A systematic review of measures of healthcare workers' vaccine confidence

Healthcare workers (HCW) perceptions toward vaccines influence patient and community vaccine decision making. In an era of rising vaccine hesitancy, understanding HCW vaccine confidence is critical. This systematic review aims to review instruments that have been validated to measure HCW vaccine confidence. We conducted a search in five databases in June 2023. Data was descriptively synthesized. Twelve articles describing 10 different tools were included. Most tools included dimensions or items on vaccine knowledge (n = 9), safety (n = 8), vaccine usefulness (n = 8), recommendation behavior (n = 8), and self-vaccination practice (n = 7). All, except one study, were conducted in high-income countries. There was variability in the quality of the validation process. There is limited existing literature on development and validation of tools for HCW vaccine confidence. Based on the tools currently available, the Pro-VC-Be tool is the most well validated. Further research needs to include low- and middle-income contexts.

The effectiveness of rural community health workers in improving health outcomes during the COVID-19 pandemic: a systematic review

BACKGROUND

Rural community health workers [CHWs] play a critical role in improving health outcomes during non-pandemic times, but evidence on their effectiveness during the COVID-19 pandemic is limited. There is a need to focus on rural CHWs and rural health systems as they have limited material and human resources rendering them more vulnerable than urban health systems to severe disruptions during pandemics.

OBJECTIVES

This systematic review aims to describe and appraise the current evidence on the effectiveness of rural CHWs in improving access to health services and health outcomes during the COVID-19 pandemic in low-and middle-income countries [LMICs].

METHODS

We searched electronic databases for articles published from 2020 to 2023 describing rural CHW interventions during the COVID-19 pandemic in LMICs. We extracted data on study characteristics, interventions, outcome measures, and main results. We conducted a narrative synthesis of key results.

RESULTS

Fifteen studies from 10 countries met our inclusion criteria. Most of the studies were from Asia [10 of 15 studies]. Study designs varied and included descriptive and analytical studies. The evidence suggested that rural CHW interventions led to increased household access to health services and may be effective in improving COVID-19 and non-COVID-19 health outcomes. Overall, however, the quality of evidence was poor due to methodological limitations; 14 of 15 studies had a high risk of bias.

CONCLUSION

Rural CHWs may have improved access to health services and health outcomes during the COVID-19 pandemic in LMICs but more rigorous studies are needed during future pandemics to evaluate their effectiveness in improving health outcomes in different settings and to assess appropriate support required to ensure their impact at scale.

Facilitators and barriers to online group work in higher education within health sciences - a scoping review

INTRODUCTION

In health education, group work is essential to prepare students for working in health care and medical teams. Following the widespread adoption of online teaching, group work increasingly takes place in online environments. Although successful group work can provide good learning outcomes, it is unclear what facilitates or hinders online group work in health science education, and to what extent this topic has been addressed. Thus, this scoping review aimed to identify the facilitators and barriers to online group work in higher health education, provide an overview of the scientific literature related to the topic, and identify knowledge gaps in the research.

METHODS

This scoping review was guided by the methodological framework described by Arksey and O'Malley, and reporting is in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Review (PRISMA-ScR). Eight online databases were searched for scientific articles published between 2012 and 2022. At least two researchers independently screened records and full-text articles and charted data including article characteristics and key information related to the research question. Findings were categorized and summarized based on the Community of Inquiry Framework.

RESULTS

After screening 3671 records and 466 full-text articles, 39 articles met the inclusion criteria. The review revealed smaller group size, consistency in group composition and joint responsibility to be facilitators. Challenges with group communication, scheduling synchronous meetings and technical issues were identified as barriers. Our findings supported the importance of all three elements of the Community of Inquiry Framework: social, cognitive, and teaching presence.

CONCLUSION

This review provides an overview of facilitators and barriers to online group work in health science education. However, there is a need for further investigation of these factors and studies addressing this topic from the teachers' perspective.

Research hotspots and trends regarding microRNAs in hypertension: a bibliometric analysis

To investigate the research levels, hotspots, and development trends regarding microRNAs in hypertension, this study conducted a visual analysis of studies on miRNA in hypertension based on the Web of Science core collection database using CiteSpace and VOSviewer analysis software along with literature from 2005-2023 as information data. Using citation frequency, centrality, and starting year as metrics, this study analyzed the research objects. It revealed the main research bodies and hotspots and evaluated the sources of literature and the distribution of knowledge from journals and authors. Finally, the potential research directions for miRNAs in hypertension are discussed. The results showed that the research field is in a period of vigorous development, and scholars worldwide have shown strong interest in this research field. A comprehensive summary and analysis of the current research status and application trends will prove beneficial for the advancement of this field.

Current status and development trends in CKD with frailty research from 2000 to 2021: a bibliometric analysis

INTRODUCTION

The prevalence of chronic kidney disease (CKD) is gradually increasing in the elderly population. At the same time, frailty has become one of the research hotspots in the field of geriatrics. Bibliometric analyses help to understand the direction of a field. Therefore, this study aimed to analyze the status and emerging trends of frailty in CKD patients.

DATA AND METHODS

The Web of Science Core Collection (WoSCC) database was screened for relevant literature published between 1 January 2000 and 31 December 2021. Next, publications were analyzed for information including authors, journals, cited references, citing journals, institutions, countries and regions, high-frequency keywords and co-citations using VOSviewer, Microsoft Excel, and R software.

RESULTS

A total of 2223 articles were obtained, from which 613 relevant articles were selected based on title and abstract screening. There was an upward trend in the number of annual publications and Johansen KL was considered the most contributing author in the field. The Clinical Journal of the American Society of Nephrology was the most productive research journal. Johns Hopkins University is the most published organization. The United States is the global leader in the field and contributes the most to research. Research hotspots focus on epidemiological studies of frailty and frailty intervention.

CONCLUSIONS

This study presents a comprehensive bibliometric analysis of CKD and frailty research. Key findings highlight the current focus on early screening and assessment of frailty in CKD patients, as well as physical function interventions in frail patients.

