including: a continuous gradient of postcranial adaptations rather than discrete locomotor categories, unexpected variations in temporal bone architecture across forest strata, and rapid morphological responses to local dietary conditions as evidenced by dental morphology. Our synthesis reveals that morphological adaptation in Brazilian platyrrhines involves complex trade-offs between competing functional demands, with different anatomical systems evolving at varying rates. The relationship between morphological specialization and ecological resilience emerges as particularly significant, with implications for conservation strategies in the face of habitat modification and climate change. We demonstrate that traditional views of morphological adaptation as a gradual process require revision, as populations show capacity for relatively rapid morphological responses to environmental changes. These findings have important implications for both evolutionary theory and conservation practice, suggesting the need for approaches that preserve not just species, but the full spectrum of morphological diversity and the ecological contexts that generate and maintain it.

IoT ecosystem extends beyond country borders and application domains, combining thousands of versatile devices that differ in terms of their structures, capabilities, and available resources. It is therefore not surprising that the landscape of wireless communication technologies and the degree of IoT devices available today is excessively broad and diverse and the reliability of LoRaWAN networks can be affected by factors such as collisions, interference, and varying signal strengths. Interference between networks leads to frame collisions and consequent packet loss. Frame colli-sions occur when two or more packets overlap in time and frequency and use the same Long Range (LoRa) parameters, i.e. the same Spreading Factor (SF), Bandwidth (BW) and Carrier Fre-quency (CF). When most devices use the same configuration, collision probability is higher. The probability of frame collisions is also affected by traffic characteristics, particularly the periodicity of the transmission and the payload size. Larger payload sizes and more frequent transmissions accumulate with higher Time on Air (ToA) and channel occupancy [1]. Transmission powers and the location of the gateways also influence this situation. The aim of this experimental work is to verify the effects of collisions and interference in Long Range Wide Area Network (LoRaWAN), focusing on how SFs and payload sizes influence signal transmission quality in an inter-SF interfe-rence scenario. This research is crucial for smart cities, where numerous IoT devices are deployed to monitor and manage urban infrastructure. Reliable and efficient communication networks are essential for the seamless operation of Urban IoT (UIoT) systems. In this context, the integration of IoT, along with other systems such as cyber-physical systems and cloud computing, necessitates robust and scalable communication networks to ensure efficient data transmission and network reliability. LoRaWAN, with its long-range and low-power capabilities, is already a widely adop-ted technology for connecting numerous devices and can be seamlessly integrated across large ur-ban environments. In such environments, inter-SF interference can significantly impact the per-formance of LoRaWAN networks. The choice of SFs and packet lengths directly affects ToA. When multiple devices operate near gateways, using different SFs and varying packet lengths can lead to increased latency and longer ToA. This, in turn, raises the risk of packet loss, especially in scenarios with a low signal-to-interference ratio (SIR). Such limitations pose challenges to the sca-lability of the network, particularly in Non-Line-Of-Sight (NLOS) situations, where obstacles like vegetation or buildings (or field walls) can further degrade signal quality. Our work validates me-asuring performance indicators like Received Signal Strength Indicator (RSSI), ToA, and Packet Delivery Ratio (PDR) to assess network reliability. By analyzing ToA in relation to RSSI and diffe-rent data sizes, and examining RSSI for different SFs and data sizes, we gain a comprehensive understanding of the network’s performance. Additionally, we evaluate packet loss through PDR to understand the reliability of packet transmission. These metrics are essential for optimizing the performance and reliability of LoRaWAN networks, which will impact various smart city applica-tions such as environmental monitoring, smart metering or smart buildings. We conclude that in an environment with inter-SF interference, the choice of SFs and packet len-gths impacts ToA, leading to packet loss in the NLOS scenario.

In this study, a diverse collection of images of myxozoans from the genera Henneguya and Myxobolus was created, providing a practical dataset for application in computer vision. Four versions of the YOLOv5 network were tested, achieving an average precision of 97.9%, a recall of 96.7%, and an F1 score of 97%, demonstrating the effectiveness of MLens in the automatic detection of these parasites. These results indicate that machine learning has the potential to make micro-parasite detection more efficient and less reliant on manual work in parasitology. The beta version of the MLens shows strong performance, and future improvements may include fine-tuning the WebApp hyperparameters, expanding to other myxosporean genera, and refining the model to handle more complex optical microscopy scenarios. This work represents a significant ad-vancement, opening new possibilities for the application of machine learning in parasitology and substantially accelerating parasite detection.

The mineral composition of monovarietal red wines from the Canary Islands was analyzed to evaluate the potential of mineral content as a marker for wine authenticity by geographical origin. Key minerals—K, Na, Mg, Mn, Fe, Cu, and Co—were quantified in 190 wine samples using flame absorption spectrometry. The study revealed slight mineral profile differences between recently introduced international grape cultivars and traditional ungrafted varieties. A significant correlation was found between K and Mg, highlighting their roles in vine physiology. Results indicated that Tenerife wines had elevated Fe and Mn, Lanzarote wines showed higher Na (likely from marine aerosols), and La Gomera wines had significantly high Mn. Linear Discriminant Analysis demonstrated that Mn, Mg, and Na differentiated wines by island, with 85% classification accuracy, while Cu and Fe correlated with wine aging. These findings emphasize the influence of volcanic soils and microclimate on mineral profiles, supporting mineral analysis as a cost-effective tool for classifying red wines by origin. This study offers insights into how terroir, grape cultivar, and winemaking practices define the unique characteristics of Canary Island wines.

