Qualitative comparative analysis (QCA) was developed by Charles Ragin in 1987 for the comparative analysis of small data sets. The method has become widespread in sociological and economic research. There are examples of successful use of QCA in the field of medicine and epidemiology. The purpose of this review is to describe the key stages of QCA with a discussion the application of this method to the analysis of medical and epidemiological data.

The epithelial-mesenchymal transition (EMT) is a major event in idiopathic pulmonary fibrosis pathogenesis. Here, we investigated whether growth arrest-specific protein 6 (Gas6) plays a protective role in lung fibrosis via suppression of the EMT and fibroblast activation. rGas6 ad-ministration inhibited the EMT in isolated mouse ATII cells 14 days post-BLM treatment based on morphologic cellular alterations, changes in mRNA and protein expression profiles of EMT markers, and induction of EMT-activating transcription factors. BLM-induced increases in gene expression of fibroblast activation-related markers and the invasive capacity of primary lung fi-broblasts in primary lung fibroblasts were reversed by rGas6 administration. Furthermore, the hydroxyproline content and collagen accumulation in interstitial areas with damaged alveolar structures in lung tissue were reduced by rGas6 administration. Targeting Gas6/Axl signaling events with specific inhibitors of Axl (BGB324), COX-2 (NS-398), EP1/EP2 receptor (AH-6809), or PGD2 DP2 receptor (BAY-u3405) reversed the inhibitory effects of rGas6 on EMT and fibroblast activation. Finally, we confirmed the antifibrotic effects of Gas6 using Gas6−/− mice. Therefore, Gas6/Axl signaling events play a potential role in inhibition of EMT process and fibroblast acti-vation via COX-2-derived PGE2 and PGD2 production, ultimately preventing the development of pulmonary fibrosis.

Background: Glycogen storage diseases (GSDs), known as glycogenoses, belong to inborn metabolic defects in the glycogen metabolism pathway. Several types of GSDs are distinguished, including Ia, Ib, III, VI, and IX. GSDs manifest as excessive glycogen deposition in the liver and muscles, resulting in the dysfunction of these organs. Therefore, the treatment of choice is multitherapy that, due to dietary restrictions, may lead to nutritional deficiencies and organ complications. This study aims to assess anthropometric, body composition, skeletal status, results of selected laboratory tests, dietary, lifestyle and physical capacity analyses in patients with glycogenoses and compare the results between GSD I and GSD III-VI-IX groups. Results: The results indicate that GSD patients were overweight or obese (44% of patients had BMI +1SD or +2SD) with high percentage of fat tissue (50% of patients had above 30% of body fat) and inadequate bone mineralisation (total body less head: median z-score -0.9, L2-L4 segment: median z-score -1.65). GSD I patients had the highest risk of developing obesity (67% of them had above 30% of body fat) and osteoporosis (total body less head: median z-score -1.0, L2-L4 segment: median -1.7). Conclusions: Special attention should be paid to appropriate diet and supplementation in patients with glycogenoses.

Background: Surgical decompression (SD) with and without posterior stabilization followed by radiotherapy is an established treatment for patients with metastatic spinal disease with epidural spinal cord compression (ESCC). This study aims to identify risk factors for occurrence of neurologic comprise resulting from local recurrence. Methods: All patients who received surgical treatment for metastatic spinal disease at our center between 2011 and 2022 were included in this study. Cases were evaluated for tumor entity, surgical technique for decompression (decompression, hemilaminectomy, laminectomy, corpectomy) neurological deficits, grade of ESCC, time interval to radiotherapy and perioperative complications. Results: A total of 747 patients were included in the final analysis, with a follow-up of 296.8 days (95% CI (263.5, 330.1)). During the follow-up period 7.5% of patients developed spinal cord/cauda syndrome (SCS). Multivariate analysis revealed prolonged time (> 35 d) to radiation therapy as solitary risk factor (p < 0.001) for occurrence of SCS during follow-up. Conclusion: Surgical treatment of spinal metastatic disease improves patients’ quality of life and Frankel grade but radiation therapy needs to be scheduled within a time frame of few weeks in order to reduce the risk of tumor-induced neurologic comprise.

This article describes rationales and limitations for making inferences based on data from randomized controlled trials (RCTs). We argue that obtaining a representative random sample from a patient population is impossible for a clinical trial because patients are accrued sequentially over time and thus comprise a convenience sample, subject only to protocol entry criteria. Consequently, the trial’s sample is unlikely to represent a definable patient population. We use causal diagrams to illustrate the difference between random allocation of interventions within a clinical trial sample and true simple or stratified random sampling, as done in surveys. We argue that group-specific statistics, such as a median survival time estimate for a treatment arm in an RCT, have limited meaning as estimates of larger patient population parameters. In contrast, random allocation between interventions facilitates comparative causal inferences about between-treatment effects, such as hazard ratios or differences between probabilities of response. Comparative inferences also require the assumption of transportability from a clinical trial’s convenience sample to a targeted patient population. We focus on the consequences and limitations of randomization procedures in order to clarify the distinctions between pairs of complementary concepts of fundamental importance to data science and RCT interpretation. These include internal and external validity, generalizability and transportability, uncertainty and variability, representativeness and inclusiveness, blocking and stratification, relevance and robustness, forward and reverse causal inference, intention to treat and per protocol analyses, and potential outcomes and counterfactuals.

Background: The purpose of this study was to ascertain the reliability of two 15-sec sprint cycling tests in men and women to estimate the maximum rate of glycolysis or lactate production (VLa-Max). Methods: Eighteen men and twelve women completed two sprint sessions over 1-week. A 10-min warm-up preceded a 3 µl blood lactate (BLC) sample, after which a 15-sec sprint was completed; cyclists then rested passively while multiple lactate samples were taken until levels peaked. VLaMax was calculated as (Peak BLC – Pre BLC) * (15-sec – Talac)-1. Trial differences and reliability across trials were analyzed using a paired-sample t-test, Pearson correlation, ICC, and Bland-Altman analysis with α=0.05 for all tests; data are reported as mean ± sd. Results: Power (W) was similar across trails (773.0 ±143.5 vs. 758.2 ± 127.4; p = 0.333) and coefficient of varia-tion (CV) of 4.7%. VLaMax was similar (0.673 ± 0.024 vs 0.635 ± 0.237; p = 0.280), but only moder-ately reliably across trials with a CV, ICC, and R value of 18.6%, 0.661, and 0.67, respectively. Conclusions: A 15-sec VLaMax cycling sprint is moderately reliable being affected both by the lac-tate measurement and other variables used in the calculation. More research may offer ways to improve reliability.

The SARS-CoV-2 infection is a cause of hypoxemic acute respiratory failure, leading to frequent intensive care unit (ICU) admission. Due to invasive organ support and immunosuppressive therapies, these patients are prone to nosocomial infections. Our aim was to assess the value of daily measurements of C-reactive protein (CRP) and Procalcitonin (PCT) in the early identification of ICU-acquired infections in COVID-19 patients Methods: We undertook a prospective observational cohort study (12 months). All adult me-chanically-ventilated patients admitted for ≥ 72 hours to the ICU with COVID-19 pneumonia were divided into an infected group (n=35) and a non-infected group (n=83). Day 0 was considered as the day of the diagnosis of infection (infected group) and day 10 of ICU stay (non-infected group). The kinetics of CRP and PCT were assessed from day -10 to day 10 and evaluated using a general linear model, univariate, repeated-measures analysis. Results: 118 patients (mean age 63 years, 74% males) were eligible for the analysis. The groups did not differ in patients' age, gender, CRP, and PCT serum levels at ICU admission. However, the infected group encompassed patients with a higher severity (SOFA score at ICU admission, p=0.009) and a higher 28-day mortality (p<0.001). Before D0, CRP kinetics showed a significant increase in infected patients, whereas in noninfected it remained almost unchanged (p<0.001), while PCT kinetics did not appear to retain diagnostic value to predict superinfection in COVID-19 patients (p=0.593). Conclusion: COVID-19 patients who develop ICU-acquired infections exhibited different biomarker kinetics before the diagnosis of those infections. Daily CRP monitoring and the recognition of the CRP kinetics could be useful in the prediction of ICU-acquired infections.

Background: The popularity of climbing via ferratas (VF) in the European Alps has increased a lot. We have analysed the pattern of injuries from accidents, which have occurred on VF, and First Aid (FA) knowledge from the persons on such alpine routes. The aim was to create specific FA courses for those who climb VF. Material and Methods: Incidences and FA knowledge of the alpinists were investigated by a questionnaire. Data included details of the individual’s alpine experience, accidents or any kind of medical emergency they have ever experienced. FA knowledge was evaluated by 18 multiple choice questions with 5 answers each. The questionnaire was based on those of previous studies but was slightly adopted for VF. For this data from rescue operations on VF were used. Finally every participant was asked to judge his FA knowledge by a Likert scale (1 to 5). Results: The collective consisted of 391 participants (28.4% women) with an average age of 41 years. A significant portion did no regular fitness training and in most cases the last FA training was years ago. Nobody had joined a specific FA course for climbers or mountaineers. The most common causes of emergencies were: weather conditions (19%), stumble (18.6%), falls (13.4%), and weariness and deficiency in concentration (13.4%). FA questions most often answered correctly were cardiac emergencies (49.6%), high altitude cerebral edema (HACE) (43.2%), snow blindness (40.9%), injuries of the head (37.1%) and hemorrhagic shock (35.3%). Those most often wrong were injuries of the back (1.0%), injuries of the thorax (2.8%), frostbite (9.0%), acute mountain sickness (AMS) (4.1%) and strategy of rescue (4.1%). VF climbers show significant less FA knowledge than mountaineers in the Western Alps and trekkers. Conclusions: Data support the need of a specific FA training for VF climbers. Their FA knowledge does not fit with the demands in the mountains while some other knowledge they have is negligible on VFs (e.g. HACE). This is in accordance with earlier studies in “classic” alpine mountaineering and in trekking.

The incidence of breast cancer and therefore need for breast reconstruction is expected to increase. The many reconstructive options available and the changing aspects of the field make this a complex area of plastic surgery, requiring knowledge and expertise. Two major types of breast reconstruction can be distinguished: Breast Implants and Autologous Flaps. Both present advantages and disadvantages. Autologous fat grafting is also commonly used. MRI is the modality of choice for evaluating breast reconstruction. Knowledge of the type of reconstruction is preferable to provide the maximum of pertinent information and avoid false positives. Early complications include seroma, hematoma, and infection. Late complications depend on the type of reconstruction. Implant rupture and implant capsular contracture are frequently encountered. Depending on the implant type, specific MRI signs can be depicted. In case of myocutaneous flap, fat necrosis, fibrosis and vascular compromise represent the most common complications. Late cancer recurrence is much less common. Rare reported late complications include breast implant associated large cell anaplastic lymphoma (BIA-ALCL) and, recently described and even rarer, Breast implant-associated squamous cell carcinoma (BIA-SCC). In this review article, the various types of breast reconstruction will be presented, with emphasis on pertinent imaging findings and complications.

Introduction: This study aimed to evaluate whether radiomic features extracted solely from the edema of soft tissue sarcomas (STS) could predict the occurrence of lung metastasis in comparison with features extracted solely from the tumoral mass. Materials and Methods: We retrospectively analyzed magnetic resonance imaging (MRI) scans of 32 STSs including 14 with lung metastasis and 18 without. Segmentation of the tumor mass and edema was performed for each MRI examination. A total of 107 radiomic features were extracted for each mass segmentation and 107 radiomic features for each edema segmentation. A two-step feature selection process was applied. Two predictive features for the development of lung metastasis were selected from the mass-related features, as well as two predictive features from the edema-related features. Two random forest models were created based on these selected features; 100 random subsampling runs were performed. Key performance metrics including accuracy and area under the ROC curve (AUC) were calculated, and the resulting accuracies were compared. Results: The model based on mass-related features achieved a median accuracy of 0.83 and a median AUC of 0.88, while the model based on edema-related features achieved a median accuracy of 0.75 and a median AUC of 0.79. Statistical analysis comparing the accuracies of the two models revealed no significant difference. Conclusion: Both models showed promise in predicting the occurrence of lung metastasis in soft tissue sarcomas. These findings suggest that radiomic analysis of edema features can provide valuable insights into the prediction of lung metastasis in soft tissue sarcomas.

The right to live with dignity during the final stages of existence, enshrined in national and supranational Charters of Rights, represents a significant step towards humanizing medicine and is integral to the right to health. Palliative Care, rooted in health, dignity, and therapeutic self-determination, has emerged as a fundamental human right and a moral imperative within health systems. It seeks to alleviate suffering, emphasizing the holistic well-being of patients with life-limiting illnesses. This paper provides a comprehensive global perspective on Palliative Care, with special attention to its implementation in Italy. Palliative Care is a crucial dimension of care, designed to enhance the quality of life for patients suffering from progressive and incurable diseases. Despite its growing need due to an aging population and increased prevalence of chronic illnesses, still remains a significant disparity in access to these essential services worldwide. Insufficient resources, legislative constraints, and limited training for healthcare providers pose challenges to the widespread adoption of Palliative Care. In Italy, although laws have been enacted to ensure the provision of Palliative Care, its availability remains inconsistent across different regions. Financial constraints and insufficient support hinder the comprehensive dissemination of these services. Recognizing the significance of Palliative Care, the Catholic Church also endorses its implementation as a response to human suffering and an approach to end-of-life care. Efforts to strengthen Palliative Care are critical to meeting the rising demand and ensuring access to compassionate and dignified care for all individuals in need. Through legislative advancements and adequate resources, Italy can make significant strides in advancing the provision of Palliative Care.

To compare the quality of low-energy VMIs obtained with three DECT platforms according to phantom diameter. Three sections of the Mercury Phantom 4.0 were scanned on two generations of split-filter CTs (SFCT-1st and SFCT-2nd) and on one Dual-source CT (DSCT). Noise power spectrum (NPS), task-based transfer function (TTF) and detectability index (d’) were assessed on VMIs from 40 to 70keV. Highest noise magnitude values were found with SFCT-1st and noise magnitude was higher with DSCT than with SFCT-2nd for 26-cm (10.2±1.3%) and 31-cm (7.0±2.5%), and the opposite for 36-cm (-4.2±2.5%). The highest average NPS spatial frequencies and TTF values at 50% (f50) values were found with DSCT. For all energy levels, f50 values were higher with SFCT-2nd than SFCT-1st for 26-cm (3.2±0.4%) and the opposite for 31-cm (-6.9±0.5%) and 36-cm (-5.6±0.7%). The lowest d’ values were found with SFCT-1st. For all energy levels, d’ values were lower with SFCT-2nd than with DSCT for 26-cm (-6.2±0.7%), similar for 31-cm (-0.3±1.9%) and higher for 36-cm (5.4±2.7%). In conclusion, compared to SFCT-1st, SFCT-2nd exhibited lower noise magnitude and higher detectability values. Compared with DSCT, SFCT-2nd had lower noise magnitude and higher detectability for the 26-cm but the opposite was true for the 36-cm.