Causal Role of Immune Cells in Chronic Obstructive Pulmonary Disease: A Two-Sample Mendelian Randomization Study

Accumulating evidence has highlighted the importance of immune cells in the pathogenesis of chronic obstructive pulmonary disease (COPD). However, the understanding of the causal association between immunity and COPD remains incomplete due to the existence of confounding variables. In this study, we employed a two-sample Mendelian randomization (MR) analysis, utilizing the genome-wide association study database, to investigate the causal association between 731 immune-cell signatures and the susceptibility to COPD from a host genetics perspective. To validate the consistency of our findings, we utilized MR analysis results of lung function data to assess directional concordance. Furthermore, we employed MR-Egger intercept tests, Cochrane's Q test, MR-PRESSO global test, and "leave-one-out" sensitivity analyses to evaluate the presence of horizontal pleiotropy, heterogeneity, and stability, respectively. Inverse variance weighting results showed that seven immune phenotypes were associated with the risk of COPD. Analyses of heterogeneity and pleiotropy analysis confirmed the reliability of MR results. These results highlight the interactions between the immune system and the lungs. Further investigations into their mechanisms are necessary and will contribute to inform targeted prevention strategies for COPD.

The association of metabolic dysfunction-associated steatotic liver disease (MASLD) with the risk of myocardial infarction: a systematic review and meta-analysis

Objective While studies have documented how metabolic dysfunction-associated steatotic liver disease (MASLD) can contribute to cardiovascular disease (CVD), whether MASLD is associated with myocardial infarction (MI) remains debateable. Herein, we systematically reviewed published articles and performed a meta-analysis to determine the relationship between MASLD and MI risk.Methods PubMed, MEDLINE, Embase, Web of Science, CNKI, CBM, VIP, and WanFang databases were searched, and the DerSimonian Laird method was used to obtain hazard ratios (HRs) for binary variables to assess the correlation between MASLD and MI risk. Subgroup analyses for the study region, MASLD diagnosis, quality score, study design, and follow-up time were conducted simultaneously for the selected studies retrieved from the time of database establishment to March 2022. All study procedures were independently conducted by two investigators.Results The final analysis included seven articles, including eight prospective and two retrospective cohort studies. The MI risk was higher among MASLD patients than among non-MASLD patients (HR = 1.26; 95% CI: 1.08-1.47, p = 0.003). The results of the subgroup analysis of the study region revealed an association of MASLD with MI risk among Americans and Asians, but not in Europeans. Subgroup analyses of MASLD diagnosis showed that ultrasonography and other (fatty liver index[FLI] and computed tomography [CT)]) diagnostic methods, but not international classification of disease (ICD), increased the risk of MI. Subgroup analysis of the study design demonstrated a stronger relationship between MASLD and MI in retrospective studies but not in prospective studies. Subgroup analysis based on the follow-up duration revealed the association of MASLD with MI risk in cases with < 3 years of follow-up but not with ≥3 years of follow-up.Conclusion MASLD increases the risk of MI, independent of traditional risk factors.

Thresholds of handgrip strength for all-cause mortality in patients with chronic kidney disease: a secondary systematic review with dose-response meta-analysis

BACKGROUND

Although handgrip strength is associated with all-cause mortality in patients with chronic kidney disease (CKD), whether this relationship is dose-related is unknown. Therefore, we examined dose-response relationships between handgrip strength and all-cause mortality in CKD patients based on previous studies by meta-analysis.

METHODS

Data sources included three electronic databases (PubMed, Web of Science, and Embase) from inception through October 2023. The included cohort was a CKD population not limited to disease stage, and their handgrip strength was objectively measured. Two researchers independently screened studies, extracted data, and assessed the risk of bias. We utilized estimates of handgrip strength categories using robust-error meta-regression (REMR), pooled study-specific estimates, and established dose-response relationships. Outcomes of interest included only all-cause mortality.

RESULTS

A total of 18 studies with 4810 participants (aged 47-71 years) were included. REMR modeling showed a U-shaped trend of association between handgrip strength and all-cause mortality in patients with CKD. Higher handgrip strength values, from 10 kg to approximately 28 kg, were associated with lower mortality risk. After that, the risk of death increased slightly.

CONCLUSION

A U-shaped association exists between handgrip strength and all-cause mortality risk in CKD patients. Future studies with quantitative measurements for each CKD stage will help to determine precise relative risk estimates between handgrip strength and mortality risk in patients with different stages of CKD.

Research prospects for kidney xenotransplantation: a bibliometric analysis

BACKGROUND

Xenograft kidney transplantation has been receiving increasing attention. The purpose of this study is to use bibliometric analysis to identify papers in this research field and explore their current status and development trends.

METHODS

Using the data in the Web of Science core database from Clarivate Analytics as the object of study, we used 'TS = Kidney OR Renal AND xenotransplantation' as the search term to find all literature from 1980 to 2 November 2022.

RESULTS

In total, 1005 articles were included. The United States has the highest number of publications and has made significant contributions in this field. Harvard University was at the forefront of this study. Professor Cooper has published 114 articles in this field. Xenotransplantation has the largest number of relevant articles. Transplantation was the most cited journal. High-frequency keywords illustrated the current state of development and future trends in xenotransplantation. The use of transgenic pigs and the development of coordinated co-stimulatory blockers have greatly facilitated progress in xenotransplantation research. We found that 'co-stimulation blockade', 'xenograft survival', 'pluripotent stem cell', 'translational research', and 'genetic engineering' were likely to be the focus of attention in the coming years.

CONCLUSIONS

This study screened global publications related to xenogeneic kidney transplantation; analyzed their literature metrology characteristics; identified the most cited articles in the research field; understood the current situation, hot spots, and trends of global research; and provided future development directions for researchers and practitioners.

An observational study of haemophilia A patients without inhibitors using the French national claims (SNDS) database

OBJECTIVES

To describe clinical characteristics, factor consumption, and events of interest in patients with haemophilia A without inhibitors receiving prophylaxis in France, and the clinical impact of switching to Elocta® in this population.

METHODS

This retrospective, observational study using the Système National des Données de Santé database, analysed data from patients with haemophilia A without inhibitors using prophylactic factor VIII (FVIII) replacement therapy during 2016-2019. Clinical characteristics, treatment patterns and switches, factor consumption, and rate of events of interest were determined. In a sub-cohort of patients treated with Elocta®, clinical characteristics, factor consumption, and rate of events of interest before and after switching to Elocta® were compared.

RESULTS

For 545 patients, with mean age (standard deviation [SD]) 25.4 (17.8) years, Elocta® was the most used treatment. Bleeding events and articular non-bleeding events leading to hospitalization occurred in 15.4% and 13.9% of patients, respectively, and 9.9% of patients had surgeries or procedures related to haemophilic arthropathy. The mean (SD) FVIII product consumption was 344 (93) IU/kg/month for extended half-life treatment, and 331 (98) IU/kg/month for standard half-life products. For the sub-cohort of 146 patients, bleeding events (SD) decreased from 0.32 (2.2) to 0.09 (0.42) events/patient/year (p = 0.227) after switching to Elocta®. There was no statistically significant difference in rates of factor consumption or articular non-bleeding events before and after initiation of Elocta®.