The core principle of investment posits a direct correlation between risk and return. In the language of finance, "risk" refers to the degree of uncertainty or the likelihood of financial loss associated with an investment choice. Conventional wisdom holds that as investment risk escalates, investors expect higher returns as compensation for the increased risk they undertake. Yet, the interplay between risk and return is not always linear or predictable. This study utilized optimization modeling in the options market to investigate the risk-return relationship, focusing on how returns change with different risk levels. Concentrating on the short iron condor strategy, with its capped risk and return, the research aimed to maximize returns within various loss scenarios. This method clarified return levels at different risk thresholds and emphasized the short iron condor strategy's role in delineating the risk-return dynamics. Using 2023 data, research on the 14 most traded US equity and 9 ETF options, with maturities ranging from 5 to 20 days, identified a clear relationship between risk, measured by both maximum loss levels and price fluctuations, and potential returns in short iron condor strategies. While higher maximum loss limits led to larger potential returns, the risk-adjusted returns, expressed as the return-to-risk ratio, declined as risk increased. However, with increased price fluctuations, the potential return actually decreased due to the capped profit potential of the strategy, showing a different dynamic compared to maximum loss risk. Furthermore, longer time to maturity further reduced this ratio, highlighting that both the level of risk and the duration of the position play critical roles in determining returns. These findings emphasize the complex balance between risk and return, and the diminishing marginal benefits of increasing risk and holding periods in short iron condor strategies.

The sigma-1 receptor (Sig-1R) has emerged as a significant target in the realm of pain management and has been the subject of extensive research. Nonetheless, its specific function in inflammatory pain within dorsal root ganglion (DRG) neurons remains inadequately elucidated. This study utilized whole-cell patch clamp techniques, single-cell real-time PCR, and immunohistochemistry to examine the influence of Sig-1R on inflammatory pain induced by complete Freund's adjuvant (CFA) in a rat model. Our results revealed several key findings: 1) The expression of Sig-1R was found to be upregulated during the progression of inflammatory pain, with a notable translocation from the cytoplasm to the membrane; 2) Inhibition of peripheral Sig-1R using S1RA resulted in a reduction of CFA-induced allodynia; 3) Activation of Sig-1R through PRE-084 led to a decrease in the fast sodium current in isolated DRG neurons from CFA-treated rats, which was associated with a diminished action potential (AP) peak and maximum depolarizing rate (MDR), as well as an increased rheobase; 4) Furthermore, PRE-084 was observed to enhance the slow component of the sodium current, resulting in hyperpolarization of the threshold potential and an increase in AP firing frequency, alongside an elevation in the mRNA expression of the slow sodium channel Nav1.9 in CFA-treated rats. In conclusion, our findings suggest that the modulation of sodium channels by Sig-1R in DRG neurons plays a significant role in the mechanisms underlying inflammatory pain.

Background and Objectives: Low-density lipoprotein cholesterol (LDL-C) is a marker of cardiovascular risk and its management. This study evaluated LDL-C control trends in patients treated at a single healthcare centre in Lithuania. Materials and Methods: The study was conducted at the primary healthcare centre Saulės šeimos medicinos centras. Five-year (2019-2023) data on patients aged 40 years or older diagnosed with dyslipidaemia were extracted from a real-world data and analytics platform TriNetX. For analysis, patients were grouped into three groups: patients with dyslipidaemia only (control group), patients with dyslipidaemia and diabetes, and patients with dyslipidaemia and cardiovascular disease (CVD). The following LDL-C goals were used for analysis: &lt; 1.4 mmol/L (a goal for very-high-risk patients in primary or secondary prevention), &lt; 1.8 mmol/L (a goal for high-risk patients), and &lt; 3.0 mmol/L (a goal for low-risk patients). Results: There were 18646 patients with dyslipidaemia. Of them, 8.9% of patients had diabetes and 3.1% of patients had CVD. The median LDL-C concentration was significantly lower in patients with diabetes (2.82 mmol/L, p &lt; 0.05) and in patients with CVD (2.45 mmol/L, p &lt; 0.05) than in the control group (3.35 mmol/L). A trend of decreasing median LDL-C over the years was observed in all groups, with the lowest median values in 2023. The proportion of patients with LDL-C level &lt; 3 mmol/L increased from 32.0% in 2019 to 41.5% in 2023. The proportion of diabetic patients achieving LDL-C &lt; 1.8 mmol/L increased from 7.4% to 25.9% and those achieving LDL-C &lt; 1.4 mmol/L increased from 3.1% to 10.6%. The proportion of patients with CVD achieving LDL-C &lt; 1.8 mmol/L increased from 14.2% to 36.6% and those achieving LDL-C &lt; 1.4 mmol/L increased from 3.0% to 14.0%. Conclusions: Trends in the control of LDL-C levels are positive over 5 years, but a significant proportion of patients still did not reach the recommended target levels.

Cybersecurity threats pose a significant challenge to the operational resilience and sustainability of Small and Medium Enterprises (SMEs) globally, with distinct implications for regional business ecosystems. This research presents an in-depth investigation into the impact of cybersecurity threats on SMEs within the unique context of Kajiado County, Kenya. Against the backdrop of an increasingly digitized business environment, this study aims to comprehensively assess the challenges SMEs face in mitigating cyber risks and their consequent impact on performance. The research employed a mixed methods approach, combining quantitative and qualitative analyses to elucidate the multifaceted nature of cybersecurity challenges encountered by SMEs in Kajiado County. Utilizing interviews, and document analysis, the study gathered empirical data to identify prevalent cybersecurity threats, their frequency, and the subsequent repercussions on SMEs' operational efficiency, financial stability, and overall performance metrics. Initial findings reveal a diverse spectrum of cybersecurity threats SMEs face in Kajiado County, ranging from phishing attacks and data breaches to ransomware incidents. These threats significantly impede business operations, resulting in financial losses, service delivery disruptions, and customer trust erosion. Moreover, the study examined the cybersecurity measures adopted by SMEs, highlighting the challenges in implementing effective preventive strategies within resource-constrained environments. Notably, the research addresses the identified gap by offering a nuanced understanding of the interplay between cybersecurity threats and SME performance in Kajiado County. The study intends to propose tailored and pragmatic recommendations to fortify SMEs' cybersecurity resilience by contextualizing global best practices within the regional business landscape. The anticipated outcomes of this research endeavor extend beyond academic discourse, aiming to provide actionable insights to SME stakeholders, policymakers, and industry practitioners. The findings aspire to empower SMEs in Kajiado County to enhance their cybersecurity posture, enabling them to navigate the evolving threat landscape more effectively and bolster their long-term sustainability within the digital economy.