A paper in this journal on decamethylcyclopentasiloxane (D5) used unreliable methods to reach unreliable conclusions. The authors concluded that D5 exposure should be viewed as causing human reproductive disorders and should be considered a developmental toxicant, that is, toxic to the developing embryo. The inadequate methods could not have answered the authors' research questions, and the paper should be withdrawn.

The aim of this review was to summarize the available literature regarding resident and medical student education utilizing the spaced repetition program Anki. This review enables current residents and medical students to recognize and utilize evidence-proven study techniques to increase learning efficacy. A systemic review of papers across all medical journals available on Pubmed was conducted to identify studies published without time constraints. The search was for (Anki) and (spaced repetition residency). Available outcome data was collected and discussed independently for students and residents. Many studies showed a statistically significant increase in exam performance associated with Anki use and high levels of satisfaction among residents and medical students. Further research is warranted to provide high-quality evidence of Anki’s applications and there is a need for exploration in additional residency specialties. Anki use has steadily increased with both medical students and residents. The application demonstrated consistent improvement on exam-based performance and was regarded highly by users. As burnout and time constrictions threaten educational workflow, Anki may serve as a powerful tool to improve the quality of learning. Further data needs to be collected and analyzed in specialties where Anki use may already exist.

Autoantibodies against specific lung cancer-associated antigens have been suggested for lung cancer diagnosis. This study aimed to evaluate the diagnostic performance of anti-gen-autoantibody immune complex (AIC) against its free antigens for CYFRA21-1, ProGRP, NGAL, and NSE in non-small cell lung cancer (NSCLC). In total, 85 patients with NSCLC and 120 healthy controls (HCs) were examined using a 9G DNA chip method. The ratios of AICs to their antigens and combinations of ratios consisting of two to four markers were calculated. The levels of AICs for CYFRA21-1, ProGRP, NGAL, and NSE were higher than their free antigens in all participants. The levels of each free antigen distinguished patients with NSCLC from HCs. The ratios of AIC to its antigen and seven combinations consisting of two to four ratios were significantly higher in patients with NSCLC than those in HCs. Excellent diagnostic performance was observed for all combination ratios (C4-1), with 85.9% sensitivity and 86.7% specificity at cutoff 3.51. Higher sensitivity was observed in the very early stages (0–I) and adenocarcinoma than in stages II–IV and other pathological types. The combination of all ratios of AICs and their antigens for all four markers is useful in the diagnosis of NSCLC.

We present a case of a combination of two rare hereditary disorders: Аdrenal insufficiency, obesity, and red hair syndrome (OBAIRH) and Duchenne muscular dystrophy (DMD) in a boy. Both diseases were diagnosed during the first year of life. OBAIRH was suggested based on the ethnicity and family history of the patient, while DMD – based on an extreme increase in transaminase and CK levels during biochemical analysis of his blood.

Artificial intelligence-powered generative language models (GLMs), such as ChatGPT and Google Bard, have the potential to provide personalized learning, unlimited practice opportunities, and interactive engagement 24/7, with immediate feedback. However, to fully utilize GLMs, properly formulated instructions are essential. Prompt engineering is a systematic approach to effectively communicating with GLMs to achieve the desired results. Well-crafted prompts yield good responses from the GLM, while poorly constructed prompts will lead to unsatisfactory responses. Besides the challenges of prompt engineering, significant concerns are associated with using GLMs in medical education, including ensuring accuracy, mitigating bias, maintaining privacy, and avoiding excessive reliance on technology. Future directions involve developing more sophisticated prompt engineering techniques, integrating GLMs with other technologies, creating personalized learning pathways, and researching the effectiveness of GLMs in medical education.

Our search of existing cancer databases aimed to assess the current landscape and identify key needs. We analyzed 71 databases, focusing on genomics, proteomics, lipidomics, and glycomics. We found a lack of cancer-related lipidomic and glycomic databases, indicating a need for further development in these areas. Proteomic databases dedicated to cancer research were also limited. To assess overall progress, we included human non-cancer databases in proteomics, lip-idomics, and glycomics for comparison. This provided insights into advancements in these fields over the past eight years. We also analyzed other types of cancer databases, such as clinical trial databases and web servers. Evaluating user-friendliness, we used the FAIRness principle to assess findability, accessibility, interoperability, and reusability. This ensured databases were easily accessible and usable. Our search summary highlights significant growth in cancer databases while identifying gaps and needs. These insights are valuable for researchers, clinicians, and database developers, guiding efforts to enhance accessibility, integration, and usability. Addressing these needs will support advancements in cancer research and benefit the wider cancer community.

The classic, chronic Philadelphia chromosome-negative (Ph-) myeloproliferative neoplasms (MPN)- mainly essential thrombocythemia (ET), polycythemia vera (PV), and myelofibrosis (MF)— represent a heterogeneous group of stem cell disorders characterized by clonal proliferation of one or more hematopoietic cell lineages with organomegaly and constitutional symptoms. Several studies have shown that the presence of chronic inflammation and a dysregulated immune system play indisputable roles in the pathogenesis of these diseases. Lately, the treatment of cancer including hematological malignancy has progressed on the agents targeting the immune system, cytokine milieu, immunomodulatory agents, and targeted immune therapy. Immune checkpoints are the molecules that regulate T cells function in the tumor microenvironment (TME). The fully unraveled primary immune checkpoints are programmed cell death-1 (PD-1)/programmed cell death ligand-1 (PD-L1), and cytotoxic T-lymphocyte antigen-4 (CTLA-4). Immune checkpoint inhibitor therapy (ICIT) is based on blocking the inhibitory pathways in T cells to promote anti-tumor immune responses and has revolutionized cancer treatment paradigms. Despite the impressive clinical success of ICIT, tumor intrinsic resistance remains a daunting challenge for oncologists leading to a low response rate in solid tumors and hematological malignancies. A phase II trial on Nivolumab for patients with Primary Myelofibrosis, post-Essential thrombocythemia myelofibrosis, or post- Polycythemia myelofibrosis has been performed (ClinicalTrials.gov Identifier: NCT02421354). This clinical trial, on the efficacy of PD-1 blockade using the monoclonal antibody Nivolumab, was prematurely terminated due to a lack of efficacy and adverse events. A multicenter, open-label, phase 2, single-arm study was conducted including pembrolizumab in patients with Dynamic International Prognostic Scoring System category of intermediate-2 or greater primary, post-essential thrombocythemia or post-polycythemia vera myelofibrosis that were ineligible for or were previously treated with Ruxolitinib. Pembrolizumab treatment was well tolerated, but there were no objective clinical responses, so the study closed after the first stage was completed. To permit more patients to benefit from immunotherapy, the focus has changed to targeting alternative novel immune checkpoints in the tumor microenvironment such as lymphocyte activation gene-3 (LAG-3), T cell immunoglobulin, and mucin domain 3 (TIM-3), V-domain immunoglobulin-containing suppressor of T-cell activation (VISTA), T cell immunoglobulin and ITIM domain (TIGIT) and human endogenous retrovirus-H long terminal repeat-associating protein 2 (HHLA2) forming the basis of next-generation ICIT. Our article aims at emphasizing and discovering the role of next-generation ICIT in MPN as targeted immunotherapy involving monoclonal antibodies, checkpoint inhibitors, or therapeutic vaccines against selected MPN epitopes that could further enhance tumor-specific immune responses. Immunotherapeutic approaches are expanding and hopefully will extend the therapeutic armamentarium in patients with myeloproliferative neoplasms. Preliminary studies from our laboratory showed over-expressed MDSC and over-expressed VISTA in MDSC, and in progenitor and immune cells directing the need for more clinical trials using next-generation ICI in the treatment of MPN.

Anemia is a well-developed discipline where the concepts of precision medicine have, in part and being studied extensively. This review discusses the treatment of EPO-deficient anemia and resistance in Chronic Kidney Disease (CKD). Traditionally, erythropoietin-stimulating agents (ESA) and iron supplementation have been used to manage anemia in CKD. However, these treatments have potential risks, including cardiovascular and thromboembolic events. Newer treatments have emerged to address these risks, such as slow-release and low-dosage intravenous iron, oral iron, and erythropoietin-iron combination therapy. Another novel approach is using hypoxia-inducible factor prolyl hydroxylase inhibitors (HIF-PHI). This review highlights the need for precision medicine targeting genetic components of EPO-deficient anemia in CKD and discusses individual variability of genes such as the erythropoietin gene (EPO), Interleukin-β gene (IL-β), and hypoxia-inducible factor gene (HIF). Pharmacogenomics testing aims to provide targeted therapies and interventions that are tailored to the specific characteristics of an individual, optimizing treatment outcomes and minimizing resistance and adverse effects. The article concludes by suggesting the potential of receptor modification to revolutionize treatment outcome of erythropoietin deficiency anemia through integration of the mentioned approach.

Exosomes have shown promising potential as a therapeutic approach for wound healing. Nevertheless, the translation from experimental studies to commercially available treatments is still lacking. To assess the current state of research in this field, a systematic review was performed to examine the methodological heterogeneity among studies conducted over the past five years. Additionally, the review analyzed the suitability of animal models used and their relevance to human medicine. A PubMed search was performed for english-language, full-text available papers published from 2018 to June 2023, focusing on exosomes derived from mammalian sources and their application in wound healing, particularly those involving in vivo assays. Out of 531 results, 148 papers were selected for analysis. The findings revealed significant variability in terms of cell sources and types, biomaterials, and administration routes under investigation, indicating the need for further research in this field. Additionally, a comparative examination encompassing diverse cellular origins, types, administration pathways, or biomaterials is imperative. Furthermore, the predominance of rodent-based animal models raises concerns, as there have been limited advancements towards more complex in vivo models and scale-up assays. These constraints underscore the substantial efforts that remain necessary before attaining commercially viable and extensively applicable therapeutic approaches using exosomes.

Cyclic vomiting syndrome (CVS) is a frequently disabling condition defined by severe, discrete, stereotypical episodes of nausea and vomiting. In our previous report [1], a candidate gene list was evaluated in a group of 80 unrelated participants with CVS with extensive genetic sequencing (whole exome/genome). Twenty-two good candidate genes for CVS association were identified, from which a cellular model of aberrant cation transport and energy metabolism was proposed. Herein, we present additional clinical information on those 80 participants, including eight case reports to highlight general principles and clinical practices. Our participants demonstrate substantial phenotypic and genotypic heterogeneity in CVS, including a propensity for specific co-morbidities in the participants and relatives, evolution towards episodes in which vomiting is less apparent, labeling with “non-organic” diagnoses, multiple genes, polygenic inheritance, and association with variants not designated as Pathogenic. Our case reports illustrate how genetic information can guide clinical management and argue to the high clinical utility of appropriate genetic testing and analysis in CVS. Expression profiling of our 22 candidate genes suggests an anatomical model in a unit defined by vagal afferents and adjacent cells. Our models are consistent with multiple current hypotheses of CVS and the efficacy of commonly-recommended treatments.

Recently, Medical Radiology has undergone significant improvements in patients’ management due to advancements in image acquisition by last generation machines, data processing, and integration of artificial intelligence. In this way, cardiovascular imaging is one of the fastest growing radiological subspecialty. A compressive review was focused on addressing how and why CT and MR have gained a I class indication in most cardiovascular diseases, and the potential impact of the tissue and functional characterization by CT photon counting, quantitative MR mapping, and 4-D flow. Regarding rectal imaging, advances in cancer imaging by using diffusion-weighted MRI sequences for identifying residual disease after neoadjuvant chemoradiotherapy and [18F] FDG PET/MRI were provided for high-resolution anatomical and functional data in oncological patients. We offered a large overview on the approach to the imaging of diffuse and focal liver diseases by US elastography, contrast-enhanced US, quantitative MRI, and CT for the patient’s risk stratification. Italy is riding the wave of these improvements. The development of large networks will be crucial to create high-quality databases for a patient-centered precision medicine by artificial intelligence. dedicated radiologists with specific training and a close relationship with the referring clinicians will be the essential human factor.

Pseudomonas aeruginosa is an antibiotic-resistant priority pathogen as listed by the World Health Organization. P. aeruginosa, a gram-negative, rod-shaped bacterium, is frequently encountered in hospital settings as an opportunistic pathogen in nosocomial infections. P. aeruginosa has shown high antibiotic resistance, which has been attributed to several mechanisms, such as intrinsic, acquired, and adaptive resistance. This study investigates several genes which may be attributed to the acquisition of antibiotic resistance in the bacteria P. aeruginosa, as it transitions from its planktonic form to the more threatening and resistant biofilm form. Subsequently, the study assesses the comparative efficacy of three antibiotics, Ofloxacin (OFX), Tobramycin (TOB), and Ceftazidime (CAZ), in altering the expression levels of the identified multidrug efflux pump genes associated with the ability to extrude antibiotics from the cell when in the biofilm form. Efflux transporter gene expression in P. aeruginosa was conducted via extraction of total RNA in planktonic and biofilm- both untreated and treated - with Tobramycin (TOB), Ofloxacin (OFX), and Ceftazidime (CAZ). Real-time quantitative reverse transcriptase PCR was employed to analyze alterations in expression levels of the Mex A, Mex B, Mex X, Mex Y, OprM, and RPSL genes in the collected samples. In samples where no antibiotics were administered, an increase in expression of the Mex B efflux pump gene was observed compared to all other efflux pump genes in the biofilm form, providing multidrug resistance when active. The gene of interest, Mex B, was assessed for antibiotic resistance by P. aeruginosa culturing in the planktonic and biofilm forms with simultaneous treatment with TOB, OFX, and CAZ. Of the three antibiotics used, OFX showed more effectiveness in preventing biofilm growth by reducing the expression level of the Mex B efflux pump gene in the biofilm form, making P. Aeruginosa more antibiotic sensitive to OFX. TOB has similar results compared to OFX but was slightly less effective in reducing the expression of Mex B. Conversely, CAZ was not effective in reducing expression of the Mex B gene in the biofilm form or the planktonic form and was determined to be ineffective at eradicating the organism.