CONCLUSION

This study provides real-world insights that advance the understanding of treatment patterns and events of interest in patients with haemophilia A on prophylactic regimens in France.

Identification of macrophage-related molecular subgroups and risk signature in colorectal cancer based on a bioinformatics analysis

Macrophages play a crucial role in tumor initiation and progression, while macrophage-associated gene signature in colorectal cancer (CRC) patients has not been investigated. Our study aimed to identify macrophage-related molecular subgroups and develop a macrophage-related risk model to predict CRC prognosis. The mRNA expression profile and clinical information of CRC patients were obtained from TCGA and GEO databases. CRC patients from TCGA were divided into high and low macrophage subgroups based on the median macrophage score. The ESTIMATE and CIBERSORT algorithms were used to assess immune cell infiltration between subgroups. GSVA and GSEA analyses were performed to investigate differences in enriched pathways between subgroups. Univariate and LASSO Cox regression were used to build a prognostic risk model, which was further validated in the GSE39582 dataset. A high macrophage score subgroup was associated with poor prognosis, highly activated immune-related pathways and an immune-active microenvironment. A total of 547 differentially expressed macrophage-related genes (DEMRGs) were identified, among which seven genes (including RIMKLB, UST, PCOLCE2, ZNF829, TMEM59L, CILP2, DTNA) were identified by COX regression analyses and used to build a risk score model. The risk model shows good predictive and diagnostic values for CRC patients in both TCGA and GSE39852 datasets. Furthermore, multivariate Cox regression analysis showed that the risk score was an independent risk factor for overall survival in CRC patients. Our findings provided a novel insight into macrophage heterogeneity and its immunological role in CRC. This risk score model may serve as an effective prognostic tool and contribute to personalised clinical management of CRC patients.

Computational fragment-based drug design of potential Glo-I inhibitors

In this study, a fragment-based drug design approach, particularly de novo drug design, was implemented utilising three different crystal structures in order to discover new privileged scaffolds against glyoxalase-I enzyme as anticancer agents. The fragments were evoluted to indicate potential inhibitors with high receptor affinities. The resulting compounds were served as a benchmark for choosing similar compounds from the ASINEX® database by applying different computational ligand-based drug design techniques. Afterwards, the selection of potential hits was further aided by various structure-based approaches. Then, 14 compounds were purchased, and tested in vitro against Glo-I enzyme. Of the tested 14 hits, the biological screening results showed humble activities where the percentage of Glo-I inhibition ranged from 0-18.70 %. Compound 19 and compound 28, whose percentage of inhibitions are 18.70 and 15.80%, respectively, can be considered as hits that need further optimisation in order to be converted into lead-like compounds.

Tiny clue reveals the general trend: a bibliometric and visualized analysis of renal microcirculation

BACKGROUND

Renal microcirculation plays a pivotal role in kidney function by maintaining structural and functional integrity, facilitating oxygen and nutrient delivery, and waste removal. However, a thorough bibliometric analysis in this area remains lacking. Therefore, we aim to provide valuable insights through a bibliometric analysis of renal microcirculation literature using the Web of Science database.

METHODS

We collected renal microcirculation-related publications from the Web of Science database from January 01, 1990, to December 31, 2022. The co-authorship of authors, organizations, and countries/regions was analyzed with VOSviewer1.6.18. The co-occurrence of keywords and co-cited references were analyzed using CiteSpace6.1.R6 software to generate visualization maps. Additionally, burst detection was applied to keywords and cited references to forecast research hotspots and future trends.

RESULTS

Our search yielded 7462 publications, with the American Journal of Physiology-Renal Physiology contributing the most articles. The United States, Mayo Clinic, and Lerman Lilach O emerged with the highest publication count, indicating their active collaborations. 'Type 2 diabetes' was the most significant keyword cluster, and 'diabetic kidney disease' was the largest cluster of cited references. 'Cardiovascular outcome' and 'diabetic kidney diseases' were identified as keywords in their burst period over the past three years.

CONCLUSION

Our bibliometric analysis illuminates the contours of nephrology and microcirculation research, revealing a landscape ripe for challenges and the seeds of future scientific innovation. While the trends discerned from the literature emerging opportunities in diagnostic innovation, renal microcirculation research, and precision medicine interventions, their translation to clinical practice is anticipated to be a deliberate process.

Hub genes and associated drugs for multiple myeloma with 1q21+: identified by bioinformatic analysis

While 1q21+ was common genetic alteration and found to have adverse effect on prognosis, the underlying genes remain unclear. Identification of related genes may provide additional help for rational intervention. The microarray dataset GSE2658 associated with MM was downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were obtained, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to annotate their functions. The hub genes were derived from the combined results of up-regulated DEGs and weighted gene coexpression network analysis (WGCNA). The receiver operating characteristic (ROC) curves of hub genes were plotted to evaluate correlation with 1q21+. Survival analysis and drug-gene interaction of hub genes were performed separately to find the prognostic value and potential targeted drugs. A total of 55 DEGs were identified. GO and KEGG pathway analyses suggested that the DEGs were related to several pathways of cell proliferation. NVL, IL6R, DUSP23 were proven to be highly correlated with 1q21+ and have adverse effects on prognosis. IL6R, DUSP23 were matched to known interaction-drug. This study revealed potential roles of hub genes in the pathogenesis and progression of MM with 1q21+, further investigations are needed to elucidate the mechanisms.

A systematic review of artificial intelligence-powered (AI-powered) chatbot intervention for managing chronic illness

BACKGROUND

Utilizing artificial intelligence (AI) in chatbots, especially for chronic diseases, has become increasingly prevalent. These AI-powered chatbots serve as crucial tools for enhancing patient communication, addressing the rising prevalence of chronic conditions, and meeting the growing demand for supportive healthcare applications. However, there is a notable gap in comprehensive reviews evaluating the impact of AI-powered chatbot interventions in healthcare within academic literature. This study aimed to assess user satisfaction, intervention efficacy, and the specific characteristics and AI architectures of chatbot systems designed for chronic diseases.

METHOD

A thorough exploration of the existing literature was undertaken by employing diverse databases such as PubMed MEDLINE, CINAHL, EMBASE, PsycINFO, ACM Digital Library and Scopus. The studies incorporated in this analysis encompassed primary research that employed chatbots or other forms of AI architecture in the context of preventing, treating or rehabilitating chronic diseases. The assessment of bias risk was conducted using Risk of 2.0 Tools.