This longitudinal qualitative study examines the impact of the Invest in Girls (IIG) financial literacy program on young women's financial knowledge, behavior, and skills, with implications for their long-term economic sustainability. Using data from 98 interviews conducted over four years with program alumnae, the study reveals a progression in financial literacy skills as participants transition from high school through college. Key findings include the evolving importance of budgeting skills, a progression from basic financial concepts to complex real-world applications, and a persistent desire for enhanced tax education. The study highlights the importance of adapting financial literacy programs to meet the evolving needs of young women. These insights contribute to our understanding of how targeted financial education can better prepare young women for long-term economic sustainability in an increasingly complex financial landscape, potentially addressing broader issues of gender inequality in finance.

This paper examines Instagram's increasing influence on the hotel industry, an area that remains under-researched from both consumer and hotel perspectives. Through an explanatory sequential design, combining a quantitative analysis of 1,031 posts with qualitative interviews from hotel professionals, the research uncovers the types of content that resonate most with users and how hoteliers strategically leverage Instagram. The findings reveal which content formats, hotel attributes, and post characteristics capture audience attention. Additionally, the study offers unique insights into Instagram’s distinct role compared to other social media platforms in the hotel industry. As the first study to define what makes a hotel "instagrammable" from both consumer and industry viewpoints, it provides valuable insights into how hotels can strategically harness Instagram to attract and engage their audience.

Globally, the importance of sustainable forest is increasing. The forest culture (FC) products are consumed from the perspective of value consumption unlike general products. The benefits contributed by FC products differ in terms of their impact on individuals and society. In this study, we investigated how the expected benefit of FC affects pursued values (PVs) and preferred activities (PAs) from the perspective of value consumption. We (1) determined the indicators of the expected benefit of PVs and PAs in FC and (2) examined the causal relationship among these factors using Means-End Chain theory. We also reviewed relevant literature and surveyed 1,700 Koreans to explore how FC benefits, values, and activities relate to consumer behavior. Factor analysis divided 11 and 19 indicators of PVs and PAs of FC, respectively, into three factors: “symbolic, social, and consumption value” and “tourism-exploration, cultural-artistic, and living-leisure activity.” The contributing characteristics of FC affect the PVs and, in turn, the PAs of FC. In other words, the concrete attitudes of consumers toward FC were categorized by abstract concepts and influenced their practical and behavioral attitudes. In conclusion, FC products should be developed with a focus on value consumption.

Pubertal gynecomastia (PG) refers to the abnormal development and hyperplasia of unilateral or bilateral breast tissue in adolescent males, a common condition with a prevalence of up to 50% among appropriately aged adolescents, and an increasing incidence trend over the years. The etiology of PG is multifactorial, including physiological, pharmacological, and pathological causes. Endocrine hormones play a crucial role in the pathogenesis of PG across different etiologies. This review provides a detailed discussion on the effects of various endocrine hormones, including FSH, LH, T, E2, PRL, GH, and thyroid hormones, on pubertal gynecomastia in adolescent males, aiming to offer valuable insights for clinical diagnosis and treatment.

Background: Predicting whether extracorporeal membrane oxygenation (ECMO) treatment duration affects prognosis is important both medically and economically. We conducted a retrospective, multicenter study to better understand the outcomes of patients treated with VV ECMO for a prolonged duration, analyzing data from the Israel ECMO registry. The study included all adult patients treated with VV-ECMO due to COVID-19-induced respiratory failure. The primary outcomes were survival rates up to 180 days from cannulation. Results: One hundred and eighty-eight patients were included in the study. The median age was 50 years (IQR 42, 50) and 69% were male. Patients were mechanically ventilated for a median of 2.5 days before cannulation (IQR 0.5, 5). The mean ECMO support duration was 29.9 days, with a maximal duration of 189.9 days. Survival rate for 180 days was 56%. We found no change in survival for patients on ECMO for 14, 28, or 56 days. Every day of mechanical ventilation before cannulation correlated with an 11% greater risk for prolonged ECMO treatment (p=0.01). Conclusions: For COVID-19-induced ARDS patients treated with VV-ECMO, ECMO treatment duration did not affect mortality. The longer the duration of mechanical ventilation before ECMO cannulation, the higher the risk for prolonged ECMO treatment.

In this study, molecular dynamics simulations were employed to analyze interactions between phospholipid membranes and human serum albumin (HSA) in the presence of mono- and divalent cations. Two types of membranes, composed of dipalmitoyl phosphatidylcholine (DPPC) and dipalmitoyl phosphatidylethanolamine (DPPE), were utilized. The results revealed that both systems exhibited high stability, with the DPPE complexes displaying greater stability compared to those formed with DPPC. This increased stability was attributed to a higher number of ionic contacts and hydrogen bonds. The presence of mono- and divalent metal cations significantly influenced the membrane’s capacity to bind proteins. However, these effects varied depending on the phospholipid composition of the bilayer. The studies confirmed the relatively low ability of DPPC to bind potassium ions, as previously observed by others. Consequently, the DPPC/HSA/K+ complex was found to be the least stable among the systems studied. While DPPC interactions were limited to HSA domains I and II, DPPE was able to interact with all domains of the protein. Both lipid bilayers exhibited substantial structural changes and characteristic curvature induced by interactions with HSA, which confirms the formation of relatively strong interactions capable of influencing the arrangement of the phospholipids.