Antimicrobial application of carbon nanomaterials such as carbon nanotubes (CNTs), capped CNT, C60 fullerene, and C70 fullerene are increasing owing to their low cytotoxicity properties compared to other nanomaterials such as metallic nanoparticles. Enhanced mechanical property and antibacterial activity can be caused by incorporation of CNTs in 3-dimentional (3D) printed nanocomposites (NCs). Interruption of bacterial membrane resulting from cylindrical shape and high aspect ratio properties has been found as the prominent antibacterial mechanism of CNTs. However, unraveling interaction of CNT, capped CNT, C60 fullerene, and C70 fullerene with virulence factors of the main bacterial pathogenesis has not yet been known. Therefore, in the present study, interactions of CNT, capped CNT, C60, and C70 with the eight virulence factors including protein kinase A and ESX-secreted protein B of Mycobacterium tuberculosis, pseudomonas elastase and exotoxin A of Pseudomonas aeruginosa, alpha-hemolysin and penicillin binding protein 2a of Staphylococcus aureus and shiga toxin 2a and heat-labile enterotoxin of Escherichia coli have been evaluated by molecular docking method of AutoDock Vina. This study disclosed the binding strength followed the sequence CNT &gt; capped CNT &gt; C70 &gt; C60 towards alpha-hemolysin of S. aureus compared to other virulence factors with values of -19.6, -18.8, -13.6, and -12.8 kcal/mol, respectively. The lowest and highest binding affinity were found for CNT against 1TII and 1MWT by binding energy values of 97.4 and -20.1 kcal/mol. The stability of CNT-1MWT complex at the different times has resulted according to the normal mode analysis of elNémo and iMOD servers. Future studies should be focused on improving cellular uptake of CNTs, capped CNTs, C70, and C60 by surface functionalization for active targeting of bacteria.

Oral mucosa is a key player in the cancer patient and during cancer treatment. The increasing prevalence of cancer and cancer therapy associated side effects are behind the major role that oral mucosa plays in the oncological patient. Oral mucositis is a debilitating severe complication caused by early toxicity of chemo and/or radiotherapy that can restrict treatment outcome possibilities, even challenging patient’s survival. It has been referred to as the most feared cancer treatment complication. Predictive variables as to who and to what extent will be affected are still unclear. Additionally, oral mucositis is one of the sources of the increasing economic burden of cancer, not only for patients and their families but also for institutions and governments. All efforts should be implemented in the search for new approaches to minimize the apparently ineluctable outburst of oral mucositis along the cancer treatment. New perspectives derived from different approaches in explaining the interrelation between oral mucositis and oral microbiome or the similarities with genitourinary mucosa may help elucidate the biomolecular pathways and mechanisms behind oral mucosa cancer-therapy related toxicity and what is more important its management in order to minimize treatment side effects and to provide enhanced cancer support.

Quantitative polymerase chain reaction (qPCR) has emerged as an important bioanalytical method for assessing the pharmacokinetics of human cell-based medicinal products after xenotransplantation into immunodeficient mice. A particular challenge in bioanalytical qPCR studies is that the different tissues of the host organism can affect amplification efficiency and amplicon detection to varying degrees, and ignoring these matrix effects can easily cause a significant underestimation of the true number of target cells in a sample. Here we describe the development and drug regulatory-compliant validation of a TaqMan® qPCR assay for quantification of mesenchymal stromal cells in the range of 125 to 20,000 cells/200 µl lysate by amplification of a human-specific, highly repetitive αsatellite DNA sequence of the chromosome 17 centromere region HSSATA17. Assessment of matrix effects in 14 different mouse tissues and blood revealed a wide range of spike recovery rates across the different tissue types from 11 to 174%. Based on these observations, we propose to perform systematic spike-and-recovery experiments during assay validation and to correct for the effects of the different tissue matrices on cell quantification in subsequent bioanalytical studies by multiplying the back-calculated cell number by tissue-specific factors derived from the inverse of the validated percent recovery rate.

We present a case of neglected atypical high grade prostatic stromal sarcoma (PSS) at the transitional zone , invading the peripheral zone, prostatic capsule & peri prostatic fat reaching to pelvic side wall, it showed T2WI hyperintense signal (compared to typical cases of PSS showing T2WI hypointense signal) corresponding to developing on top of pre-existing nodular hyperplasia. Careful reviewing of prostatic central / transitional zone lesions is mandatory especially at T2WI, diffusion & contrast images to exclude malignancy which can be easily missed due to multiple signal patterns described. Most common tumors at transitional zone is stromal sarcoma which could be typical showing T2 hypointense signal or atypical showing T2 hyperintense signal intensity.

Introduction: When analyzing the current situation in Spain, derived from our current lifestyles far from following a Mediterranean lifestyle, there is an alarming prevalence of diabesity, which increases the global risk of suffering from cardiovascular disease (CVD) and decreases the quality of life related to health (QLRH). In order to guarantee, beyond glycemic control of patients with type 2 diabetes (DM2), adequate control of cardiovascular risk factors (CVRF) in DM2, it will be necessary to carry out a community intervention focused on the application of education programs focused on promoting the acquisition of healthy eating habits through the Mediterranean diet (DMED). This dietary pattern, together with physical exercise, has been shown to contribute to improving the QLRH of patients. Objective: To analyze the quality of life related to health, present in patients with poorly controlled type 2 diabetes, to determine the possible relationship between this and the degree of adherence to the Mediterranean diet and to examine whether there are differences between the sexes. Material and methods: Observational descriptive study in 93 patients diagnosed with DM2 with poor glycemic control (1Ac ≥ 7%), carried out in various health centers in Albacete and Cuenca, in which the baseline relationship between adherence to DMed and the HRQoL. They were administered a data collection sheet that included a survey of the degree of adherence to the DMed (MEDAS-14) and QLRH (SF-12v2) in the Primary Care (PC) medical and nursing consultations. The variables were analyzed: age groups, sex, years of evolution of DM2, body mass index (BMI), as well as basal glycemia (GB) and glycosylated hemoglobin (HbA1c). The “MEDAS-14” (adherence to DMED) was the main variable and the “SF-12v2” (QLRH) was the secondary variable. Material and methods: Observational descriptive study in 93 patients diagnosed with DM2 with poor glycemic control (1Ac ≥ 7%), carried out in various health centers in Albacete and Cuenca, in which the baseline relationship between adherence to DMed and the HRQoL. They were administered a data collection sheet that included a survey of the degree of adherence to the DMed (MEDAS-14) and QLRH (SF-12v2) in the Primary Care (PC) medical and nursing consultations. The variables were analyzed: age groups, sex, years of evolution of DM2, body mass index (BMI), as well as basal glycemia (GB) and glycosylated hemoglobin (HbA1c). The “MEDAS-14” (adherence to DMED) was the main variable and the “SF-12v2” (QLRH) was the secondary variable. Results: Patients with poorly controlled DM2 and with low adherence to the MedDM show a non-significantly greater affectation in the physical sphere: fair general state of health, physical function I (limitation to make moderate efforts such as moving a table, vacuuming or walk more than an hour), physical function II (limitation to climb several flights of stairs), physical role (problems at work or daily activities doing less than loved ones), physical role II (they had to stop performing some tasks at work or activities of daily living) and regular body pain. Presenting less affectation in the mental field: emotional role I, emotional role II, mental health I, vitality and mental health II. Without affectation in the social function since they consider that rarely the emotional or physical problems have hindered their social activities. Conclusions: Diabetic patients with poor glycemic control have low adherence to the MedMD (<9 points) and have poor QLRH. Low adherence to the DMed and obesity are related to a greater affectation in the physical dimension and less affectation in the mental dimension without affectation in the "social function".

Different types of disasters, whether natural or human character, lead to the significant loss of human lives. In the latter case, the quick action of identification of corpses and human remains is mandatory. There are a variety of protocols to identify victims, however, genetics is one of the tools that allow an exact identification of the victim. However, several factors may interfere with this identification, from the biological samples’ degradation not allowing the analysis of nuclear information, to failure to dispose of biological samples from family members. Access to certain family members could be a determinant of the proper choice of genetic markers that allow the identification of the victim, or his/her inclusion in a given genetic maternal or paternal lineage. With the new advances in the genetic field, it is expected to allow soon the identification of victims from disasters only with his/her biological postmortem samples, being possible to draw a robot portrait and its most likely physical characteristics. In all cases, genetics is the only modern tool with universal character and can be used in essentially all biological samples, giving and identification of more or less accurate statistical character, depending on whether nuclear or lineage markers are used.

Proteinuria is known to be associated with all-cause and cardiovascular mortality, and nephrotic syndrome is defined by the level of proteinuria and hypoalbuminemia. With advances in medicine, new causative genes for genetic kidney diseases are being discovered increasingly frequently. We reviewed articles on proteinuria/nephrotic syndrome, focal segmental glomerulosclerosis, membranous nephropathy, diabetic kidney disease/nephropathy, hypertension/nephrosclerosis, Alport syndrome, and rare diseases, which have been studied in mouse models. Significant progress has been made in understanding the genetics and pathophysiology of kidney diseases thanks to advances in science, but research in this area is ongoing. In the future, genetic analyses of patients with proteinuric kidney disease/nephrotic syndrome may ultimately lead to personalized treatment options.

The adipose-derived mesenchymal stem cells are becoming the tool of choice for many clinical applications and nowadays, nearly 200 clinical trials are running worldwide to prove the efficacy of this cell type for many diseases and pathological conditions. To reach the goal of cell therapies and produce ATMPs as drugs for regenerative medicine, it is necessary to properly standardize the GMP processes and thus collection methods, transportation strategies, extraction protocols and characterization procedures without forgetting that all the tissues of the human body are characterized by a wide inter-individual variability genetically determined and acquired during life. Here we compare 302 samples processed under GMP rules to exclude the influence of the operator and of the anatomical site of collection. Variability in the age of patients, gender and laboratory parameters like total cell number, cell viability, stem cell number and other stromal vascular fraction cell sub-populations have been compared to each other. Results show that, when the laboratory protocol is standardized, the variability in quantifiable cell parameters is widely statistically non-significant, meaning that we can make a further step toward standardized advanced cell therapy products.

Cell-based therapy in regenerative medicine is a powerful tool that can be used both to restore various cell lost in a wide range of human disorders, and in renewal processes. Stem cells show promise for universal use in clinical medicine, potentially enabling the regeneration of numerous organs and tissues in the human body. This is possible due to their self-renewal capacity, and their ability to differentiate into various cell types. To date, pluripotent stem cells seem to be the most promising regeneration tool. Recently, a novel stem cell niche, called multilineage-differentiating stress-enduring (Muse) cells, is emerging. These cells are of particular interest because they are pluripotent and are found in adult human mesenchymal tissues. One of their most significant features is that they can produce cells representative of all the three germ layers. Furthermore, they can be easily harvested from fat, and isolated from the mesenchymal stem cells. This makes them very promising, allowing autologous treatments and avoiding the problems of rejection, typical of transplants. Muse cells have recently been employed, with encouraging results, in numerous preclinical studies, performed to test their efficacy in the treatment of various pathologies, exploiting their regenerative potential in different tissues. This systematic review aimed to 1) highlight the specific potential of Muse cells, and provide a better understanding of this niche, and 2) originate the first organized review of already tested applications of Muse cells in regenerative medicine. The obtained results could be useful to extend the possible therapeutic applications of Muse cells.

Objective: The Asia-Pacific region (APAC) represents a unique environment for the publication of biomedical research, particularly industry-funded research. Awareness and adoption of international guidelines on ethical publication practices continues to increase across APAC, but the reframing and expansion of many of the recommendations in the Good Publication Practice (GPP) 2022 guidelines versus GPP3 published in 2015 have important implications for publishing industry-funded biomedical research in the region. Methods: This manuscript provides practical guidance for stakeholders in APAC on interpreting and applying the recommendations made in the GPP 2022 guidelines. Results: Key focus areas include navigating new opportunities for communicating industry-funded research, such as plain language summaries, social media, and preprints; implementing formal processes to improve the integrity of published research in APAC; and methods of promoting transparency and inclusion when publishing industry-funded research. Key APAC-specific issues, including encore presentations, leadership on publication ethics in the region, access to professional resources, and support for educating regional stakeholders are also discussed. Conclusions: Overall, this review offers a pragmatic guide for stakeholders in industry-sponsored research on applying GPP 2022 in practice with a focus on effectively integrating these guidelines in an APAC context.

Knee Osteoarthritis (KOA), the most common knee degenerative disease, involve a slow destructive process, leading to disability and ultimately total knee replacement. The progression of KOA is related to the loss of rheological properties of the synovial fluid (SF), due to slow immunological, inflammatory and enzymatic processes that cleave the hyaluronic acid (HA) and decrease the concentration of specific proteins. Since no effective treatments have been found to halt the progression of KOA, injection of HA-based viscoelastic gels combined with physiotherapy (PT) is an alternative to symptomatic therapies. In order to evaluate the effect of viscosupplementation and PT on the SF characteristics, the SF aspirated from the KOA was spectrophotometrically and rheological analyzed, comparing the receiving groups of HA Kombihylan® and groups that received Kombihylan® and complex PT. In patients treated with PT, SF extracted 6 weeks after viscosupplementation had a superior elastic moduli (G') and viscous moduli (G") profile behavior, having a homogeneous distribution of proteins and polysaccharides in the SF, stimulating stronger interactions. In the absence of PT the G' and G" profiles are non-uniform, suggesting an unorganized supplemented SF with some clustering phenomena, proteins aggregation and a low level of entanglement between HA and macromolecular components in the SF.

As the world's population ages and the resulting health problems become more serious, medical and health policies in developed countries are focused on how to prevent and treat the diseases of the aging population and how to maintain their quality of life. Typical age-related diseases include deafness, cataracts, osteoarthritis, chronic obstructive pulmonary disease, diabetes mellitus, and dementia. Although the mechanisms by which these diseases develop differ, they are all caused by the accumulation of molecular and cellular damage over time. In addition, age-related diseases can cause a decline in physical and mental functions and the ability to perform activities of daily living, as well as the loss of roles in society and a sense of fulfillment in life. Therefore, there is a need for treatment and measures to accurately grasp and maintain their quality of life.