RESULTS

Seven hundred and eighty-four results were obtained, and subsequently, eight studies were found to align with the inclusion criteria. The intervention methods encompassed health education (n = 3), behaviour change theory (n = 1), stress and coping (n = 1), cognitive behavioural therapy (n = 2) and self-care behaviour (n = 1). The research provided valuable insights into the effectiveness and user-friendliness of AI-powered chatbots in handling various chronic conditions. Overall, users showed favourable acceptance of these chatbots for self-managing chronic illnesses.

CONCLUSIONS

The reviewed studies suggest promising acceptance of AI-powered chatbots for self-managing chronic conditions. However, limited evidence on their efficacy due to insufficient technical documentation calls for future studies to provide detailed descriptions and prioritize patient safety. These chatbots employ natural language processing and multimodal interaction. Subsequent research should focus on evidence-based evaluations, facilitating comparisons across diverse chronic health conditions.

Nomogram establishment for short-term survival prediction in ICU patients with aplastic anemia based on the MIMIC-IV database

OBJECTIVE

To establish an efficient nomogram model to predict short-term survival in ICU patients with aplastic anemia (AA).

METHODS

The data of AA patients in the MIMIC-IV database were obtained and randomly assigned to the training set and testing set in a ratio of 7:3. Independent prognosis factors were identified through univariate and multivariate Cox regression analyses. The variance inflation factor was calculated to detect the correlation between variables. A nomogram model was built based on independent prognostic factors and risk scores for factors were generated. Model performance was tested using C-index, receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis (DCA) and Kaplan-Meier curve.

RESULTS

A total of 1,963 AA patients were included. A nomogram model with 7 variables was built, including SAPS II, chronic pulmonary obstructive disease, body temperature, red cell distribution width, saturation of peripheral oxygen, age and mechanical ventilation. The C-indexes in the training set and testing set were 0.642 and 0.643 respectively, indicating certain accuracy of the model. ROC curve showed favorable classification performance of nomogram. The calibration curve reflected that its probabilistic prediction was reliable. DCA revealed good clinical practicability of the model. Moreover, the Kaplan-Meier curve showed that receiving mechanical ventilation could improve the survival status of AA patients in the short term but did not in the later period.

CONCLUSION

The nomogram model of the short-term survival rate of AA patients was built based on clinical characteristics, and early mechanical ventilation could help improve the short-term survival rate of patients.

A GC×GC-MS method based on solid-state modulator for non-targeted metabolomics: Comparison with traditional GC-MS method

The formidable challenge posed by the presence of extremely high amounts of compounds and large differences in concentrations in plasma significantly complicates non-targeted metabolomics analyses. In this study, a comprehensive two-dimensional gas chromatography-quadrupole mass spectrometry (GC×GC-qMS) method with a solid-state modulator (SSM) for non-targeted metabolomics in beagle plasma was first established based on a GC-MS method, and the qualitative and quantitative performance of the two platforms were compared. Identification of detected compounds was accomplished utilizing NIST database match scores, retention indices (RIs) and standards. Semi-quantification involved the calculation of peak area ratios to internal standards. Metabolite identification sheets were generated for plasma samples on both analytical platforms, featuring 22 representative metabolites chosen for validating qualitative accuracy, and for conducting comparisons of linearity, accuracy, precision, and sensitivity. The outcomes revealed a threefold increase in the number of identifiable metabolites on the GC×GC-MS platform, with lower limits of quantitation (LLOQs) reduced to 0.5-0.05 times those achieved on the GC-MS platform. Accuracy in quantification for both GC×GC-MS and GC-MS fell within the range of 85-115%, and the vast majority of intra- and inter-day precisions were within the range of 20%. These findings underscore that relative to the conventional GC-MS method, the GC×GC-MS method developed in this study, combined with SSM, exhibits enhanced qualitative capabilities, heightened sensitivity, and comparable accuracy and precision, rendering it more suitable for non-targeted metabolomics analyses.

A universal ANN-to-SNN framework for achieving high accuracy and low latency deep Spiking Neural Networks

Spiking Neural Networks (SNNs) have become one of the most prominent next-generation computational models owing to their biological plausibility, low power consumption, and the potential for neuromorphic hardware implementation. Among the various methods for obtaining available SNNs, converting Artificial Neural Networks (ANNs) into SNNs is the most cost-effective approach. The early challenges in ANN-to-SNN conversion work revolved around the susceptibility of converted SNNs to conversion errors. Some recent endeavors have attempted to mitigate these conversion errors by altering the original ANNs. Despite their ability to enhance the accuracy of SNNs, these methods lack generality and cannot be directly applied to convert the majority of existing ANNs. In this paper, we present a framework named DNISNM for converting ANN to SNN, with the aim of addressing conversion errors arising from differences in the discreteness and asynchrony of network transmission between ANN and SNN. The DNISNM consists of two mechanisms, Data-based Neuronal Initialization (DNI) and Signed Neuron with Memory (SNM), designed to respectively address errors stemming from discreteness and asynchrony disparities. This framework requires no additional modifications to the original ANN and can result in SNNs with improved accuracy performance, simultaneously ensuring universality, high precision, and low inference latency. We verify it experimentally on challenging object recognition datasets, including CIFAR10, CIFAR100, and ImageNet-1k. Experimental results show that the SNN converted by our framework has very high accuracy even at extremely low latency.

Global burden of depression or depressive symptoms in children and adolescents: A systematic review and meta-analysis

BACKGROUND

Depression is the leading cause of health-related disability. A proportion of depression cases begin in childhood and increase dramatically during adolescence. This systematic review and meta-analysis aimed to estimate the global prevalence of depression or depressive symptoms in children and adolescents and explore the temporal and regional distribution of depression or depressive symptoms.

METHODS

This systematic review and meta-analysis identified peer-reviewed literature published through April 8, 2023, using the MEDLINE, Embase and APA PsycINFO databases, supplemented by reverse reference searches. Observational studies published in English and based on validated instruments with prevalence data on depression or depressive symptoms in children and adolescents aged ≤18 years were eligible. Random-effects meta-analysis and meta-regression analysis were performed using R software.

RESULTS

This systematic review and meta-analysis included a total of 96 studies (29 countries, 528,293 participants) published between 1989 and 2022. The pooled prevalence of mild-to-severe, moderate-to-severe, and major depression were 21.3 % (95%CI, 16.7 %-26.7 %), 18.9 % (95%CI, 14.6 %-24.2 %), and 3.7 % (95%CI, 2.7 %-5.1 %) respectively. Meta-regression analysis showed that from 1989 to 2022, the prevalence of mild-to-severe and moderate-to-severe depression increased over time (P = 0.002, P = 0.034, respectively), but the prevalence of major depression did not change significantly (P = 0.636).