This study investigates the healing action of topical formulations based on Libidibia ferrea fruits in cutaneous wounds induced in Wistar rats. Three types of preparations were evaluated: glycolic extract, infusion, and gel. The phytochemical compounds present in the preparations, such as tannins and flavonoids, are known for their antioxidant, anti-inflammatory, and antimicrobial properties, which are essential for the healing process, histopathological methods evaluated healing, including analysis of epithelialization, granulation tissue formation, and collagen deposition. The results demonstrated that the preparations accelerated the healing process, with a more significant deposition of type I collagen in the groups treated with infusion and glycolic extract, suggesting a more advanced tissue repair process. The infusion demonstrated the best healing results, possibly due to the higher flavonoid content. The findings reinforce the therapeutic potential of Libidibia ferrea in the development of natural healing agents.

Hydrogen production by water splitting is one of the low-cost green hydrogen production technologies. The challenge is to develop inexpensive and highly active catalysts. Herein, we present the preparation of electrocatalysts based on cobalt-phosphorus (Co-P) coatings with different P contents for hydrogen and oxygen evolution reactions (HER and OER). The Co-P coatings were deposited on the copper (Cu) surface using the inexpensive and simple method of electroless metal deposition. The morphology, structure, and composition of the Co-P coatings deposited on the Cu surface were studied by scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDX), while their activity for HER and OER in 1 M KOH was investigated by linear sweep voltammetry (LSVs) and chrono-techniques. It was found that the catalyst activity for both HER and OER depends on the P content of the catalyst and varies to the highest efficiency for each reaction. The Co-P coating with the 11 wt% of P exhibited the lowest overpotential value of –115.4 mV for the HER to obtain a current density of 10 mA cm-2 compared to the Co-P coatings with 8 wt% (–121.5 mV) and 5 wt% (–182.9 mV) of P. In contrast, the lowest OER overpotential (394 mV) was observed for the Co-P coating with the 8 wt% of P to obtain a current density of 10 mA cm-2 as compared to the Co-P coatings with 5 wt% (416 mV) and 11 wt% (432 mV) of P. These results suggest that the obtained catalysts are suitable for HER and OER in alkaline media.

Temperature tolerance is crucial in aquaculture, as it greatly affects growth, survival, and overall performance. Traditional methods of improving temperature tolerance are often limited by low heritability estimates and the challenge of precisely assessing the trait. This study used low-coverage whole-genome sequencing (lcWGS) with genotype imputation to identify genetic markers linked to cold stress in Nile tilapia. Fish exposed to cold conditions at two sites in Egypt produced a dataset of 7,300,252 filtered SNPs. Out of these, 10 SNPs located in an intronic region of a noncoding RNA (lncRNA) on LG16 were strongly linked to cold stress. Further investigation suggested that these SNPs alter micro-RNA (miRNA) binding sites, potentially affecting interactions with miRNAs miR-133b and miR-139, both of which are associated with stress response and cold adaptation. Validating these markers across various fish populations could support efforts to improve cold tolerance in tilapia through selective breeding.

It is desirable to upgrade previous evolutionary theories, which have remained incomplete and controversial for decades. Here we employ the concept of carbon-based entities (CBEs), which include methane, amino acids, proteins, organisms, and other entities containing relatively many carbon atoms. We deduce the driving force, mechanisms, steps, modes, tempos of CBE evolution, through integration of biology, physics, and chemistry using logics for complex issues. We hence establish the Carbon-Based Evolutionary Theory (CBET). The CBET suggests that evolution is the increase in hierarchy, diversity, fitness of CBEs under natural selection and driven by thermodynamics due to the chemical effect of the thermodynamic features of the Earth on CBEs. It provides better explanations for life origin, macroevolution events, natural selection, sympatric speciation, and evolution tempos than previous evolutionary theories. It reveals the evolutionary basis of multiple important social notions, including diversity, collaboration, altruism, obeying rules, and proper increase in freedom. It refutes some wrong notions in thermodynamics, including negative entropy (negentropy) and that biological order is equal to thermodynamic order, which have misled many people. The CBET is supported by its deduction and application. It could be a rare bridge linking laws of thermodynamics, evolution of life, and development of human society, and could have great significance in various sciences.

Cereals are essential for global food consumption, providing half of the world’s calorie intake and ranking as the top traded agricultural commodity. By 2032, 41% of cereals are expected to be consumed directly by humans, with 37% used as animal feed. As key sources of carbohydrates, protein, minerals, vitamins, and fiber, cereals deliver balanced nutrients for humans and livestock. Rice and wheat are the most widely consumed, yet over 3 billion people still suffer from micronutrient deficiencies, particularly iron, zinc, and vitamin A, leading to widespread malnutrition affecting over 2 billion globally. Nutrient deficiencies contribute significantly to childhood mortality, with undernutrition causing 45% of deaths in children under five. Cereals naturally contain essential micronutrients, including iron, zinc, and B vitamins, along with fiber that supports cardiovascular health and reduces cancer risks. While the growth rate of cereal consumption is slowing, it remains steady, especially in developing countries, where cereals provide 56% of daily caloric intake. To address malnutrition, biofortifying cereals to enhance iron, zinc, and vitamin A content could be transformative. This review highlights the nutritional importance of cereals, the need for balanced diets, and how biofortification and genetic editing can combat global malnutrition.