Growth-preserving instrumentation techniques are utilized in early-onset scoliosis (EOS) cases requiring surgical intervention. Shilla technique corrects the deformity and may reduce the need for surgical treatment with its growth-directing effect. As with other techniques, various prob-lems can be encountered following the administration of the Shilla technique. The aim of this study was to examine the effect of complications encountered with Shilla treatment on correction and growth. Sixteen patients with a follow-up period of at least 1 year who received Shilla growth guidance for EOS were included in the study. No complications occurred in 50% cases and no unplanned surgery was required. Of the remaining 8 patients with postoperative im-plant-related complications (50%), 6 (37.5%) required unplanned surgery; this consequently caused implant failure in the proximal region in 5 cases (31.25%) and deep tissue infection around the implant in 1 case (6.25%). Deformity correction, spine length, and quality-of-life scores significantly improved in EOS through Shilla growth guidance. Although implant-related com-plications were observed and related unplanned surgeries were required with Shilla growth guidance in our patients, these complications did not have a significant negative effect on correc-tion and spine growth.

Dietary iron assimilation is critical for health and essential to prevent iron deficient states and related comorbidities, such as anemia. The bioavailability of iron is limited, while its absorption and metabolism are tightly controlled to keep body iron stores within a relatively narrow range. Genetic inactivation of the iron hormone hepcidin causes hereditary hemochromatosis, an endocrine disorder of iron overload characterized by chronic hyperabsorption of dietary iron, with deleterious clinical complications if untreated. The impact of high dietary iron intake and elevated body iron stores in the general population is not well understood. Herein, we summarize epidemiological data suggesting that high intake of heme iron, which is abundant in meat products, poses a risk factor for metabolic syndrome pathologies, cardiovascular diseases, and some cancers. We discuss clinical relevance and potential limitations of data from cohort studies, as well as the need to establish causality and elucidate molecular mechanisms.

Attempts to use computers to aid in the detection of breast malignancies date back more than 20 years. Despite significant interest and investment, this has historically led to minimal or no significant improvement in performance and outcomes with traditional computer aided detection. However, recent advances in artificial intelligence and machine learning are now starting to deliver on the promise of improved performance. There are at present more than 20 FDA approved AI applications for breast imaging, but adoption and utilization are widely variable and overall low. Breast imaging is unique and has aspects that create both opportunities and challenges for AI development and implementation. Breast cancer screening programs worldwide rely on screening mammography to reduce the morbidity and mortality of breast cancer and many of the most exciting research and available AI applications focus on cancer detection for mammography. There are however multiple additional potential applications for AI in breast imaging including decision support, risk assessment, breast density quantitation, workflow and triage, quality evaluation, response to neoadjuvant chemotherapy assessment, and image enhancement. In this review the current status, availability, and future directions of investigation of these applications are discussed, as well as the opportunities and barriers to more widespread utilization.

Background: This paper examined the gap between obesity and its diagnosis for cohorts of patients with overweight, obesity, and morbid obesity in the Veterans Administration (VA) population. Using the risk adjustment models, it also identified factors associated with the underdiagnosis of obesity. Methods: Analysis was performed on a VA data set. We identified diagnosed patients and undiag-nosed patients (identified through BMI but not diagnosed by ICD-10 codes). The groups’ de-mographics were compared using nonparametric chi-square tests. We used logistic regression analysis to predict the likelihood of the omission of diagnosis. Results: Of the 2,900,067 veterans with excess weight, 46% were overweight, 46% had obesity, and 8% of them had morbid obesity. The overweight patients were the most underdiagnosed (96%), followed by the obese (75%) and morbidly obese cohorts (69%). Older, male, and White patients were more likely to be undiagnosed as overweight and obese; younger males were more likely to be undiagnosed as morbidly obese. (p<.05) Comorbidities significantly contributed to diagnosis. Conclusion: Underdiagnosis of obesity continues to be a significant problem despite its prevalence. Diagnosing obesity accurately is necessary to provide effective management and treatment.

There is great interest in thoracic kyphosis as it is thought to be a contributor to neck pain, neck disability, and sensorimotor control measures; though this has not been completely inves-tigated in treatment or case control studies. This case control design investigated participants with non-specific chronic neck pain. Eighty participants with a defined hyper-kyphosis (> 55°) were compared to 80 matched participants with normal thoracic kyphosis (< 55°). Participants were matched for age and neck pain duration. Hyper-kyphosis was further categorized into two distinct types: postural kyphosis (PK) and Scheuermann’s kyphosis (SK). Posture measures included formetric thoracic kyphosis and the craniovertebral angle (CVA) to assess forward head posture. Sensorimotor control was assessed by the following measures: smooth pursuit neck torsion test (SPNT), overall stability index (OSI) and left and right rotation repositioning accuracy. A measure of autonomic nervous system function included the amplitude and latency of skin sympathetic response (SSR). Differences in variable measures were examined using the Student’s t-test to compare the means of continuous variables between the two groups. Pearson correlation was used to evaluate the relationship between participant’s thoracic kyphosis magnitude (in each group separately and as an entire population) and their CVA, SPNT, OSI, head repositioning ac-curacy, and SSR latency and amplitude. Hyper-kyphosis participants had a significantly greater neck disability index compared to the normal kyphosis group (p < 0.001) with the SK group having greatest disability (p < 0.001). Statistically significant differences between the two kyphosis groups and the normal kyphosis group for all the sensorimotor measured variables were identified with the SK group having the most decreased efficiency of the measures in the hyper-kyphosis group, including: SPNT, OSI and left and right rotation repositioning accuracy. Also, there was a signif-icant difference in neurophysiological findings for SSR amplitude (entire sample of kyphosis vs. normal kyphosis, p < 0.001), but there was no significant difference for SSR latency (p = .07). The CVA was significantly greater in the hyper-kyphosis group (p < 0.001). The magnitude of the thoracic kyphosis correlated with worsening CVA (with the SK group having the smallest CVA; p < 0.001) and the magnitude of the decreased efficiency of the sensorimotor control measures and the amplitude and latency of the SSR. The PK group, overall, showed the greatest correlations between thoracic kyphosis and measured variables. Participants with hyper-thoracic kyphosis exhibited abnormal sensorimotor control and autonomic nervous system dysfunction compared to those with normal thoracic kyphosis.

The COVID-19 pandemic has resulted in an unprecedented acceleration in scientific production across multiple disciplines. The vast number of publications available makes it challenging for healthcare professionals and researchers to keep up with the current state of knowledge regarding COVID-19. This article presents covid19-help.org, a free expert-curated database designed to increase the availability of relevant original data related to COVID-19 treatment and prevention via immunization. To accelerate the process of identifying relevant original scientific publications and to simplify annotation of their content, the database uses our artificial intelligence in medical literature (AIM.lit) tool. The article provides an overview of the covid19-help.org database design, the criteria used to select publications, and the use of the AIM.lit tool. The database allows users to easily search and filter records, provides concise information on individual substances and their mechanisms of action, lists relevant original scientific publications with annotations, and offers links to external resources. The AIM.lit tool increases the speed of publication selection and extraction of basic relevant information, without compromising the validity of the data. The technology and experience gained from creating the covid19-help.org database and its tools could also be useful in other areas where scientific information organization is a challenge.

Background: Falls risk assessment tools are used in hospital inpatient settings to identify pa-tients at increased risk of falls (which may be related to muscle loss/sarcopenia) to guide and target interventions for falls prevention. In 2022, Western Health, Melbourne, Australia, intro-duced a new falls risk assessment tool, the Western Health St. Thomas’ Risk Assessment Tool (WH-STRATIFY), a modified version of The Northern Hospital’s risk tool (TNH-STRATIFY), which replaced the Peninsula Health Risk Screening Tool (PH-FRAT). Aims: To determine the predictive accuracy of three falls risk assessment tools (PH-FRAT, TNH-STRATIFY and WH-STRATIFY) on admission to Geriatric Evaluation Management (GEM) units. Method: A retrospective observational study was conducted on four GEM units. Data was col-lected on 54 consecutive patients who fell during admission and 62 randomly sampled patients who did not fall between December 2020 and June 2021. Participants were scored against three falls risk assessment tools. The event rate Youden (Youden IndexER) indices were calculated and compared using default and optimal cut points to determine which tool was most accurate for predicting falls. Results: Using default cut points to compare falls assessment tools, TNH-STRATIFY had the highest predictive accuracy (Youden IndexER = 0.20, 95% confidence interval CI = 0.07, 0.34). The PH-FRAT (Youden IndexER = 0.01 and 95% CI = -0.04, 0.05) and WH-STRATIFY (Youden IndexER = 0.00 and 95% CI = -0.04, 0.03) were statistically equivalent and not predictive of falls compared to TNH-STRATIFY. When calculated optimal cut points were applied, predictive accuracy im-proved for PH-FRAT (Cut point 17, Youden IndexER = 0.14 and 95% CI = 0.01, 0.29) and WH-STRATIFY (Cut point 7, Youden IndexER = 0.18 and 95% CI = 0.00, 0.35). Overall, all tools had low predictive accuracy for falls. Conclusion: TNH-STRATIFY had the highest predictive accuracy for falls. The predictive accu-racy of WH-STRATIFY improved and was significant when the calculated optimal cut point was applied. The optimal cut points of falls risk assessment tools should be determined and validated in different clinical settings to optimise local predictive accuracy, enabling targeted falls risk mitigation strategies and resource allocation.

By using the Sala-i-Martin version of extreme bounds analysis, which is based on the cumulative density function, cross-sectional evidence is presented on the robust determinants of the infection and case mortality rates of Covid-19. The results show that, for most part, different factors determine the infection and mortality rates in developed and developing countries. However, common factors are also apparent, including the risk environment, the number of tests per million and the percentage of population over 65. Notwithstanding the possibility that the official data represent a partial reflection of the truth, it is concluded that the results presented in this study provide useful information that may aid decision-making in the fight against the pandemic.

Abstract Introduction: The mHealth active participant centred (MAPC) adverse events following immunisation (AEFI) surveillance is a promising area for early AEFI detection resulting in risk minimisation. Passive (spontaneous) AEFI surveillance is the backbone for vaccine pharmacovigilance, but has inherent drawbacks of under reporting, and requires strengthening with active surveillance methods. The Zimbabwe stimulated telephone assisted rapid safety surveillance (Zm-STARSS) randomized controlled trial (RCT) sought to evaluate the efficacy and feasibility of AEFI detection using a short message service (SMS) and computer assisted telephone interview (CATI) approach. Method: A multicentre Zm-STARSS RCT enrolled consented adult vaccinees or parents or guardians of children receiving vaccines, including COVID-19 vaccines, at study vaccination clinics. At enrolment study participants were randomised to either SMS-CATI group or control group. SMS prompts were sent on days 0-2 and 14 post-vaccination to SMS-CATI group to ascertain if a medical event following immunisation (MEFI) had occurred. However, no SMSs were sent to the control group. SMS-CATI group who responded “Yes” to SMS prompts were interviewed by research healthcare workers (RHCWs) who completed a CATI to determine if an MEFI/AEFI had occurred whilst an AEFI in control group was determined from passive AEFI reporting channels. The primary study outcome was the AEFI detection rate in the SMS-CATI group compared to the control group. Results: A total of 704 (31%) participants responded to the SMS prompts, with 75% (528/704) indicating “No” and 25% (176/704) "Yes” to experiencing a MEFI. However, 31% (55/176) who responded “Yes” did not complete a CATI survey, but 69% (121/176) did; and 64% (77/121) of these indicated negative to AEFI experience whilst 36% (44/121) were affirmative. There were no AEFI reported in control group participants. Zm-STARSS showed promising results in that the AEFI detection rate using SMS-CATI was 2% (44/2280) on an intention to treat cohort. Conclusion: Despite the low SMS response and CATI completion rate, we demonstrated that Zm-STARSS SMS system improves AEFI detection compared to passive AEFI surveillance. We recommend cost-effective multi-channel digital approaches for holistic pharmacovigilance to improve AEFI detection in LMICs for all vaccines.

The Internet of Medical Things (IoMT) is playing a pivotal role in the healthcare sector by allowing faster and more informed hospital care, personalized treatment, and medical solutions. A very effective and trustworthy solution for resource-constrained medical devices is provided by Physical Unclonable Functions (PUF) - based identity and authentication systems, however they are not yet entirely reliable. This paper proposes VXorPUF, a Vedic Principles - Based Hybrid XOR Arbiter PUF. Modeling attacks were performed on the proposed architecture and an accuracy of 49.80 % was achieved. Uniqueness, Reliability and Randomness were the figures of merit used to evaluate PUF.

Acute appendicitis is the most common surgical emergency worldwide with peak incidence in the age range of 10 – 19 years, and the standard treatment procedure is the surgical removal of the appendix to avoid complications such as its rupture, peritonitis, and sepsis. The COVID-19 pandemic required the global population to stay indoors to stop the spread of virus, which led to a decline in the number of patients visiting the hospital for non- COVID -19 related emergencies. This is a case of a 9-year-old girl who tested positive for COVID -19 and developed acute appendicitis and benefited from individualised classical homeopathy. Further scientific investigation is necessary to establish the relevance of individualised classical homeopathy in treating serious acute conditions such as acute appendicitis.

Obstructive Sleep Apnea (OSA) is nowadays one of the respiratory pathologies with a higher in-cidence globally in developed countries. This situation led to an increase in the demand for medical appointments and diagnostic studies related to that condition, especially those based on poly-somnographies and cardiorespiratory polygraphies. These studies are limited in resources, causing long waiting lists with the subsequent impact on the patients’ health. Furthermore, it is necessary to mention that OSA’s symptomatology is not very specific, and it is typically present in the general population (excessive sleepiness, snore, etc.). In this regard, this paper proposes a novel intelligent clinical decision support system for the diagnosis of OSA which could be used to help medical teams, both in primary care settings and in units specialized in respiratory pathologies. The aim of the proposed system is to help discriminate the patients suspected of suffering from the pathology from those who are not. To this end, two types of information sets of heterogeneous nature are consid-ered. The first one encompasses objective data, related to the patient's health profile with infor-mation usually available in electronic health records. The second type comprises subjective data, referred to the symptomatology reported by the patient in a previous interview. To process the first group of information, a Machine Learning classification algorithm is used, Bagged Trees in this case. For processing the second information set, related with the symptomatology of the patient, a col-lection of expert systems based on fuzzy inferential systems arranged in cascade are employed. As a result, the system is able to determine two risk indicators related to the patient's risk of suffering from OSA: the Statistical Risk and the Symbolic Risk respectively. Subsequently, by interpreting both risk indicators mentioned it will be possible to determine the severity of the patients’ health, proposing a preliminary evaluation on their condition. For the initial tests of the system, a software artifact has been built using a dataset with 4,978 selected patients, suspected of suffering from OSA, from the Álvaro Cunqueiro Hospital in Vigo. The results obtained are promising, demonstrating the potential usefulness of this type of tools in medical diagnosis. Once the system has been validated with new data from clinical environments, it is considered as possible to obtain a relevant improvement in the quality of the healthcare services, and a reduction in the associated costs.