LIMITATIONS

Only English articles were included. There was significant heterogeneity across the included studies. The studies included were mostly based on self-report scales to assess depressive symptoms.

CONCLUSION

In this systematic review, about one in five children and adolescents globally suffered from depression or had depressive symptoms, and this proportion was increasing over time.

Implementation of a realistic artificial data generator for crash data generation

In this paper, a framework is outlined to generate realistic artificial data (RAD) as a tool for comparing different models developed for safety analysis. The primary focus of transportation safety analysis is on identifying and quantifying the influence of factors contributing to traffic crash occurrence and its consequences. The current framework of comparing model structures using only observed data has limitations. With observed data, it is not possible to know how well the models mimic the true relationship between the dependent and independent variables. Further, real datasets do not allow researchers to evaluate the model performance for different levels of complexity of the dataset. RAD offers an innovative framework to address these limitations. Hence, we propose a RAD generation framework embedded with heterogeneous causal structures that generates crash data by considering crash occurrence as a trip level event impacted by trip level factors, demographics, roadway and vehicle attributes. Within our RAD generator we employ three specific modules: (a) disaggregate trip information generation, (b) crash data generation and (c) crash data aggregation. For disaggregate trip information generation, we employ a daily activity-travel realization for an urban region generated from an established activity-based model for the Chicago region. We use this data of more than 2 million daily trips to generate a subset of trips with crash data. For trips with crashes crash location, crash type, driver/vehicle characteristics, and crash severity. The daily RAD generation process is repeated for generating crash records at yearly or multi-year resolution. The crash databases generated can be employed to compare frequency models, severity models, crash type and various other dimensions by facility type - possibly establishing a universal benchmarking system for alternative model frameworks in safety literature.

Quality assessment of health science-related short videos on TikTok: A scoping review

OBJECTIVE

The aims of this review are to clarify the current state of research in terms of assessment tools and assessors of the quality of health science-related short videos on TikTok, to identify limitations in existing research; and to provide a reference for future studies.

METHODS

A scoping review was conducted. The Cochrane Library, PubMed, MEDLINE, Web of Science, Embase, Scopus, EBSCO, CNKI, VIP, Wanfang Data, and CBM databases were searched from September 2016 to November 2022. Manual searching was also performed to identify additional eligible studies. A total of 2620 documents were initially retrieved, and 29 were ultimately included. The literature was screened and collected, and data were extracted and summarized by 2 researchers.

RESULTS

(1) The quality evaluation tools used in the 29 papers included the DISCERN, PEMAT(A/V), GQS, JAMA, HONcode, guidelines and self-developed tools. Twenty-four of the included articles used the DISCERN for quality assessment, which was the most frequently used evaluation tool. However, most of these tools were not developed to assess health science-related short videos, lacked credibility tests, and had poor applicability; therefore, the accuracy of the evaluation results might be biased. (2) The assessors of the quality of health science-related short videos on TikTok were mainly experts in related fields and medical students, with doctors (12/14) being the most common evaluators. Fifteen studies did not report the identity of the evaluators, and 12 studies did not report interrater reliability.

CONCLUSION

This scoping review found that there is a lack of specific quality assessment tools for health science-related short videos on TikTok. Second, the current quality assessors of health science-related short videos on TikTok are limited. Future research should focus on the development of reliable, scientific quality assessment tools for health science-related short videos; unifying the evaluation standards; inviting users with different backgrounds and different health literacy levels to conduct quality assessments; exploring the quality assessment of health science-related short videos on TikTok from different perspectives.

An elastic competitive and discriminative collaborative representation method for image classification

Collaborative representation-based (CR) methods have become prevalent for pattern classification tasks, achieving formidable performance. Theoretically, we expect the learned class-specific representation of the correct class to be discriminative against others, with the representation of the correct class contributing dominantly in CR. However, most existing CR methods focus on improving discrimination while having a limited impact on enhancing the representation contribution of the correct category. In this work, we propose a novel CR approach for image classification called the elastic competitive and discriminative collaborative representation-based classifier (ECDCRC) to simultaneously strengthen representation contribution and discrimination of the correct class. The ECDCRC objective function penalizes two key terms by fully incorporating label information. The competitive term integrates the nearest subspace representation with corresponding elastic factors into the model, allowing each class to have varying competition intensities based on similarity with the query sample. This enhances the representation contribution of the correct class in CR. To further improve discrimination, the discriminative term introduces an elastic factor as a weight in the model to represent the gap between the query sample and the representation of each class. Moreover, instead of focusing on representation coefficients, the designed ECDCRC weights associated with representation components directly relate to the representation of each class, enabling more direct and precise discrimination improvement. Concurrently, sparsity is also enhanced through the two terms, further boosting model performance. Additionally, we propose a robust ECDCRC (R-ECDCRC) to handle image classification with noise. Extensive experiments on seven public databases demonstrate the proposed method's superior performance over related state-of-the-art CR methods.

Fast and accurate 3-D spine MRI segmentation using FastCleverSeg

Accurate and efficient segmenting of vertebral bodies, muscles, and discs is crucial for analyzing various spinal diseases. However, traditional methods are either laborious and time-consuming (manual segmentation) or require extensive training data (fully automatic segmentation). FastCleverSeg, our proposed semi-automatic segmentation approach, addresses those limitations by significantly reducing user interaction while maintaining high accuracy. First, we reduce user interaction by requiring the manual annotation of only two or three slices. Next, we automatically Estimate the Annotation on Intermediary Slices (EANIS) using traditional computer vision/graphics concepts. Finally, our proposed method leverages improved voxel weight balancing to achieve fast and precise volumetric segmentation in the segmentation process. Experimental evaluations on our assembled diverse MRI databases comprising 179 patients (60 male, 119 female), demonstrate a remarkable 25 ms (30 ms standard deviation) processing time and a significant reduction in user interaction compared to existing approaches. Importantly, FastCleverSeg maintains or surpasses the segmentation quality of competing methods, achieving a Dice score of 94%. This invaluable tool empowers physicians to efficiently generate reliable ground truths, expediting the segmentation process and paving the way for future integration with deep learning approaches. In turn, this opens exciting possibilities for future fully automated spine segmentation.

Large-scale network abnormality in behavioral addiction

BACKGROUND

Researchers have endeavored to ascertain the network dysfunction associated with behavioral addiction (BA) through the utilization of resting-state functional connectivity (rsFC). Nevertheless, the identification of aberrant patterns within large-scale networks pertaining to BA has proven to be challenging.