Effective management of white oak stand density is essential for maintaining biodiversity and ecosystem functions. This study examines the relationship between the stand density of declining white oaks and soil characteristics. Multicycle Forest Inventory data were aggregated to identify white oak plots throughout the eastern U.S. Stand density was calculated across plot systems that utilized basal area metrics across various states. Key soil variables were analyzed to assess their influence on stand density. The regression analyses revealed that these soil textures, including soil clay loam, fine sandy loam, and loamy fine sand had a significant negative impact on the stand density of declining white oaks (p ≤ 0.05), while both high and low extremes of TAW were positively associated with stand density. These findings underscore the critical role of soil properties in influencing the stand density of declining white oak populations. Insights from this study can support forest managers in assessing white oak stand conditions on a regional scale. Future work should expand on soil variables, integrating biotic and abiotic factors for a comprehensive approach to white oak conservation across the eastern U.S.

The prospective studies applied to the plantain sector are extensive, but they are not directly related to innovation models. Therefore, this article proposes an Innovation Management Model for this chain and, specifically, for the one located in the department of Quindío-Colombia. The methodology employed combines stakeholder engagement, prioritization of variables, a comprehensive literature review, and model validation. The study identifies critical variables essential for fostering agricultural and technological innovation across platforms and projects, enhancing capacity development, and promoting research, development, and innovation (R&amp;D&amp;I) activities. Furthermore, it examines supply chain dynamics, innovation management processes, knowledge and technology integration, and the adaptation of business models for sustainable development, alongside environmental impact assessments. Initially, 53 variables were identified in the input stage, 36 in the transformation component, and 31 in the output stage. The analysis of priority variables revealed significant gaps in leadership and strategic capacity, resource availability, agricultural innovation, market competitiveness, information and communication technologies, regulatory compliance, and network resilience. These gaps profoundly affect creativity, innovation, and sustainability within Colombia's plantain sector.

Abstract: Background and Objectives: Phyllodes tumors are rare breast neoplasms with limited therapeutic options and poorly understood molecular characteristics. This study aimed to analyze genomic alterations and treatment outcomes in advanced phyllodes tumors using Japan's national clinical genomic testing registry (C-CAT database) to identify potential therapeutic targets and predictive markers. Materials and Methods: We conducted a retrospective analysis of 60 phyllodes tumor cases from 80,329 patients registered in the C-CAT database between June 2019 and August 2024. Comprehensive genomic profiling was performed using multiple platforms including FoundationOne CDx, NCC OncoPanel, and other approved tests. Treatment responses were evaluated according to RECIST criteria, and pathogenic variants were assessed using established databases including ClinVar and OncoKB. Results: The cohort's median age was 54 years (range: 13-79), with TERT promoter variants (70%), MED12 (52%), and TP53 (50%) mutations being the most frequent alterations. Forty patients received first-line chemotherapy, predominantly an-thracycline-based regimens (n=29). Although not reaching statistical significance, cases with CDKN2A and TERT alterations showed trends toward treatment resistance (OR>3.0). One patient with high tumor mutational burden (37/Mb) responded to pembrolizumab. Potential germline variants were identified in two cases (3.3%), involving MSH6 and TP53 alterations. Notably, no cases with CDKN2B alterations demonstrated treatment response (p=0.09). Conclusions: Our findings suggest distinct molecular patterns in phyllodes tumors compared to other soft tissue sarcomas, with potential implications for treatment selection. The identification of specific genetic alterations associated with treatment resistance may guide therapeutic decision-making, while the presence of actionable mutations in select cases indicates potential opportunities for targeted therapy approaches.

Background/Objectives: Research on calcitonin gene-related peptide (CGRP) in adult migraine is extensive, but its role in childhood migraine remains unclear. This study aimed to evaluate serum CGRP levels in children experiencing migraine and tension-type headache (TTH) during interictal periods, comparing these levels to age-matched healthy controls. Methods: A total of 66 migraine patients, 59 with TTH, and 53 controls were recruited and strat-ified by headache onset age: under 7, 7-12, and over 12 years. CGRP levels were quantified using enzyme-linked immunosorbent assay (ELISA). Results: Migraine patients showed significantly higher serum CGRP levels than both TTH pa-tients and controls (p < 0.001) with no significant difference between the latter two groups. Among migraine patients, those without aura (MO) exhibited higher CGRP levels than those with aura (MA). CGRP levels were lower in MA patients whose headaches began between ages 7-12 compared to subjects with MO, while no significant differences were found in patients whose headaches began after age 12. Conclusions: These findings suggest elevated serum CGRP is indicative of pediatric migraine, with variations based on migraine type and age of onset. The difference in CGRP in preadolescent migraineurs with and without aura suggest that CGRP levels may vary depending on the age and on migraine type.

Based on productivity and efficiency indicators, we investigated the performance of the Brazilian oil and gas exploration industry, comparing the performance of this sector with other industrial sectors. We associate productivity with the concept of total factor productivity (TFP), while efficiency is measured using the stochastic frontier production model. Our sample was assembled from the Annual Industrial Survey (PIA) for 29 Brazilian industrial sectors from 2007 to 2019. The results derived from both methods allow us to affirm that the policies resulting from the Pre-Salt have significantly boosted the oil and natural gas extraction sector in terms of technological progress and efficiency. Between 2007 and 2009, the sector was among the least efficient, ranking 29th. However, in 2019 it reached first place in terms of efficiency. This structural change, which began in 2010 as a result of the technological innovations resulting from investments in R&D, has undergone a change since 2010, reflected in the upward trend towards pre-salt exploration promoted by Petrobras, as well as the new regulatory framework and government incentives for oil exploration in Brazil. Unfortunately, these productivity gains have not been exported to other branches of industry connected to the oil industry.