Background: Bibliometric analysis provides insight into knowledge gaps of a specific field. We want to know what part of medical care has been the subject of research in a group of Mexican physicians. The Instituto de Seguridad y Servicios Sociales de los Trabajadores del Estado (ISSSTE) cares for a wide spectrum of disease and it provides a unique vision of what specialists has investigated about health. Methods: Papers belonging to “ISSSTE” affiliation were harvested and downloaded to .csv Excel files from the Scopus database including most bibliometric variables. VOSviewer, biblioshiny and bibliometric were used to conduct the bibliometric. Results: 2,063 papers were found and re-trieved; internal medicine had the greatest number of papers with 831; nine institutions were listed claiming “ISSSTE” as their mother affiliation; original papers represent 82% of the total and 52% of them were written in Spanish. Research production in Mexico City and the most productive center is Centro Médico Nacional 20 de Noviembre. Discussion: We identified main institutions, prolific authors, top-cited researchers and their affiliations, however, our paper is a call to action for the medical community in Latin America to join our efforts in building a solid group of researchers for the future of science.

The increasing attendance of paediatric emergency departments becomes a serious health issue. To reduce an elevated burden of medical errors, inevitably caused by high level of stress exerted on emergency physicians, we propose potential areas for improvement in regular paediatric emergency departments. In effort to guarantee demanded quality of care to all incoming patients, the workflow in paediatric emergency departments should be sufficiently optimized. The key component remains implementing one of the validated paediatric triage systems upon patient's arrival at emergency department and fast-tracking patients with low level of risk according to the triage system. To ensure the patient's safety, emergency physicians should follow issued guidelines. Cognitive aids, such as well-designed checklists, posters or flow charts, generally improve physicians' adherence to guidelines and should be therefore available in every paediatric emergency department. To sharpen diagnostic accuracy, the use of ultrasound in paediatric emergency department, according to ultrasound protocols, should be targeted to answer specific clinical questions. Combining all mentioned improvements might reduce number of errors linked with overcrowding. The review serves not only as a blueprint for modernizing paediatric emergency departments, but also as a bin of useful literature which can be suitable in the paediatric emergency field.

Introduction: Adverse events related to vaccines are reactions that can lead to serious complications, in the present that we live at the publication of this article with the need to generate high rates of vaccination have been presented in the case of vaccine agents against COVID-19 adverse events, with thrombotic events being the most important. Case Presentation: This is a 37-year-old female patient who presents 11 days after ChAdOx1-S vaccination against COVID-19 presents with intense headache associated with symptoms of intracranial hypertension, she was initially taken to simple cranial tomography showing presence of cerebral edema and indirect signs of cerebral venous thrombosis, it is proposed to carry out cerebral angiography which showed the presence of thrombosis of both transverse venous sinuses, no hypercoagulability states were found in the laboratories, the immunological tests carried out for antiphospholipid syndrome were negative, the thrombotic event was associated with the ChAdOx1-S vaccine, later the patient was taken to thrombectomy by Neuroradiology, achieving complete channeling of both transverse venous sinuses. Conclusions: Adverse events associated with vaccination have been described with multiple vaccine agents, this component against COVID-19 is also an event to consider, knowing the pathophysiology of these events is essential to be able to reduce these risks and select the least risky agent. Despite this risk, vaccination remains the only cost-effective measure to reduce the morbidity and mortality associated with COVID-19.

Innovation development in healthcare has paved the path towards the improvement in system efficiency, patient care and cost-effective healthcare services, increasing the overall efficiency of the healthcare system in a significant manner. Electronic medical record system has fostered interoperability and collaboration in healthcare departments by enabling different systems to exchange and use patient data. This trend has been accelerated dominantly by the pandemic, leading to increased use of telemedicine, referring specifically to the use of electronic communication to provide healthcare services remotely. Moreover, an increased prevalence towards the use of artificial intelligence and machine learning in healthcare has contributed effectively towards the rigorous and informed analysis of large amounts of data as well as to identify patterns or trends that may be useful for improving care delivery or identifying potential issues. A unified electronic medical record System that will generate competitive opportunities and success prospects with reference to enhanced interoperability in US healthcare departments, can contribute towards the improvement in the quality and efficiency of care by making it easier for healthcare providers to access and share patient information as well as reducing the risk of errors in effective patient care.

ChatGPT is gaining widespread acceptance for its ability to generate natural language sentences in response to various inputs and is expected to become a supplementary tool for diagnosing and determining treatment policies in clinical settings. ChatGPT was used to evaluate its ability to perform clinical inference and accuracy in answering questions on the 117th Japanese National Medical Licensing Examination held in February 2023. The exam questions were manually inputted into ChatGPT's window, and the accuracy of ChatGPT's responses was determined based on answers provided by a preparatory school. ChatGPT provided answers for 389 out of 400 questions, and its overall correct answer rate was 55.0%. The correct answer rate for 5-choice-1, 5-choice-2, and 5-choice-3 were 57.8%, 42.9%, and 41.2%, respectively. The highest correct answer rate was for the compulsory exam (67.0%), followed by the specific knowledge exam (54.1%) and the cross category exam (47.9%). The correct answer rate for non-image questions was 56.2% and for image questions, it was 51.5%. The study suggests that ChatGPT has potential to support healthcare professionals in clinical decision-making in Japanese clinical settings, but caution should be exercised in interpreting and using the answers generated by ChatGPT due to room for improvement in performance.

The COVID-19 pandemic has presented an unprecedented challenge to the healthcare system. Identifying the genomics and clinical biomarkers for effective patient stratification and management is critical to controlling the spread of the disease. Omics datasets provide a wealth of information that can aid in understanding the underlying molecular mechanisms of COVID-19 and identifying potential biomarkers for patient stratification. Artificial intelligence (AI) and machine learning (ML) algorithms have been increasingly used to analyze large-scale omics and clinical datasets for patient stratification. In this manuscript, we demonstrate the recent advances and predictive accuracies in AI and ML-based patient stratification modeling linking omics and clinical biomarker datasets, focusing on COVID-19 patients. Our ML model not only demonstrates that clinical features are enough an indicator of COVID-19 severity and survival but also infers what clinical features are more impactful, which makes our approach a useful guide for clinicians for prioritization of best-fit therapeutics for a given cohort of patients. Moreover, with weighted gene network analysis, we are able to provide insights into gene networks that have a significant association with COVID-19 severity and clinical features. Finally, we have demonstrated the importance of clinical biomarkers in identifying high-risk patients and predicting disease progression.

Artificial Intelligence (AI) play an important role in many fields, including medical education, practice, and research. Many medical educators started using ChatGPT at the end of 2022 for many purposes. The aim of this article was to explore the potential uses, benefits and risks of using ChatGPT in integrated pharmacotherapy of infectious disease module education. This study was an experimental study with content analysis of the potential applications of the ChatGPT model for integrated pharmacotherapy of infectious diseases module education was performed. The findings of this study shows that there are many potential uses, benefits and risks associated with the use ChatGPT. In conclusion, medical and health sciences educators can use ChatGPT as a guide in many aspects related to the integrated pharmacotherapy of infectious disease curriculum development, syllabus design, lecture notes preparation and exam preparation with caution.

Simulation-Based Medical Education that uses Virtual Patients has become increasingly important during the COVID-19 pandemic. With the need for social distancing and minimizing contact, medical simulation technology has provided a valuable tool for healthcare professionals to practice and improve their skills without the need for face-to-face interactions. MedSIM is a medically accurate simulation platform with Virtual Patients designed for undergraduate medical education. Our study involved two groups of students. The PreCOVID group, before the pandemic, underwent conventional teaching methods. The COVID group, during the pandemic, had students exposed to conventions skills taught earlier and were taught again with MedSIM. Students indicated high satisfaction with the clinical skills demonstrated by the Virtual Patients. More than half agreed that MedSIM had enabled them to perform all kinds of procedures on patients (PreCOVID group 68.8%, COVID group 71.3%), showed cues and consequences much like those in natural clinical environments (PreCOVID 68.4%, COVID 71.3%). Also, MedSIM allowed them to have a repetitive practice that helps in critical skills transfer to actual patients (PreCOVID 72.7%, COVID 74.7%). MedSIM met the expectations of most of the students. Students from both groups rated the online performance of the MedSIM simulator as "Very good." Analysis from a customized word cloud indicated that most students found MedSIM to be good and of educational value. MedSIM platform enhances healthcare professionals' skills and knowledge, leading to better patient outcomes and increased access to healthcare, supporting SDG 3 (Good Health and Well-being). It also provides a safe and controlled environment for healthcare professionals to learn and practice essential skills, supporting SDG 4 (Quality Education).

The mechanistic target of rapamycin (mTOR) kinase is one of the top drug targets for promoting health and lifespan extension. Besides rapamycin, only a few other mTOR inhibitors have been developed and shown their ability to slow aging. We used machine learning to predict novel small molecules targeting mTOR. We selected one small molecule, TKA001, based on in-silico predictions of a high on-target probability, low toxicity, favorable physicochemical properties, and preferable ADMET profile. We confirmed TKA001 binding in silico by molecular docking. TKA001 potently inhibits both TOR complex 1 and 2 downstream signaling in vitro. Furthermore, TKA001 inhibits human cancer cell proliferation in vitro and extended the lifespan of C. elegans, suggesting that TKA001 can slow aging in vivo.

Predicting Length of Stay (LoS) and understanding its underlying factors is essential to minimize the risk of hospital-acquired conditions, improve financial, operational, and clinical outcomes, and to better manage future pandemics. The purpose of this study is to forecast patients’ LoS using a deep learning model and analyze cohorts of risk factors minimizing or maximizing LoS. We employed various pre-processing techniques, SMOTE-N to balance data, and Tab-Transformer model to forecast LoS. Finally, Apriori algorithm was applied to analyze cohorts of risk factors influencing LoS at hospital. The Tab-Transformer outperformed the base Machine Learning models with an F1-score (.92), precision (.83), recall (.93), and accuracy (.73) for discharge dataset, and F1-score (.84), precision (.75), recall (.98), and accuracy (.77) for deceased dataset. The association mining algorithm was able to identify significant risk factors/indicators belonging to lab, X-Ray, and clinical data such as elevated LDH, and D-Dimer, lymphocytes count, and comorbidities such as hypertension and diabetes responsible for extending patients LoS. It also reveals what treatments has reduced the symptoms of COVID-19 patients leading to reduction in LoS particularly when no vaccines or medication such as Paxlovid were available.

Diffuse optical tomography (DOT) is a medical imaging procedure using light to measure the geometric and working properties of cells, for instance, oxygen consumption, water content, and fat percentage in the tissue by performing three-dimensional visualization of the tissue. This paper aims to explain the theory behind diffuse optical tomography imaging and how the technology works. The paper explains how photon migration techniques based on diffusion theory can be used to image the optical properties of tissue. There are several reasons why near-infrared (NIR) imaging is the most effective method in terms of recovering optical parameters quantitatively in the near-infrared region. The author discusses the methods in detail. This research also presents various advantages, practical uses, and potential problems that have been related to DOT in this work. There is also a brief discussion of current research developments in medical imaging using near-infrared wavelengths, and what the future holds for that area.

Accurate image registration is essential for eye movement compensation in optical coherence tomography (OCT) and OCT angiography (OCTA). The spatial resolution of an OCT instrument is typically anisotropic, i.e., different resolutions in the lateral and axial dimensions. When OCT images have anisotropic pixel resolution, residual distortion (RD) and false translation (FT) were always observed after image registration for rotational movement. In this study, RD and FT were quantitively analyzed over different degrees of rotational movement and various lateral and axial pixel resolution ratio (RL/RA) values. The theoretical analysis confirmed that the RD and FT increase significantly with the rotation degree and RL/RA. An image resizing assisting registration (RAR) strategy was proposed for accurate image registration. The performance of direct registration (DR) and RAR for retinal OCT and OCTA images were quantitatively compared. Experimental results confirmed that unnormalized RL/RA causes RD and FT, RAR can effectively improve the performance of OCT and OCTA image registration and distortion compensation.

Longitudinal image registration of pulmonary computed tomography (PCT) images may serve as an essential tool for investigating the relationship between radiation dose distribution and the occurrence and phenotype of radiation-induced lung disease (RILD). Although numerous longitudinal registration algorithms have been developed for PCT, most similarity-based approaches are not suitable for PCT involving RILD due to the complex tissue variation between two PCT images. Moreover, conventional feature-based approaches might fail to find a sufficient number of matched pairs of feature points due to the disparate lung deformation caused by breathing and RILD. To overcome the challenges resulting from RILD, component structure coherence point drift (CSCPD) was proposed to establish a deformation model by decomposing the chest into several components and matching them with individual parameters based on coherence point drift (CPD). Moreover, a regional vascular point matching (RVPM) algorithm was proposed to generate a vascular subtree and to substantially increase the number of corresponding pairs between two images. Eventually, the components were recomposed and aligned by a thin plate spline algorithm. A performance assessment on 15 pairs of PCT images of patients with RILD yielded recall and precision values of 0.85 and 0.89 for RVPM, respectively. Moreover, the target registration error of CSCPD with RVPM (2.3 ± 1.79) was significantly better than that of conventional CPD with RVPM (2.95 ± 1.89) and conventional CPD (5.04 ± 2.87). Therefore, the proposed registration system is robust enough to address the disparate deformation of lungs with RILD, and it improves registration accuracy within the parenchyma.

The optical properties and physiology of biological tissue, as well as how near-infrared (NIR) light interacts with the tissue, both play a significant role in interpreting the tissue probing optical measurements, and in solving the inverse problem of near-infrared spectroscopy (NIRS)-based medical imaging modalities such as diffuse optical tomography and functional near-infrared spectroscopy. This paper discusses the optical properties of tissue, specifically in the NIR wavelength range, which influence NIRS measurements in NIRS-based medical imaging. There is an easy-to-understand explanation given in this paper of the NIR light-tissue interaction phenomenon. The mathematical explanation, the processes involved in the interaction, and the rationale for a few approximations are described. Various types of chromophores present in the tissue, their composition in the tissue, and how these chromophores overall affect the scattering and absorption of NIR light are presented. The absorption spectra of these chromophores are shown. Finally, the paper concludes with the author’s perspective on two NIRS-based medical imaging modalities, diffuse optical tomography, and functional near-infrared spectroscopy.