METHODS

Whole-brain seed-based rsFC studies comparing subjects with BA and healthy controls (HC) were collected from multiple databases. Multilevel kernel density analysis was employed to ascertain brain networks in which BA was linked to hyper-connectivity or hypo-connectivity with each prior network.

RESULTS

Fifty-six seed-based rsFC publications (1755 individuals with BA and 1828 HC) were included in the meta-analysis. The present study indicate that individuals with BAs exhibit (1) hypo-connectivity within the fronto-parietal network (FN) and hypo- and hyper-connectivity within the ventral attention network (VAN); (2) hypo-connectivity between the FN and regions of the VAN, hypo-connectivity between the VAN and regions of the FN and default mode network (DMN), hyper-connectivity between the DMN and regions of the FN; (3) hypo-connectivity between the reward system and regions of the sensorimotor network (SS), DMN and VAN; (4) hypo-connectivity between the FN and regions of the SS, hyper-connectivity between the VAN and regions of the SS.

CONCLUSIONS

These findings provide impetus for a conceptual framework positing a model of BA characterized by disconnected functional coordination among large-scale networks.

Technology to Support Older Adults in Home Palliative Care: A Scoping Review

BACKGROUND

Today, many older adults use health technologies, approach their final days with laptops, smartphones, and tablets. Telepalliative care is a service that remotely delivers palliative care through videoconferencing, telephonic communication, or remote symptom monitoring. The service meets the needs of patients who want to die at home and reducing unnecessary hospitalizations. The objective of this study is to map the literature on the use of technology by the terminally ill older adult population being cared for at home, to identify which technology systems are in use, to determine how technology can change communication between palliative care professionals and patients, and to explore the strengths or weaknesses patients perceive regarding the use of technology.

METHODS

We conducted a scoping review following the methodology of Arksey and O'Malley. A literature search was conducted in the MEDLINE, Embase, Web of Science, SCOPUS, PsycINFO, CINAHL, Ilisi and Google Scholar databases.

RESULTS

Fourteen eligible papers identified various tools available in clinical practice and found that most older adults are comfortable and satisfied using them. Despite being physically distanced from clinicians, patients felt cared for even though eye contact was lacking. Being unfamiliar with technology emerged as a barrier to telepalliative care in addition to difficulties caused by screen size and internet connection problems.

CONCLUSIONS

Older adults in palliative care at home perceive technology as a means of receiving efficient care. However, future research is needed to investigate what they look for in a technological tool and to develop more suitable technologies for them.

CLINICAL TRIAL REGISTRATION

The protocol of this study has been published in the Open Science Framework (OSF) preregistrations at https://osf.io/acv7q to enhance replicability and transparency and reduce any publication or reporting bias.

Temporal validation of a Generalized Additive2 Model (GA2M) to assess the risk of Chronic Kidney Disease (CKD)

OBJECTIVE

To assess the temporal validity of a model predicting the risk of Chronic Kidney Disease (CKD) using Generalized Additive2 Models (GA2M).

MATERIALS

We adopted the Italian Health Search Database (HSD) with which the original algorithm was developed and validated by comparing different machine learnings models.

METHODS

We selected all patients aged >=15 being active in HSD in 2019. They were followed up until December 2022 so being updated with three years of data collection. Those with prior diagnosis of CKD were excluded. A GA2M-based algorithm for CKD prediction was applied to this cohort in order to compare observed and predicted risk. Area Under Curve (AUC) and Average Precision (AP) were calculated.

RESULTS

We obtained an AUC and AP equal to 88% and 30%, respectively.

DISCUSSION

The prediction accuracy of the algorithm was largely consistent with that obtained in our prior work which was based on a different time-window for data collection. We therefore underlined and demonstrated the relevance of temporal validation for this prediction tool.

CONCLUSION

The GA2M confirmed its high accuracy in prediction of CKD. As such, the respective patient- and population-based informatic tools might be implemented in primary care.

Using machine learning to link electronic health records in cancer registries: On the tradeoff between linkage quality and manual effort

BACKGROUND

Cancer registries link a large number of electronic health records reported by medical institutions to already registered records of the matching individual and tumor. Records are automatically linked using deterministic and probabilistic approaches; machine learning is rarely used. Records that cannot be matched automatically with sufficient accuracy are typically processed manually. For application, it is important to know how well record linkage approaches match real-world records and how much manual effort is required to achieve the desired linkage quality. We study the task of linking reported records to the matching registered tumor in cancer registries.

METHODS

We compare the tradeoff between linkage quality and manual effort of five machine learning methods (logistic regression, random forest, gradient boosting, neural network, and a stacked method) to a deterministic baseline. The record linkage methods are compared in a two-class setting (no-match/ match) and a three-class setting (no-match/ undecided/ match). A cancer registry collected and linked the dataset consisting of categorical variables matching 145,755 reported records with 33,289 registered tumors.

RESULTS

In the two-class setting, the gradient boosting, neural network, and stacked models have higher accuracy and F1 score (accuracy: 0.968-0.978, F1 score: 0.983-0.988) than the deterministic baseline (accuracy: 0.964, F1 score: 0.980) when the same records are manually processed (0.89% of all records). In the three-class setting, these three machine learning methods can automatically process all reported records and still have higher accuracy and F1 score than the deterministic baseline. The linkage quality of the machine learning methods studied, except for the neural network, increase as the number of manually processed records increases.

CONCLUSION

Machine learning methods can significantly improve linkage quality and reduce the manual effort required by medical coders to match tumor records in cancer registries compared to a deterministic baseline. Our results help cancer registries estimate how linkage quality increases as more records are manually processed.

Assessing the applicability and appropriateness of ChatGPT in answering clinical pharmacy questions

OBJECTIVES

Clinical pharmacists rely on different scientific references to ensure appropriate, safe, and cost-effective drug use. Tools based on artificial intelligence (AI) such as ChatGPT (Generative Pre-trained Transformer) could offer valuable support. The objective of this study was to assess ChatGPT's capacity to correctly respond to clinical pharmacy questions asked by healthcare professionals in our university hospital.

MATERIAL AND METHODS

ChatGPT's capacity to respond correctly to the last 100 consecutive questions recorded in our clinical pharmacy database was assessed. Questions were copied from our FileMaker Pro database and pasted into ChatGPT March 14 version online platform. The generated answers were then copied verbatim into an Excel file. Two blinded clinical pharmacists reviewed all the questions and the answers given by the software. In case of disagreements, a third blinded pharmacist intervened to decide.