Background: Sodium-glucose cotransporter-2 inhibitors (SGLT2i) are known for efficacy in managing blood sugar levels in type 2 diabetes and also improving cardiovascular and renal outcomes. However, increased risk of urogenital infections (UGIs) remains a significant concern in their safety profile. This study aimed to investigate the association between SGLT2i therapy and UGIs in a real-world clinical set up of a tertiary care centre in India. The key hypothesis was SGLT2i use is associated with incidence of UGIs while the secondary hypothesis included the relationship of the incidence of risk with the dose. Methods: This was a six-month prospective observational study, that enrolled 309 adults ≥18 years of age, who were newly prescribed SGLT2 inhibitors, particularly Dapagliflozin 5 mg and 10 mg from October 2023. The incidence of UGIs was assessed through patient interviews and medical records during the follow up from 2 weeks of initiation. UGIs were defined as clinical manifestations that resolved on SGLT2i discontinuation or appropriate treatment. Results: Of 309 initiators, 300 patients were followed up. 9 patients didn’t follow up in OPD. From these, 58 (19.3%)patients developed UGIs, comprising of 47 (15.6%) bacterial infections and 11 (3.6%) UGIs of fungal origin. The association was performed on basis of logistic regression analysis which revealed a statistically significant association of UGIs with the prescription of SGLT2i. Higher doses of SGLT2 inhibitors was observed to be associated with significantly increasing the risk of UGIs (p < 0.05). No significant risk factors such as body mass index (BMI), age, sex, HbA1c, or educational status were identified to have significant associations with UGI occurrence. Conclusion: This study highlights a clinically relevant association between SGLT2i use and urogenital infections.

Milk analysis is critical to determine its intrinsic quality, as well as its nutritional and economic value. Currently, the advancements and utilization of spectroscopy-based techniques combined with machine learning algorithms have made feasible the development of analytical tools and re-al-time monitoring and prediction systems in the dairy ruminant sector. The objectives of the cur-rent review were i) to describe the most widely applied spectroscopy-based and supervised ma-chine learning methods utilized for the evaluation of milk components, origin, technological properties, adulterants, and drugs residues, ii) to present and compare the performance and adaptability of these methods and their most efficient combinations, providing insights into the strengths, weaknesses, opportunities, and challenges of the most promising ones regarding the capacity to be applied in milk quality monitoring systems both at the point-of-care and beyond, and iii) to discuss their applicability and future perspectives for the integration of these methods in milk data analysis and decision support systems across the milk value-chain.

We have investigated the individual and combined effects of temperature and light on the physi-ology of the diatom Phaeodactylum tricornutum, a model organism widely used for studies on diatom physiology and ecology and biofuel production. Our results show that the combination of changes in temperature and light intensity affects growth rates, the content of pigments and active photosystems, photosynthetic efficiency, lipid production and fatty acid composition of P. tricornutum. Measurements of the maximum electron transport rate (rETRmax) and rETR at maximum PAR, confirmed that in P. tricornutum light sensitivity is substantially higher as the growth temperature increases under light/dark cycles of two light intensities (25-60 µmol m-2 s-1) but it is just the opposite under continuous light (25 µmol m-2 s-1). Furthermore, higher rETRmax were observed at higher ir-radiance (either in intensity or in a continuous light time regime) at the two temperatures tested. On the other hand, increasing light intensity amplifies the observed effect of temperature on photo-system I (PSI) activity in light/dark regimes, but not in continuous light conditions, leading to a greater deficiency in PSI activity, due to limitations in the electron supply to this photosystem. Moreover, the change in the culture temperature from 20°C to 25°C triggers an increase in the number and size of cytoplasmic lipid droplets under conditions of increased light intensity, but even more drastically under continuous illumination. It should be noted that the combination of a temperature of 25°C and continuous illumination in P. tricornutum cell cultures causes a drastic increase of triacylglycerides, and a change in the composition of total fatty acids that is optimal for a possible use as biodiesel.

This study investigates the preparation of KNN-ZnO composites through optimized sintering conditions under both inert and oxygen atmospheres. Initial experiments employing inert hot pressing were hindered by the necessity for annealing, which resulted in cracks due to the differing thermal expansion coefficients of KNN and ZnO, rendering this method unsuitable for composite fabrication. Consequently, sintering under an oxygen atmosphere was adopted, yielding a uniform distribution of ZnO particles within the KNN matrix. The resultant microstructure demonstrated significant densification, enhanced homogeneity, and minimal porosity. Electrical performance evaluations revealed that while the incorporation of ZnO led to a slight reduction in the ferroelectric properties (d₃₃ and polarization) of the KNN matrix, it concurrently improved the electromechanical quality factor (Q_m) by 40%. However, the overall enhancements were less pronounced when compared to analogous BNT-BT systems, underscoring the necessity for further optimization of processing conditions to boost electromechanical efficiency and piezoelectric response in KNN-based ceramics.

Abstract Micropaleontology is the study of microscopic fossils such as coccolithophores, foraminifera, diatoms, and radiolaria. These diminutive fossils range in size from 0.001 to 1 mm and are the most abundant and ancient of Earth’s unicellular organisms. I now report the finding of a ~0.5 – 0.6 mm long limbed squamate with a spade-like tail and features that otherwise resemble those of extant Geckos on the dorsal surface of a trace fossil from Hamblen County, Tennessee. Also found are three 0.4 - 0.6 mm long saurian-like juveniles undergoing body elongation and limb reduction, findings in keeping with a lizard-to-snake transformation. The dorsal surface also contains the remains of a tiny snake-like squamate measuring 1.0 mm snout-to-vent and 7.0 mm snout-to-tail. The miniaturization of these fossils may represent an ecomorphological response to the Permian-Triassic and/or the mid-Cretaceous extinctions as well as the metabolic and reproductive advantages of smallness. The findings open a new area of investigation for paleontologists and micropaleontologists and underscore the resilience and variety of life forms on Earth.