Recently, there has been increasing attention toward inhaled nanoparticles (NPs) to develop in-halation therapies for diseases associated with the pulmonary system and investigate the toxic ef-fects of hazardous environmental particles on human lung health. Taking advantage of microfluidic technology for cell culture applications, lung-on-a-chip devices with great potential in replicating the lung air-blood barrier (ABB) have opened new research insights in preclinical pathology and therapeutic studies associated with aerosol NPs. Surprisingly, the air interface in such devices, which makes them tremendously unique among the other common liquid-based cellular mi-crosystems and enables exposure of cultivated cells to the drug/toxic aerosols, has been largely disregarded. Accordingly, there exists a significant research gap in the comprehensive under-standing of the NPs’ dynamics in the lung-on-a-chip devices, which is momentous to control the injection of aerosols to the target area and attain a desired physiochemical efficacy. To address this research gap, a numerical parametric study is presented to provide a deep insight into the dynamic behavior of the airborne NPs in a gas-liquid dual-channel lung-on-a-chip device with a porous membrane separating the channels. A finite element multi-physics model is developed to enable a particle tracing investigation simultaneously in both air and medium phases to replicate the in vivo conditions avoiding laborious and costly experimental trials. The results include the impact of fluid flow as well as geometrical properties on the distribution, deposition, and translocation of the NPs with diameters ranging from 10 nm to 900 nm in the lung-on-a-chip. The obtained results strongly suggest the aerosol injection of NPs instead of the aqueous solution for more efficient deposition on the substrate of the air channel and higher translocation to the media channel. The current study proposes to optimize the affecting parameters to control the injection and delivery of aerosol par-ticles into the lung-on-chip device depending on the objectives of biomedical investigations and provides optimized values for some specific cases. Therefore, this study can assist scientists and researchers in complementing their experimental investigation in future preclinical studies on pulmonary pathology associated with inhaled hazardous and toxic environmental particles, as well as therapeutic studies for developing inhalation drug delivery.

Musculoskeletal models (MSKMs) are used to estimate the muscle and joint forces involved in human locomotion, often associated with the onset of degenerative musculoskeletal pathologies (e.g. osteoarthritis). Subject-specific MSKMs offer more accurate predictions than their scaled-generic counterparts. This accuracy is achieved through time-consuming personalisation of models and manual tuning procedures that suffers from potential repeatability errors, hence limiting the wider application of this modelling approach. In this work we have developed a methodology for identifying and ranking the muscles that are more important to the determination of the joint forces, thus producing reduced but still accurate representation of the musculoskeletal system in shorter timeframes. The methodology hinges on Sobol's sensitivity analysis (SSA) for ranking the muscle importance. The thousands of data points required for SSA are generated using Gaussian Process emulators, a Bayesian technique to infer the input-output relationship between nonlinear models from a limited number of observations. Results show that there is a pool of muscles whose personalisation has little effects on the model predictions. Furthermore, joint forces in subject generic and subject generic models are influenced by different set of muscles, suggesting the existence of a model specific component of the sensitivity analysis.

Fluorescence microscopy is an important tool for disease diagnosis, often requiring costly optical components, such as fluorescence filter cubes and high-power light sources. Due to its high cost, conventional fluorescence microscopy cannot be fully exploited in low-income settings. Smartphone-based fluorescence microscopy becomes an interesting low-cost alternative, but raises challenges in the optical system. We present the development of a low-cost inverted laser fluorescence microscope, that uses a smartphone to visualize the fluorescence image of biological samples. Our fluorescence microscope uses a laser-based simplified optical filter system, that provides analog optical filtering capabilities of a fluorescence filter cube. Firstly, we validated our inverted optical filtering by visualizing microbeads labeled with three different fluorescent compounds or fluorophores, commonly used for disease diagnosis. Secondly, we validated the disease diagnosis capabilities, by comparing the results of our device with those of a commercial fluorescence microscope. We successfully detected and visualized Trypanosoma cruzi parasites, responsible of the Chagas infectious disease, and the presence of Antineutrophil cytoplasmic antibodies of the ANCA non-communicable autoimmune disease. The samples were labeled with the fluorescein isothiocyanate (FITC) fluorophore, one of the most commonly used for disease diagnosis. Our device provides a 400 X magnification and is at least two orders magnitude cheaper than conventional commercial fluorescence microscopes.

The unprecedented growth of publicly available SARS-CoV-2 genome sequence data has increased demand for effective and accessible SARS-CoV-2 data analysis and visualization tools. A majority of the currently available tools either require computational expertise to deploy or limit user input to pre-selected subsets of SARS-CoV-2 genomes. To address these limitations, we developed ViralVar, a publicly available, point-and-click webtool that gives users the freedom to investigate and visualize user-selected subsets of SARS-CoV-2 genomes obtained from the GISAID public database. ViralVar has two primary features that enable: 1) visualization of spatiotemporal dynamics of SARS-CoV-2 lineages, and 2) structural/functional analysis of genomic mutations. As proof-of-principle, ViralVar was used to explore the evolution of the SARS-CoV-2 pandemic in the USA in the pediatric, adult, and elderly population (n > 1.7 million genomes). While the spatiotemporal dynamics of variants did not differ between these age groups, several USA-specific sublineages arose relative to the rest of the world. Our development and utilization of ViralVar to provide insights on the evolution of SARS-CoV-2 in the USA demonstrates the importance of developing accessible tools to facilitate and accelerate large-scale surveillance of circulating pathogens. The ViralVar webserver is freely available at http://viralvar.org/.

The origins of directed acyclic graphs (DAGs) date back to the emergence of ‘graph theory’ in the early 1700s (Biggs et al. 1986). DAGs are conceptual or literal, diagrammatic representations of causal paths between variables which are constructed – as their name suggests – on the basis of two over-riding principles: first, that all causal paths are ‘directed’ (i.e. for each pair of variables, only one can represent the cause, while the other must be its consequence); and second, that no direct cyclical paths, or indirect cyclical pathways (comprising sequences of consecutive paths) are allowed, such that no consequence can be considered its own direct or indirect cause (hence ‘acyclic’; Law et al., 2012). As such DAGs reflect the knowledge, presumptions, assumptions and/or speculation of the analyst(s) concerned regarding the causal relationships between each of the variables included therein. Current convention dictates that variables are represented as nodes/vertices, and that any causal paths between variables are represented as directed arcs/edges/lines, often in the form of arrows (see Figure 1). Although each arc indicates the presence and direction of a known/presumed/assumed/speculative causal relationship between the two variables concerned, drawing an arc does not require the sign, magnitude, precision or shape of the relationship to be known or declared (Tennant et al., 2021). In this respect, DAGs provide a simple, uncomplicated, accessible and entirely nonparametric approach for postulating causal relationships amongst any variables of interest even when these are uncertain, unknown or entirely speculative (Ellison, 2020). Nonetheless, as a result of the parametric constraints imposed by the presence/absence of possible arcs within any given DAG, these also reflect and support a number of more sophisticated statistical applications which make it possible to use DAGs to inform the design of multivariable statistical models that reflect the causal structure(s) involved – albeit without the need to know or understand the mathematical technicalities on which these are based (Lewis and Kuerbis, 2016). These features make DAGs attractive cognitive, educational and analytical tools for strengthening the epistemological, theoretical and empirical basis of causal inference, and there has been a recent proliferation in the use of DAGs across a range of applied scientific disciplines (e.g. Knight and Winship, 2013), and an associated upsurge in analytical methods training (e.g. Elwert, 2011; Gilthorpe, 2017; Hernán 2018; Roy, 2021; Hünermund, 2021). This Chapter reflects on a decade of delivering medical statistics training to undergraduate medical students at the University of Leeds between 2012-2021 in which the third year research, evaluation and special studies module (‘RESS3’) has used DAGs to support the development of applied statistical skills relevant to the extended student-selected research and evaluation projects (ESREP) students undertake in their fourth and final years (Ellison, 2021; Ellison et al., 2014a,b). Based on successive iterations of the structure and content of the RESS3 module, together with notes made during formal and informal planning and review meetings with module leads, lecturers, tutors and students, we draw on the claims and criticisms made of DAGs in the epidemiological literature to identify a number of explicit strengths (and associated, often implicit. weaknesses) that are central to their use in prediction and causal inference modelling. While using DAGs requires (and benefits from) a clear understanding of their non-parametric nature and parametric implications, the weaknesses of DAGs seem likely to reflect both: the challenges inherent in the modelling of data generating processes when these are imperfectly understood; and troublesome cognitive and heuristic tendencies common to all analytical tools – in which the tool facilitates the task in hand by reducing the necessity (and benefits of) exploring uncertainties and identifying assumptions. These, more epistemological considerations appear particularly challenging for medical undergraduates to grasp (Ellison, 2021), but also appear poorly understood by many established analysts and clinical epidemiologists (Ellison, 2020).

Recent lines of evidence suggest the intriguing hypothesis that consuming common culinary herbs of the mint family might help prevent or treat Covid. Individual citizens could easily explore the hypothesis using ordinary kitchen materials. I offer a philosophical framework to account for the puzzling lack of public health messaging about this interesting idea.

One of the biggest challenges in arm prosthesis design is to resemble normal hand functionality. Some of arm prosthesis try to mimic all hand movement, while other try to produce several main hand grip patterns. We design a Versatile Gripping Technology (VGT) from a basic whippletree mechanism to produce main hand grip patterns in arm prosthesis. VGT development is the result of abstraction, negation, and systemic variation process of existing solution. To validate VGT, we test it to produce six pattern of hand grip from Southampton Hand Assessment Procedure. VGT capable to produce lateral, power, tip, extension, and spherical grip. In other hand, VGT including the thumb movement and it only use one simple shape moving part. Thus, VGT is effective and efficient. With this result, we argue that abstraction, negation, and systemic variation process of existing solution can create a novel solution and it is applicable in another problem or domain.

Background: COVID-19 made its debut as a pandemic in 2020; since then, more than 607 million cases and at least 6.5 million deaths have been reported worldwide. While the burden of disease has been described, the long-term effects or chronic sequelae are still being described. Objective: To describe the findings of a current systematic review of the long-term effects related to post-COVID-19 sequelae. Design: A systematic review was carried out in which cohort studies, case series, clinical case reports were included, and the PubMed, Scielo, SCOPUS and Web of Science databases were ex-tracted. Information published 2020 to June 1, 2022, was sought. Results: We reviewed 300 manuscripts during the first step of the literature review process. Then 260 abstracts were analyzed. In the end, we included 32 manuscripts: 9 for pulmonary, 6 for cardiac, 2 for renal, 9 for neurological and psychiatric, and 8 for cutaneous sequelae. Conclusion: Studies show that the most common sequelae are those linked to the lungs, followed by skin, cutaneous and psychiatric alterations. Women report a higher incidence of the sequelae, as well as those with comorbidities and severer COVID-19 history. The COVID-19 pandemic has not only caused death and disease since its apparition but has also sickened millions of people around the globe who potentially suffer from serious illnesses that will continue to add to the list of health problems and further burden healthcare systems around the world.

This study is a cross-sectional survey involving physicians around Indonesia aimed to explore physician hesitancy to treat COVID-19 patients after experiencing COVID-19 infection coupled with associated occupational risk factors. The questionnaire was distributed via contact information from the Indonesian Physician Association database. Out of 383 participants, 25.6% suffered from moderate symptoms of COVID-19 and 2.9% were admitted for critical care. Hesitancy to treat suspected, probable, or confirmed COVID-19 patients was found in 20.3% of physicians. A higher hesitancy rate was found in older physicians and those with less experience in treating COVID-19 patients. Specialist trainees and those who work in public hospitals were physicians with the lowest hesitancy in treating COVID-19 patients. There is a significant hesitancy in treating COVID-19 patients among physicians who have suffered from COVID-19 which calls for further action by management and policy makers.

Interprofessional collaboration between general practitioners (GPs) and community pharmacists (CPs) is central to implement antimicrobial stewardship (AMS) programs in primary care. This study aimed to design a GP-pharmacist antimicrobial stewardship (GPPAS) model in Australian primary care. A seven-component exploratory study was conducted since 2017 to 2021 to inform a GPPAS model. We generated both secondary and primary evidence through a systematic review, a scoping review, a rapid review, nationwide surveys of Australian GPs and CPs including qualitative components and a pilot study of a GPPAS model. All study evidence was synthesised, reviewed, merged and triangulated to design a prototype GPPAS model using a Systems Engineering Initiative for Patient Safety theoretical framework. Secondary evidence informed effective GPPAS interventions, and primary evidence captured interprofessional issues, challenges and future needs to implement GPPAS interventions by GPs and CPs. A GPPAS model framework involving GP-pharmacist team-based five GPPAS sub-models were successfully designed to foster AMS education, antimicrobial audits, diagnostic stewardship, delayed prescribing, and routine review of antimicrobial prescription by improved GP-CP collaboration. A GPPAS model could be used as a guide to collaboratively optimise antimicrobial use by GPs and CPs. Implementation studies on GPPAS model and sub-models are required to integrate GPPAS model into GP-pharmacist interprofessional care models in Australia.

Purpose: We assessed if a CXR AI algorithm can detect missed or mislabeled chest radiographs (CXRs) findings in radiology reports. Methods: We queried multi-institutional radiology reports search database of 13-million reports to identify all CXRs reports with addendums from 1999-2021. Of the 3469 CXR reports with an addendum, a thoracic radiologist excluded reports where addendum was created for typographic errors, wrong report template, missing sections, or uninterpreted signoffs. The remaining reports with addendum (279 patients) with errors related to side-discrepancy or missed findings such as pulmonary nodules, consolidation, pleural effusions, pneumothorax, and rib fractures. All CXRs were processed with an AI algorithm. Descriptive statistics were performed to determine the sensitivity, specificity, and accuracy of AI to detect missed or mislabeled findings. Results: AI had high sensitivity (96%), specificity (100%), and accuracy (96%) for detecting all missed and mislabeled CXR findings. The corresponding finding-specific statistics for AI were nodules (96%, 100%, 96%), pneumothorax (84%, 100%, 85%), pleural effusion (100%, 17%, 67%), consolidation (98%, 100%, 98%), and rib fractures (87%, 100%, 94%). Conclusion: The CXR AI could accurately detect mislabeled and missed findings. Clinical Relevance: The CXR AI can reduce the frequency of errors in detection and side-labeling of radiographic findings.