RESULTS

Documentation-related issues (n=36) and drug administration mode (n=30) were preponderantly recorded. Among 69 applicable questions, the rate of correct answers varied from 30 to 57.1% depending on questions type with a global rate of 44.9%. Regarding inappropriate answers (n=38), 20 were incorrect, 18 gave no answers and 8 were incomplete with 8 answers belonging to 2 different categories. No better answers than the pharmacists were observed.

CONCLUSIONS

ChatGPT demonstrated a mitigated performance in answering clinical pharmacy questions. It should not replace human expertise as a high rate of inappropriate answers was highlighted. Future studies should focus on the optimization of ChatGPT for specific clinical pharmacy questions and explore the potential benefits and limitations of integrating this technology into clinical practice.

The State of Artificial Intelligence in Pediatric Surgery: A Systematic Review

BACKGROUND

Artificial intelligence (AI) has been recently shown to improve clinical workflows and outcomes - yet its potential in pediatric surgery remains largely unexplored. This systematic review details the use of AI in pediatric surgery.

METHODS

Nine medical databases were searched from inception until January 2023, identifying articles focused on AI in pediatric surgery. Two authors reviewed full texts of eligible articles. Studies were included if they were original investigations on the development, validation, or clinical application of AI models for pediatric health conditions primarily managed surgically. Studies were excluded if they were not peer-reviewed, were review articles, editorials, commentaries, or case reports, did not focus on pediatric surgical conditions, or did not employ at least one AI model. Extracted data included study characteristics, clinical specialty, AI method and algorithm type, AI model (algorithm) role and performance metrics, key results, interpretability, validation, and risk of bias using PROBAST and QUADAS-2.

RESULTS

Authors screened 8178 articles and included 112. Half of the studies (50%) reported predictive models (for adverse events [25%], surgical outcomes [16%] and survival [9%]), followed by diagnostic (29%) and decision support models (21%). Neural networks (44%) and ensemble learners (36%) were the most commonly used AI methods across application domains. The main pediatric surgical subspecialties represented across all models were general surgery (31%) and neurosurgery (25%). Forty-four percent of models were interpretable, and 6% were both interpretable and externally validated. Forty percent of models had a high risk of bias, and concerns over applicability were identified in 7%.

CONCLUSIONS

While AI has wide potential clinical applications in pediatric surgery, very few published AI algorithms were externally validated, interpretable, and unbiased. Future research needs to focus on developing AI models which are prospectively validated and ultimately integrated into clinical workflows.

LEVEL OF EVIDENCE

2A.

Impacts of probiotics on the efficacies of immune checkpoint inhibitors with or without chemotherapy for patients with advanced non-small-cell lung cancer

The relationships between the therapeutic effects of immune checkpoint inhibitors (ICIs) and the intestinal flora have attracted increasing attention. However, the effects of oral probiotics on the efficacies of ICIs used to treat non-small-cell lung cancer (NSCLC) remain unclear. We investigated the effects of probiotics on the efficacies of ICIs in patients treated with and without chemotherapy. We investigated patients with advanced NSCLC on ICI monotherapy or combination ICI and chemotherapy using the Okayama Lung Cancer Study Group Immunotherapy Database (OLCSG-ID) and the Okayama Lung Cancer Study Group Immunochemotherapy Database (OLCSG-ICD). In total, 927 patients (482 on ICI monotherapy, 445 on an ICI + chemotherapy) were enrolled. Most were male, of good performance status, smokers, and without epidermal growth factor receptor (EGFR)/anaplastic lymphoma kinase (ALK) mutations. Probiotics were administered to 19% of patients on ICI monotherapies and 17% of those on ICIs + chemotherapy. Of the former patients, progression-free survival (PFS) and overall survival (OS) were significantly better in the probiotics group (PFS 7.9 vs. 2.9 months, hazard ratio [HR] 0.54, p < .001; OS not attained vs. 13.1 months, HR 0.45, p < .001). Among patients receiving ICI and chemotherapy, there were no significant differences in PFS between those on probiotics and not but OS was significantly better in the probiotics group (PFS 8.8 vs. 8.6 months, HR 0.89, p = .43; OS not attained vs. 22.6 months, HR 0.61, p = .03). Patients on probiotics experienced better outcomes following ICI treatment.

Triplet-constrained deep hashing for chest X-ray image retrieval in COVID-19 assessment

Radiology images of the chest, such as computer tomography scans and X-rays, have been prominently used in computer-aided COVID-19 analysis. Learning-based radiology image retrieval has attracted increasing attention recently, which generally involves image feature extraction and finding matches in extensive image databases based on query images. Many deep hashing methods have been developed for chest radiology image search due to the high efficiency of retrieval using hash codes. However, they often overlook the complex triple associations between images; that is, images belonging to the same category tend to share similar characteristics and vice versa. To this end, we develop a triplet-constrained deep hashing (TCDH) framework for chest radiology image retrieval to facilitate automated analysis of COVID-19. The TCDH consists of two phases, including (a) feature extraction and (b) image retrieval. For feature extraction, we have introduced a triplet constraint and an image reconstruction task to enhance discriminative ability of learned features, and these features are then converted into binary hash codes to capture semantic information. Specifically, the triplet constraint is designed to pull closer samples within the same category and push apart samples from different categories. Additionally, an auxiliary image reconstruction task is employed during feature extraction to help effectively capture anatomical structures of images. For image retrieval, we utilize learned hash codes to conduct searches for medical images. Extensive experiments on 30,386 chest X-ray images demonstrate the superiority of the proposed method over several state-of-the-art approaches in automated image search. The code is now available online.

Artificial intelligence in the practice of forensic medicine: a scoping review

Forensic medicine is a thriving application field for artificial intelligence (AI). Indeed, AI applications intended to forensic pathologists or forensic physicians have emerged since the last decade. For example, AI models were developed to help estimate the biological age of migrants or human remains. However, the uses of AI applications by forensic pathologists or physicians and their levels of integration in medicolegal practices are not well described yet. Therefore, a scoping review was conducted on PubMed, ScienceDirect, and Scopus databases. This review included articles that mention any AI application used by forensic pathologists or physicians in practice or any AI model applied in one expertise field of the forensic pathologist or physician. Articles in other languages than English or French or dealing mainly with complementary analyses handled by experts who are not forensic pathologists or physicians or with AI to analyze data for research purposes in forensic medicine were excluded from this review. All the relevant information was retrieved in each article from a grid analysis derived and adapted from the TRIPOD checklist. This review included 35 articles and revealed that AI applications are developed in thanatology and in clinical forensic medicine. However, those applications seem to mainly remain in research and development stages. Indeed, the use of AI applications by forensic pathologists or physicians is not actual due to issues discussed in this article. Finally, the integration of AI in daily medicolegal practice involves not only forensic pathologists or physicians but also legal professionals.