Medicine is struggling with the constantly rising incidence of breast cancer. The key in this fight is to be able to speed up diagnosis, as rapid diagnosis reduces the number of aggressive or advanced cases. For this process to be effective, it is necessary to have the right attitude towards diagnosis as a research practice. Our critical analysis of diagnosis, as a methodology of medical science, reflects on it as a research practice that is regulated in a socio-subjective way by a methodological culture. This position allows us to contrast critical methodological culture with the habitual-practical, or methodical, culture of practising diagnosis. We point to the interpretative status of medical analyses performed by medical historians by referring to Italian Renaissance painting and historical-artistic interpretations. In this field, analysing disputes between researchers as a clash of methodologies in the ways interpretation transforms signs into meanings, is a critical methodological reflection. Medicine is a diverse scientific discourse with a paradigmatic structure in which new ways of conducting diagnostic tests may appear. It is only possible to see this from the methodological level. In addition, passive respect for existing patterns of conduct hinders an exchange of views between researchers, what limits the possibility of correcting research procedures. The ultimate consequence of such passivity is an inabili/ty to improve diagnosis, which in turn, harms the interests of patients. In this regard, it is worth remembering that the paramount objective of diagnosis is not the disease, but the patient.

Background/Objectives: To examine the trend of treatment zone (TZ) decentration over 12 months of Orthokeratology (OK) wear using two corneal refractive therapy (CRT) lens designs: standard (STD) and dual axis (DA). Methods: A prospective, randomized, longitudinal study was conducted at the Optometry Clinic of the Complutense University of Madrid. Subjects were fitted with STD or DA designs in one of the eyes, randomly. Refraction, uncorrected visual acuity (VA) and corneal topography were performed at baseline, 1-night, 1-week, 1-, 3-, 6- and 12-month of lens wear. Subjects requiring lens parameter adjustments or replacements after 3 months were excluded. Decentration was measured by subtracting pre-OK from post-OK tangential curvature maps at each visit, with decentration distance and corneal optical TZ measured using MATLAB. Correlations between decentration and visual acuity (VA) were also analyzed. Results: Thirty healthy children completed all the visits (17 female and 13 male) with a mean age of 12.83±2.42 years (range: 8–17 years). No statistically significant differences (p&gt;0.05) were found either in the horizontal, vertical nor total decentration through the visits between lens designs. However, for STD design horizontal and total decentration increased significantly at the last visit (p&lt;0.05). For DA design, no significant differences were found along time (p&gt;0.05). No correlation was found between decentration and VA. Conclusions: Total decentration in both lens designs was similar throughout one year of follow-up. The standard design tended to decenter horizontally during the last 6 months, while the dual-axis design maintained consistent decentration throughout the year.

Stakeholders and their dynamics are often neglected in innovation systems literature. The importance of bioeconomy is growing for its implications on addressing environmental challenges, shaping economic decisions, markets and sustainable development. This paper analyses stakeholders’ dynamics for knowledge creation and innovation to transit from unsustainable practices to the sustainable use of biological resources - bioeconomy. The originality of this paper is the creation of an analytical framework to characterise the interactions of stakeholders and how these interactions reshape the innovation systems to create a new narrative and a new knowledge base platform for innovation. Using a qualitative approach, data was collected through surveys between 2022 and 2024. We explored the dynamics of 29 stakeholders involved and collaborating in R&amp;D activities from the biotechnology sector in Caldas, Colombia. Our findings show that dynamics towards bioeconomy are only at the discursive level. Stakeholders carry out research activities as a means to generate income rather than for innovative purposes, overlooking informal interactions which generate novel ideas that could translate into solutions, services, and products. We conclude that bioeconomy transition needs a systemic disequilibrium by a new institutional infrastructure that enables stakeholders, including civil society, to create a structural change for embracing innovation dynamics.

The study offers an in-depth investigation of the impact of climate conditions on various terrain features in the Kaffiøyra area, southwest Svalbard, focusing on the changes of shorelines, glacier termini, and outwash areas. We utilized cost-effective, consistent remote sensing data and GIS techniques to analyze historical long-term changes in these terrain features, evaluated the effects of temperature variations on glacier retreat, and developed a predictive model for future glacier area changes based on temperature data from climate change scenarios and the ground observation station near the site. The results demonstrated a range of responses in shoreline stability and changes across different zones in the study site. Based on the predefined baseline, the glacier-shorelines (tidewater glacier) exhibit significant erosion, with the averaged rates varying from -64.7 to + 9.2 m/yr. However, the land-shorelines show relatively stable, with changes ranging from -2.2 to +3.7 m/yr, and the outwash areas exhibited minor increases of less than 10% as compared to the data obtained in 1985. The analysis of seven glacier termini indicated a general reduction in the glacier area; notably, Aavatsmarkbreen, a tidewater glacier, retained only 32% of its original size by 2023. The land glaciers such as Waldemarbreen, Irenebreen, Elisebreen, Eivindbreen, Andreasbreen, and Oliverbreen, preserve 54.8%, 61.3%, 65.0%, 74.0%, 55.5%, and 43.7% of their areas, respectively. A marked negative linear correlation between temperature and remaining glacier areas was observed, enabling the prediction of future glacier variations under various climate conditions. The study outlines the evolution of terrain features in Kaffiøyra in nearly 40 years. Results show the relative stability of land-shorelines, significant glacier retreats in response to warming temperatures, and slight growth in outwash areas. The study highlights the critical need for robust land monitoring systems that enhance our understanding and enable effective decision-making through predictive modeling of climate impacts.