Heart rate (HR) and respiratory rate (RR) are two vital parameters of the body medically used for diagnosing short/long term illness. Out-of-the-body, non-contact HR/RR measurement remains a challenge due to imprecise readings. “Invisible” wearables integrated into day-to-day garments has the potential to produce precise readings with comfortable user experience. Sleep studies and patient monitoring benefit from “Invisibles” due to longer wearability without significant discomfort. This paper suggests a novel method to reduce the footprint of sleep monitoring devices. We use a single silver-coated nylon fabric band integrated into a substrate of standard cotton/nylon garment as a resistive elastomer sensor to measure air and blood volume change across the chest. We introduce a novel event-based architecture to process data at the edge device and describe two algorithms to calculate real-time HR/RR on ARM Cortex-M3 and Cortex-M4F microcontrollers. RR estimations show a sensitivity of 99.03% and a precision of 99.03% for identifying individual respiratory peaks. The two algorithms used for HR calculation show a mean absolute error of 0.81±0.97 and 0.86±0.61 beats/minute compared to a gold standard ECG-based HR. The event-based algorithm converts the respiratory/pulse waveform into instantaneous events, therefore, reducing the data size by 40-140 times and requires 33% less power to process and transfer data. Further, we show that events hold enough information to reconstruct the original waveform, retaining pulse, and respiratory activity. We suggest fabric sensors and event-based algorithms would drastically reduce the device footprint and increase the performance for HR/RR estimations during sleep studies providing better user experience.

Photoplethysmography is a widely used technique to noninvasively assess heart rate, blood pressure, and oxygen saturation. This technique has a large potential for further applications, for example in the field of physiological and mental health monitoring. However, advanced applications of photoplethysmography have been hampered by the lack of accurate and reliable methods to analyze the characteristics of the complex nonlinear dynamics of the photoplethysmogram. Methods of nonlinear time series analysis may be used to estimate the dynamical characteristics of the photoplethysmogram but they are highly influenced by the length of the time series, which is often limited in practical photoplethysmography applications. The aim of this study was to evaluate the error in the estimation of the dynamical characteristics of the photoplethysmogram associated to the limited length of the time series. The dynamical properties were evaluated using recurrence quantification analysis, and the estimation error was computed as a function of the length of the time series. Results demonstrated that properties such as de-terminism and entropy can be estimated with an error lower than 1% even for short photople-thysmogram recordings. Additionally, the lower limit for the time series length to estimate the average prediction time was computed.

This study assesses the efficacy of a microwave colonoscopy algorithm to detect colorectal cancer precursors or polyps in an ex-vivo human colon model. The algorithm works with a device composed of a cylindrical ring-shaped switchable antenna array, which can be attached to the tip of a conventional colonoscope as an accessory. The accessory is connected to an external processing unit that generates an acoustic signal when a polyp is detected. Nowadays, 22% of polyps go undetected with conventional colonoscopy and the risk of cancer after a negative colonoscopy can be up to 7.9%. Fifteen ex-vivo freshly excised human colons with cancer (n=12) or polyps (n=3) were examined with the microwave-assisted colonoscopy system simulating a real colonoscopy exploration. Successive measurements of the colon were taken with the microwave-based colonoscopy device and processed with a microwave imaging algorithm. After the experiment, the dielectric properties of the specimens were measured with a coaxial probe and finally the samples underwent a pathology analysis. The results show that all the neoplasms were detected with a sensitivity of 100% and specificity of 87.4%.

False positives on multiparametric (mp)-MRI result in a large number of unnecessary biopsies in men with clinically insignificant diseases. This study investigates whether quantitative diffusion MRI can improve differentiation between false positives, true positives and normal tis-sue. Twenty-three patients underwent mp-MRI and Vascular, Extracellular and Restricted Diffu-sion for Cytometry in Tumours (VERDICT)-MRI, followed by transperineal biopsy. The patients were categorised into two groups following biopsy: (1) significant cancer - true positive (2) atro-phy/inflammation/high-grade prostatic intraepithelial neoplasia (PIN) - false positive. The clinical apparent diffusion coefficient (ADC) values of the lesions were obtained, and the intravoxel inco-herent motion (IVIM), diffusion kurtosis imaging (DKI) and VERDICT models were fitted using a deep learning approach. Significant differences (p < 0.05) between true positive and false positive lesions were found in ADC, IVIM perfusion fraction (f) and diffusivity (D), DKI diffusivity (DK) and kurtosis (K) and VERDICT intracellular volume fraction (fIC), extracellular-extravascular vol-ume fraction (fEES) and diffusivity (dEES) values. Significant differences between false positives and normal tissue were only found for the VERDICT fIC. These results demonstrate that model-based diffusion MRI could reduce the number of unnecessary biopsies due to false positive prostate lesions and shows promising sensitivity to benign diseases that mimic cancer.

Artificial intelligence is dramatically transforming medical imaging. We assessed the levels of artificial intelligence use among radiology trainees and explored their perceived impact of artifi-cial intelligence on the radiology workflow and radiology profession, in correlation with the perceived ease of use and behavioral intention to use artificial intelligence. This cross-sectional study enrolled radiology trainees from Saudi Arabia, and an online 5-part-structured question-naire was disseminated via online networks to trainee in July 2021. We included 98 participants (51 male; age 27.59±2.02 years). Level of use was low; few used it in routine practice (7%). The impact of artificial intelligence on the radiology workflow was positively perceived in all radi-ology workflow steps (3.64–3.97 out of 5). A positive impact on the radiology profession was more frequently perceived for technical and performance aspects (81%–85%) compared with prestige and legal aspects (64%–71%). Perceived ease of use and behavioral intention to use arti-ficial intelligence was associated with the current professional activity, level of use artificial in-telligence use, and perceived impact on the profession as well as on radiology workflow (p<0.05). Levels of artificial intelligence use in radiology are very low. The perceived positive impact of ar-tificial intelligence on radiology workflow and profession is correlated with an increase in be-havioral intention to use artificial intelligence. Thus, increasing awareness about the favorable impact can improve the behavioral use.

Providing healthcare workers with cost information about the medications they prescribe can influence their decisions, particularly when that information is provided at the very moment they are faced with a prescribing decision. The current study aimed to analyse the impact of nudges that presented cost information to prescribers through their organisation's electronic prescribing system. The nudges were co-created by the research team (composed of behavioural scientists) and the lead hospital pharmacist. One nudge provided simple cost information (percentage difference between two brands of Mesalazine – Asacol and Octasa). The second nudge provided the potential annual cost-saving that could result if the cheaper medication was selected across the organisation. While the statistical analyses revealed that these nudges were not effective, several administrative barriers were overcome, which may inform future research. For example, presenting aggregated cost information to the prescribers is possible even when the actual cost of medicine is confidential and can not be displaced. In addition, future research could reveal more behavioural factors that facilitate medication optimisation.

Competency frameworks outline the perceived knowledge, skills and other attributes required for professional practice. Competency frameworks have gained in popularity, in part for their ability to inform health professions education, assessment, professional mobility, and other activities. Previous research has shown inadequate reporting within reports describing their development and that may jeopardize their use and application. We aimed to develop a set of minimum criteria that provides guidance to authors (and consumers) in an effort to improve reporting of the development of competency frameworks. The checklist was developed by a 35-member expert panel and a five-member research team following published guidance from the Enhancing the QUAlity and Transparency Of health Research (EQUATOR) Network. The final checklist contains 20 essential reporting items including guidance on reporting title and abstract, framework development, the development process, testing and funding/conflicts of interest. The intent of the COmpeteNcy FramEwoRk Development in Health Professions (CONFERD-HP) reporting guideline is to help readers (including researchers, educators, regulators, health professionals, and patients) develop a greater understanding of relevant terminology, core concepts, and key items to report for competency framework development in health professions.

Gas chromatography-coupled mass spectrometry (GC-MS) has been used in biomedical research to analyze volatile, non-polar, and polar metabolites in a wide array of sample types. Despite advances in technology, missing values are still common in metabolomics datasets and must be properly handled. We evaluated the performance of ten commonly used missing value imputation methods with metabolites analyzed on an HR GC-MS instrument. By introducing missing values into the complete (i.e., data without any missing values) NIST plasma dataset we demonstrate that Random Forest (RF), Glmnet Ridge Regression (GRR), and Bayesian Principal Component Analysis (BPCA) shared the lowest Root Mean Squared Error (RMSE) in technical replicate data. Further examination of these three methods in data from baboon plasma and liver samples demonstrated they all maintained high accuracy. Overall, our analysis suggests that any of the three imputation methods can be applied effectively to untargeted metabolomics datasets with high accuracy. However, it is important to note that imputation will alter the correlation structure of the dataset, and bias downstream regression coefficients and p-values.

Therapeutic patient education (TPE) aims to empower the patients and their caregivers to effectively care for and manage their conditions. Such educational programs have been shown to improve health behaviors, disease outcomes and quality of life among different patient populations. The field of TPE has evolved extensively over decades, owing to interdisciplinary research. No study so far has been done to map this field, to identify the stakeholders and gaps requiring future research. By leveraging the theory of co-citation, Citespace was used to visualize the bibliographic data pertaining to TPE research. A total of 54,533 articles published in English language were analyzed to identify influential funders, regions and institutes contributing to this field. Besides these, important theoretical and empirical work that has shaped this field has also been mapped. Our analysis revealed several important insights. Most of the important theories which helped shape TPE were inspired from the social sciences. Five important themes of research were identified including disorders, study designs utilized in TPE research, scope of the TPE literature, and outcomes, and populations. The research focused on improving perceptions, behaviors, and attitudes for health promotion, reducing stigma as well as self-management and medication adherence. Most of the research was developed in the context of high income countries. The future research should more involve patients and use digital technology. Meta-analytical studies need to be done to identify the specificities of TPE interventions across different disorders. More research should involve low and middle income countries (LMIC) to ensure knowledge and technology transfer.

Combining Matrix-Assisted Laser Desorption/Ionization Time-of-Flight (MALDI-TOF) spectra data and artificial intelligence (AI) has been introduced for rapid prediction on antibiotic susceptibility test (AST) of S. aureus. Based on the AI predictive probability, the cases with probabilities between low and high cut-offs are defined as “grey zone”. We aimed to investigate the underlying reasons of unconfident (grey zone) or wrong predictive AST. A total 479 S. aureus isolates were collected, analyzed by MALDI-TOF, and AST prediction, standard AST were obtained in a tertiary medical center. The predictions were categorized into the correct prediction group, wrong prediction group, and grey zone group. We analyzed the association between the predictive results and the demographic data, spectral data, and strain types. For MRSA, larger cefoxitin zone size was found in the wrong prediction group. MLST of the MRSA isolates in the grey zone group revealed that uncommon strain types composed 80%. Amid MSSA isolates in the grey zone group, the majority (60%) was composed of over 10 different strain types. In predicting AST based on MALDI-TOF AI, uncommon strains and high diversity would contribute to suboptimal predictive performance.

In addition to the classical steroids, which have cholesterol as a precursor, there are steroids with 7-dehydrocholesterol as a precursor. This review describes the identification of these steroids, their biosynthesis and some aspects of their function. There are three classes of these compounds, distinguished by the number of their carbon atoms, 23, 24 and 25. Each class has a spiral steroid and is a phosphodiester. Up until these investigations, no spiral steroids or steroid phosphodiesters were known. There are at least 13 compounds of which six have been purified to near homogeneity. Each one has been characterized by its mass and proposed composition. They function by regulating the NaK-ATPase. Based on the tissues in which they have been detected, each class of compound seems to regulate a different isoform of the NaK-ATPase. This is an important site of endocrine regulation.

During the COVID-19 pandemic, conducting face-to-face medical residency interviews was challenging due to infection prevention precautions, social distancing and travel restrictions. Virtual interviews were implemented by the Saudi Commission for Health Specialties (SCFHS) as an alternative process for residency matching while striving to maintain the same quality standards. This national survey was conducted to assess the satisfaction and perceptions of faculty members’ virtual interviews performance in the assessment for the medical training residency programs. Among the participating 173 faculty members (34.1%) did not have previous experience with video-conferencing. Zoom application was the most commonly used platform (65.9%). Most (89.6%) of the faculty perceived virtual interviews as “adequate” for the candidates to express themselves, while almost half of the faculty (53.8%) agreed that virtual interviews allowed them to accurately reach an impression about the candidates. Overall, 73.4% of faculty felt comfortable ranking the virtually interviewed candidates. We conclude that the acceptance of participating faculty members in the first Saudi medical residency training matching cycle virtual interviewing event was well perceived. This study provides evidence for future application and research of virtual interviews in residency candidates' assessment, especially after the pandemic crisis resolves.

Sanitisation has become a major component of everyday life, with emphasis on the hands and surfaces. The face remains unsanitised due to the lack of an acceptable sanitiser. The use of masks has been mandated to reduce the spread of the pathogens by covering the face, however, there remain issues with the use of personal protective equipment. The face remains a harbour for upper respiratory tract infections, with constant deposition of microbes. By reducing microbial load, the risk of both infection and severity are reduced. HOCl has proven antimicrobial and anti-inflammatory activity, including efficacy against SARS-CoV-2. A facial sanitiser, alongside hand sanitisers and masks, improves protection against SARS-CoV-2. The advantages of regular sanitising of the face and mask include reduced level of microbial contamination, risk of biofilm formation, and respiratory tract and skin infections. HOCl was reviewed as a face and mask sanitiser, concluding that it was an ideal product.