Background/Objectives: Recent advancements in artificial intelligence (AI) have spurred interest in developing computer-assisted analysis for imaging examinations. However, the lack of high-quality datasets remains a significant bottleneck. Labeling instructions are critical to improving dataset quality but are often lacking. This study aimed to establish a liver MRI segmentation protocol and assess its impact on annotation quality and inter-reader agreement; Methods: This retrospective study included 20 patients with chronic liver disease. Manual liver segmentations were performed by a radiologist in training and a radiology technician on T2 weighted imaging (wi) and T1wi at the portal venous phase. Based on the inter-reader discrepancies identified after the first segmentation round, a segmentation protocol was established, guiding the second round of segmentation, resulting in a total of 160 segmentations. Dice Similarity Coefficient (DSC) assessed inter-reader agreement pre- and post-protocol with a Wilcoxon signed-rank test. Slice selection at extreme cranial or caudal liver positions was evaluated using the McNemar test; Results: The per-volume DSC significantly increased after protocol implementation for both T2wi (p<0.001) and T1wi (p=0.03). Per-slice DSC also improved significantly for both T2wi and T1wi (p<0.001). The protocol reduced the number of liver segmentations with a non-annotated slice on T1wi (p=0.04), but the change was not significant on T2wi (p=0.16); Conclusions: Establishing a liver MRI segmentation protocol improves annotation robustness and reproducibility, paving the way for advanced computer-assisted analysis. Moreover, segmentation protocols could be extended to other organs and lesions and incorporated into guidelines, thereby expanding the potential applications of AI in daily clinical practice.

Global climate has undergone significant changes in the past 50-60 years. The transition from stable climate conditions to current warming has significantly influenced permafrost landscapes and infrastructure in cold regions. Arctic and Subarctic landscapes had distinct reactions to these changes. The objective of this study is to analyze the regional aspects of climate and permafrost changes in Yakutia's Arctic and subarctic regions from the past 50-60 years. Our analysis focused on climate change patterns during different periods, including cold period, pre-warming, and modern warming. We analyzed data on mean annual air temperature, thawing and freezing indices, and precipitation. During these periods, we determined the reaction's characteristics in various landscape regions. The most significant impact on permafrost began in the mid-2000s, as our observations confirmed. Global warming directly causes permafrost temperature to rise, the active layer to thicken, and cryogenic processes to develop. The conducted research provides the information needed to study the effects of global warming on permafrost and adapt socio-economic conditions in northern Yakutia.

This paper presents a novel privacy-preserving approach, Fed-CNN1D (Federated Convolutional Neural Network 1D), based on federated learning for monitoring air quality and classifying different activities of daily living in indoor spaces. The system employs six different types of sensors to collect measurement parameters, which are then used to train a 1D CNN model locally for activity recognition. The proposed model is lightweight and edge deployable, making it suitable for real-time applications. Experiments were conducted using an air quality dataset specifically curated for Activity of Daily Living (ADL) classification. The results demonstrate that our approach Fed-CNN1D achieves 96.50% accuracy, 96.27% F1-Score, 96.54% precision, 96.21% recall, 0.11% loss, with low communication cost of 0.099Mb, and a swift detection time of 15 ms.

Essential Tremor (ET) is among the most common neurological movement disorders, with incidence rates increasing with age. The disorder is characterized by a bilateral action tremor of the upper limbs, enduring for a minimum of three years in duration, and shows early signs of onset with progression over time. Recent terminology has categorized a subcategory of ET, known as Essential-Tremor Plus (ET-Plus), which consists of a combination of symptoms that include tremors at rest and additional neurological signs. This includes memory impairment, gait disturbances, or various other neurological symptoms that do not have full clinical significance for diagnosis and suggest a more complex underlying neurological condition. Abnormalities located primarily in the cerebellum, including the loss and damage to Purkinje Cells (PC), were core pathological features of cerebellar damage in postmortem ET. Given the complexity and uncertainty of the pathophysiological aspects of tremor generation, treatment options are still limited, and current therapeutic measures lack the capacity to fully alleviate symptoms. In consideration, there are current pharmaceutical, stimulation, and genetic studies being conducted to characterize the etiology and pathophysiology of ET and aid in the reduction of progression and symptoms. In this review, we will discuss various aspects of ET including neurophysiology, pathophysiology, genetic factors and etiology, treatment options and future directions of advancements for ET management.

This study addresses the challenge of predicting the dynamic behavior of the structures under seismic excitation. Accurate prediction of such systems' responses is critical for the design and evaluation of buildings and infrastructure. Traditional methods, including numerical models and differential equation solvers, often face significant computational burdens, especially with nonlinear hysteretic behaviors and large-scale problems. To overcome these limitations, a novel Fuzzy-based Convolutional Neural Network (FuzzyCNN) model is developed. This model integrates fuzzy logic principles with convolutional neural networks to effectively manage the uncertainties and complexities inherent in soil-structure interaction under seismic loads. The model's performance is validated through both numerical simulations and experimental data from a mid-rise concrete building subjected to seismic events. Comparative analysis with a traditional Physics-informed CNN (PhyCNN) model demonstrates the superior accuracy and robustness of the FuzzyCNN in predicting seismic responses. Key results show that the FuzzyCNN model not only enhances prediction accuracy but also handles uncertainties more effectively than the PhyCNN model. The findings suggest that the FuzzyCNN model can significantly improve the efficiency and accuracy of dynamic response predictions. This advancement offers valuable implications for engineering design, seismic risk assessment, and the development of more resilient infrastructure.

In this paper, we used meteorological data to simulate vertical profiles of optical turbulence at the Special Astrophysical Observatory (Russia, 43°40′19 ′′ N 41°26′23′′E, 2100 m a.s.l.), site of the 6-m Big Telescope Alt-azimuthal. The vertical profiles of optical turbulence are calculated using ERA-5 reanalysis data. These profiles are corrected using DIMM measurements as well as estimations of atmospheric boundary layer heights. We may note that the method basically reconstructs the most important features of the shape of the measured profile under clear sky. Atmospheric turbulent layers were identified, and the strength of optical turbulence in these layers was estimated. The model hourly values of seeing corresponding to the obtained vertical profiles range from 0.85 to 2.10 arcsec, values of the isoplanatic angle vary in the range from 1 to 3.0 arcsec (at λ = 500 nm). These estimations are close to the measured median of seeing (1.21 arcsec).