Background: Clean cookstove interventions can theoretically reduce exposure to household air pollution and benefit health, but this requires near-exclusive use of the stoves, with simultaneous disuse of traditional stoves. Previous cookstove trials have reported low adoption of new stoves and/or extensive continued traditional stove use. Methods: The Household Air Pollution Intervention Network (HAPIN) trial randomized 3195 pregnant women in Guatemala, India, Peru, and Rwanda either to a liquefied petroleum gas (LPG) stove and fuel intervention (n = 1590) or to control (n = 1605). The intervention consisted of an LPG stove and two initial cylinders of LPG, free fuel refills delivered to the home, and regular behavioral messaging. We assessed intervention fidelity (delivery of the intervention as intended) and adherence (intervention use) through the end of gestation, as relevant to the first primary health outcome of the trial: infant birth weight. Fidelity and adherence were evaluated using stove and fuel delivery records, questionnaires, visual observations, and temperature-logging stove use monitors (SUMs). Results: 1585 women received the intervention, at a median (interquartile range) of 8.0 (5.0–15.0) days post-randomization and gestational age of 17.9 (15.4–20.6) weeks. Over 96% reported cooking exclusively with LPG at two follow-up visits during pregnancy. Less than 4% reported ever running out of LPG. Complete abandonment of traditional stove cooking was observed in over 67% of intervention households. 31.4% removed their traditional stoves upon receipt of the intervention, and among those who retained traditional stoves, the majority did not use them: traditional stove use was detected via SUMs on a median (interquartile range) of 0.0% (0.0%, 1.6%) of follow-up days (median follow-up = 134 days). Conclusions: Fidelity of the HAPIN intervention, as measured by stove installation, timely ongoing fuel deliveries, and behavioral reinforcement as needed, was high. Exclusive use of the intervention during pregnancy was also high.

Three-dimensional convolutional neural networks (3D CNN) as a type of artificial intelligence (AI) are powerful in image processing and recognition using deep learning to perform generative and descriptive tasks. The advantage of CNN compared to its predecessors is that it automatically detects the important features without any human supervision. 3D CNN are used to extract features in three dimensions where input is a 3D volume or a sequence of 2D pictures e.g., slices in a cone-beam computer tomography scan (CBCT). The main aim of this article was to bridge interdisciplinary cooperation between forensic medical experts and deep learning engineers. With emphasis activating clinical forensic experts in the field with possibly basic knowledge of advanced artificial intelligence techniques with interest in its implementation in their efforts to advance the forensic research further. This paper introduces a novel workflow of 3D CNN analysis of full-head CBCT scans. Authors explore and present 3D CNN method for forensic research design concept in five perspectives: (1) sex determination, (2) biological age estimation, (3) 3D cephalometric landmark annotation, (4) growth vectors prediction, (5) facial soft-tissue estimation from the skull and vice versa. In conclusion, 3D CNN application can be a watershed moment in forensic medicine, leading to unprecedented improvement of forensic analysis workflows based on 3D neural networks.

Background: Observational health data has the potential to be a rich resource to inform clinical practice and regulatory decision making. However, the lack of standard data quality processes makes it difficult to know if these data are research ready. The EHDEN COVID-19 Rapid Col-laboration Call presented the opportunity to assess how the newly developed open-source tool Data Quality Dashboard (DQD) informs the quality of data in a federated network. Methods: 15 Data Partners (DPs) from 10 different countries worked with the EHDEN taskforce to map their data to the OMOP CDM. Throughout the process at least two DQD results were collected and compared for each DP. Results: All DPs showed an improvement in their data quality between the first and last run of the DQD. The DQD excelled at helping DPs identify and fix conformance is-sues but showed less of an impact on completeness and plausibility checks. Conclusions: This is the first study to apply the DQD on multiple, disparate databases across a network. While study-specific checks should still be run, we recommend that all data holders converting their data to the OMOP CDM use the DQD as it ensures conformance to the model specifications and that a database meets a baseline level of completeness and plausibility for use in research.

Obstructive sleep apnea (OSA) is a common sleep disorder, and continuous positive airways pressure (CPAP) is the most effective treatment. Poor adherence is one of the major challenges in CPAP therapy. The recent boom of wearable optical sensors measuring oxygen saturation makes the at-home multiple-night CPAP titrations possible, which may essentially improve the adherence of CPAP therapy by optimizing its pressure in a real-life setting economically. We tested whether the oxygen desaturations (OD) measured in the arm muscle (arm_OD) by gold-standard frequency-domain multi-distance near-infrared spectroscopy (FDMD-NIRS) changes with titrated CPAP pressures in OSA patients together with polysomnography. We found that the arm_OD (2.08 ± 1.23%, mean ± standard deviation) was significantly smaller (P-value &lt;0.0001) than the fingertip OD (finger_OD) (4.46 ± 2.37%) measured by polysomnography pulse oximeter. Linear mixed-effects models suggested that CPAP pressure was a significant predictor for finger_OD but not for arm_OD. Since FDMD-NIRS measures a mixture of arterial and venous OD, whereas fingertip pulse oximeter measures arterial OD, our results of no association between arm_OD and finger_OD indicate that the arm_OD mainly represented venous desaturation. Arm_OD measured by near-infrared optical sensors may be not a suitable indicator of the effectiveness of CPAP titration.

Sleep deprivation (failure to get enough sleep) is a public health issue that can negatively impact our body including cognitive function. Many studies have been done in Saudi Arabia to evaluate the impact of poor sleep and academic performance but almost all of them were done at university level. To investigate the relationship between sleep quality and general degree of academic achievement in school students. This is a cross-sectional, school-based study, conducted at Jazan Region, Saudi Arabia. A total of 957 participants (615 male and 342 female) were recruited from a state school. The study used a standardized, confidential, validated self-administered questionnaire to assess sleep quality and habits. By using Statistical Package for Social Studies (SPSS 22), achievement of the students was significantly related to the following parameters; laziness and fatigue after wake-up time and during school time, lack of concentration during school time, difficulty in complete tasks during school time and inability to maintain wakefulness during school time. Furthermore, students with poor academic degrees reported more significant incidence of sleeping at school, excessive daytime sleepiness, and other sleep related disorder than good academic degrees. Students with poor sleep quality have lower school grades.

Background: Heat waves are correlated with increased mortality in the aged population. Social isolation is known as a vulnerability factor. This study aims at evaluating the correlation between an intervention to reduce social isolation and the increase in mortality in the population over 80 during heat waves. Methods: The study adopts a retrospective ecologic design. We compared the excess mortality rate (EMR) in the over 80 population during heat waves in urban areas of Rome (Italy), where a program to reduce social isolation was implemented compared to others where it was not implemented. We measured mortality of the summer periods from 2015 to 2019 compared with 2014 (a year without heat waves). Winter mortality, cadastral income and proportion of over 90 were included in the multivariate Poisson regression. Results: The EMR in the intervention and controls was 2.70% and 3.81%, respectively. Rate ratio 0.70 (c.i. 0.54 - 0.92, p-value 0.01). The Incidence Rate Ratio (IRR) of the interventions with respect to the controls is 0.76 (c.i. 0.59 - 0.98). After adjusting for other variables, the IRR was 0.44 (c.i. 0.32 - 0.60). Conclusions: Reducing social isolation could limit the impact of heat waves on the mortality of the elderly population.

International migrants comprised 14% of the UK population in 2020, but migrant health in the UK has rarely been studied at a population level using primary care electronic health records (EHRs). Given the difficulty of determining migration status using EHRs, this study developed a migration phenotype and assessed its validity. We developed a phenotyping algorithm using codes for country of birth, visa status, non-English main/first language and non-UK origin. It was applied to a Clinical Practice Research Datalink (CPRD) GOLD database of 16,071,111 primary care patients between 1997 and 2018. We compared the completeness and representativeness of the identified migrant population to Office for National Statistics (ONS) country of birth and 2011 census data by year, age, sex, geographic region of birth and ethnicity. Between 1997-2018, 403,768 migrants (2.51% of the CPRD GOLD population) were identified using the phenotype. 178,749 (1.11%) of these migrants were identified by codes indicating foreign country of birth or visa status, 216,731 (1.35%) a non-English main/first language, and 8,288 (0.05%) non-UK origin. The cohort was similarly distributed compared to ONS migration statistics in terms of sex and region of birth. Recording of migration improved from identifying approximately one-tenth of the expected proportion of migrants according to the ONS in 2004 to a quarter in 2018. Younger migrants were better represented than those aged 50 and over. The migration phenotype identified a large number of migrants and can be used to undertake large-scale migration health research in CPRD GOLD to inform healthcare policy, practice and action. While the cohort was representative of the UK migrant population in terms of sex and region of birth, migration status was under-recorded in earlier years and older ages, and future studies for these groups should therefore be interpreted with caution.

This study aims to investigate the changes in the structural relationship between alienation in physical education (PE) class, school happiness, and future healthy life expectancy in Korean adolescents after the COVID-19 pandemic. The data were collected from adolescents in the Republic of Korea using scales for these factors. The collected data were analyzed with frequency analysis, reliability analysis, validity analysis, independent t-test, and path analysis. The key results were as follows. First, there were partial changes in each of the parameters since the outbreak of COVID-19. Second, before the pandemic, alienation in PE class negatively affected school happiness, and school happiness positively affected future healthy life expectancy; however, alienation in PE class did not affect future healthy life expectancy showing a complete mediating effect. Third, during the pandemic, alienation in PE class negatively affected school happiness, and school happiness positively affected future healthy life expectancy; alienation in PE class negatively affected future healthy life expectancy, showing a partial mediating effect. These findings emphasize the importance and potential of school education, especially PE, in promoting happiness and healthy life in adolescents. We expect these findings to have practical implications for future research by presenting theoretical and empirical data.

The Ministry of Health (MOH) in the Kingdom of Saudi Arabia (KSA) established a National School-based Screening Program (NSBSP) for health screening of school-going children. Students from specific grades were systematically screened for several health problems including obesity, visual and auditory problems, dental cavities, scoliosis, and attention-deficit/hyperactivity disorder (ADHD). This cross-sectional study aimed to determine the prevalence of these health problems among primary school students based on secondary data obtained from the NSBSP. We included 444,259 screened school children from the 1st and 4th grades of 50% of the selected schools (both private and public) across the KSA during the academic year 2018-2019. Among them, the most prevalent health problems identified were dental cavities (38.7%), eye refractory errors (10.9%), and overweight and obesity (10.5%); the less prevalent problems included ADHD (2.81%), auditory problems (0.6%), and scoliosis (0.48%). A greater prevalence of most health problems was observed in girls than boys. The NSBSP successfully aided the detection of health conditions with high and low prevalence among primary school students in the KSA, and thus, identification of health problems of specific concern. Implementation of effective school health services for the prevention, early detection, diagnosis, and treatment of these health problems is imperative.

The intricate nature of congenital heart disease requires understanding of complex, patient-specific three-dimensional dynamic anatomy of the heart, from imaging data such as three-dimensional echocardiography for successful outcomes from surgical and interventional procedures. Conventional clinical systems use flat screens and therefore display remains two-dimensional, which undermines the full understanding of the three-dimensional dynamic data. Additionally, control of three-dimensional visualisation with two-dimensional tools is often difficult, so used only by imaging specialists. In this paper we describe a virtual reality system for immersive surgery planning using dynamic three-dimensional echocardiography, which enables fast prototyping for visualisation such as volume rendering, multi-planar reformatting, flow visualisation, and advanced interaction such as three-dimensional cropping, windowing, measurement, haptic feedback, automatic image orientation, and multi-user interactions. The available features were evaluated by imaging and non-imaging clinicians, showing that the virtual reality system can help improve understanding and communication of the three-dimensional echocardiography imaging and potentially benefit congenital heart disease treatment.

Keywords: Chemical Intolerance, Drug Intolerance, Food Intolerance, QEESI, BREESI, Multiple Chemical Sensitivity, Toxicant-induced Loss of Tolerance, Prevalence

Introduction: Due to COVID-19 pandemic, performance of many program has been declined and Tuberculosis (TB) program is not an exception. TB case detection and notification has been recognized as one of worst hit area. The objective of this study was to explore the TB notification status of India during this pandemic and explore options to mitigate the issue. Methods: A secondary data analysis was performed on open source TB notification database of India. Relevant literature review was done to find out remedies based on the different initiative taken by different states of India. Results: In 2020, total TB notification decreased in all the states in comparison to 2019. The percentage of loss in the country was 34%. Private TB notification also decreased in 2020 in all the states except in Jharkhand. The percentage of loss in private TB notification in the country was 35%. Notification started declining in the month of February 2020 and it was lowest in the month of April-2020. The trend of notification began to improve since May 2020 when the States started taking innovative initiatives like Integrated TB Covid Case Search. Conclusion: Due to the ongoing COVID-19 pandemic the notifications of TB cases declined noticeably which has a serious implication in terms of silent spread within household and community. But the picture can be improved with integrated approach for TB-COVID case finding and management.

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines, using machine learning, deep learning and neural networks. AI enables machines to learn from experience and perform human-like tasks. The field of AI research has been developing fast over the past five to ten years, due to the rise of ‘big data’ and increasing computing power. In the medical area, AI can be used to improve diagnosis, prognosis, treatment, surgery, drug discovery, or for other applications. Therefore, both academia and industry are investing a lot in AI. This review investigates the biomedical literature (in the PubMed and Embase databases) by looking at bibliographical data, observing trends over time and occurrences of keywords. Some observations are made: AI has been growing exponentially over the past few years; it is used mostly for diagnosis; COVID-19 is already in the top-5 of diseases studied using AI; the United States, China, United Kingdom, South Korea and Canada are publishing the most articles in AI research; MIT is the world’s leading university in AI research; and convolutional neural networks are by far the most popular deep learning algorithms at this moment. These trends could be studied in more detail, by studying more literature databases or by including patent databases. More advanced analyses could be used to predict in which direction AI will develop over the coming years. The expectation is that AI will keep on growing, in spite of stricter privacy laws, more need for standardization, bias in the data, and the need for building trust.

Although automated Acute Lymphoblastic Leukemia (ALL) detection is essential, it is challenging due to the morphological correlation between malignant and normal cells. The traditional ALL classification strategy is arduous, time-consuming, often suffers inter-observer variations, and necessitates experienced pathologists. This article has automated the ALL detection task, employing deep Convolutional Neural Networks (CNNs). We explore the weighted ensemble of deep CNNs to recommend a better ALL cell classifier. The weights are estimated from ensemble candidates' corresponding metrics, such as accuracy, F1-score, AUC, and kappa values. Various data augmentations and pre-processing are incorporated for achieving a better generalization of the network. We train and evaluate the proposed model utilizing the publicly available C-NMC-2019 ALL dataset. Our proposed weighted ensemble model has outputted a weighted F1-score of 88.6%, a balanced accuracy of 86.2%, and an AUC of 0.941 in the preliminary test set. The qualitative results displaying the gradient class activation maps confirm that the introduced model has a concentrated learned region. In contrast, the ensemble candidate models, such as Xception, VGG-16, DenseNet-121, MobileNet, and InceptionResNet-V2, separately produce coarse and scatter learned areas for most example cases. Since the proposed ensemble yields a better result for the aimed task, it can experiment in other domains of medical diagnostic applications.