User Experiences of CBT for Anxiety and Depression: A Qualitative Systematic Review and Meta-synthesis

Cognitive behavioural therapy (CBT) is an evidence-based treatment for anxiety and depression. It is important to determine the positive and negative aspects of CBT from the perspective of service users. However, there has been a lack of qualitative exploration into service user experiences of the therapy. This review aimed to address this gap by examining participants' experiences of CBT for anxiety and depression. Databases were searched and data were synthesised thematically. CBT was well-received by participants, though barriers to engagement were identified. CBT was often perceived as too difficult or demanding, as well as interventions being short and therefore superficial. Clinician qualities of being trustworthy, non-judgemental, and understanding appear to be significant contributors to client engagement and recovery. Findings support the delivery of in-depth clinician led CBT for anxiety and depression, as well as highlighting the need to review CBT delivery to better support service users.

Ocular toxicity associated with anti-HER2 agents in breast cancer: A pharmacovigilance analysis using the FAERS database

Anti-human epidermal growth factor receptor 2 (HER2) agents have exhibited pronounced tumor-inhibitory activity, yet the accompanying ocular toxicity has frequently been underestimated. We aim to conduct a comprehensive comparative analysis of ocular toxicity risk related to various anti-HER2 agents. We executed a retrospective pharmacovigilance investigation based on the FDA Adverse Event Reporting System (FAERS) database, covering the period from Q2 2018 to Q1 2023. The disproportionality analysis was performed to assess ocular toxicity risk. Multivariate logistic regression was implemented to mitigate potential biases. Moreover, the time to onset of ocular toxicity was also evaluated. A total of 3467 ocular adverse event (AE) reports concerning anti-HER2 agents were collected. At the preferred term (PT) level, there were 69 positive signals, among which excessive eye blinking, abnormal sensation in the eye, and asthenopia presented a significant risk. In comparison to tyrosine kinase inhibitors (TKIs), antibody drugs were associated with a broader range of ocular disorders at Standardized MedDRA Queries (SMQ)levels, including conjunctival disorders, corneal disorders, ocular infections, ocular motility disorders, optic nerve disorders, and retinal disorders. In terms of onset time, pertuzumab displayed an earlier onset at 21.5 days, while trastuzumab deruxtecan had the latest at 91.5 days. In summary, our study reveals varying degrees of ocular toxicity related to anti-HER2 agents, with a significantly higher risk observed in antibody drugs. Additionally, novel ocular toxicity signals, not documented in product labels, have been detected. In the future, further research will be necessary to validate our findings.

ALSFRS-R decline rate prior to baseline is not useful for stratifying subsequent progression of functional decline

OBJECTIVE

One of the difficulties in developing a novel drug for patients with amyotrophic lateral sclerosis (ALS) is the significant variation in the clinical course. To control this variation, a 12-week run-in period is used in some clinical trials. Based on the Amyotrophic Lateral Sclerosis Functional Rating Scale Revised (ALSFRS-R) change during the run-in period, only moderate progressors are selected in some clinical trials. Some reports showed that the ALSFRS-R progression rate was associated with survival. However, it is unclear whether the ALSFRS-R change in the run-in period is a useful prognostic factor of the ALSFRS-R change from baseline. In addition, we explore the inclusion criteria that could control the variability in ALS-function progression without setting a run-in period.

METHODS

We utilized the Japanese and US ALS registry databases (JaCALS and PRO-ACT). Patients were classified into three populations (rapid, moderate, and slow progressors) based on the ALSFRS-R change prior to baseline. We also classified patients into three prognostic populations based on the ALSFRS-R change from baseline. We confirmed whether each of the three populations were matched with their respective three prognostic populations.

RESULTS

Our data showed that the three groups classified by the ALSFRS-R change during the 12 weeks prior to baseline or by the rate of progression from onset to baseline did not accord with the three prognostic groups.

CONCLUSIONS

Our results showed that the ALSFRS-R change in the run-in period or from onset to baseline is not useful for stratifying subsequent progression of functional decline in clinical trials.

Investigating the progression of preeclampsia through a comprehensive analysis of genes associated with per- and polyfluoroalkyl substances

Per- and Polyfluoroalkyl Substances (PFAS) are synthetic chemicals utilized in the production of various products that possess water and dirt-repellent properties. Exposure to PFAS has been linked to numerous diseases, such as cancer and preeclampsia (PE). However, whether PFAS contributes to the advancement of PE remains uncertain. In this study, we conducted an extensive bioinformatics analysis using the Comparative Toxicogenomics Database (CTD) and Gene Expression Omnibus (GEO) databases, leading us to discover a connection between PE and four specific PFAS. Moreover, further examination revealed that six genes associated with PFAS exhibited significant diagnostic potential for individuals with PE. By employing receiver operating characteristic (ROC) curves, our PFAS-related gene-based nomogram model demonstrated outstanding predictive efficacy for diagnosing PE. Immune infiltration analysis showed that six PFAS-related genes were significantly associated with the level of immune cell infiltration. The expression of PFAS-related genes in PE patients was confirmed by collecting clinical samples. This research has offered fresh perspectives on comprehending the impact of PFAS on PE, drawing attention to the connection between environmental factors and the risks and development of PE.

Psilocybin Exposures Reported to U.S. Poison Centers: National Trends Over a Decade

PURPOSE

We describe trends in psilocybin exposures among adolescents and young adults as reported to US poison centers over the past decade.

METHODS

We queried the National Poison Data System for cases involving psilocybin during January 1, 2013-December 31, 2022. Persons aged 13-25 years were included. We examined exposures to psilocybin by demographics, clinical effects, level of care, and medical outcome.

RESULTS

During the 10-year study period, 4,055 psilocybin-involved exposures were reported among adolescents and young adults, 2,667 (65.8%) being single substance exposures. Most single substance cases received medical attention (adolescents: 75.3% [n = 1,176], young adults: 72.1% [n = 797]). We did not find significant change in the number of cases during 2013-2018. Cases started increasing in 2019. In 2022, cases more than tripled among adolescents and more than doubled among young adults, compared to 2018 (p < .0001).

DISCUSSION

Continued national surveillance is critical to determine the impact of psilocybin exposures on youth as it becomes increasingly available.

Predicting amyotrophic lateral sclerosis (ALS) progression with machine learning

OBJECTIVE

To predict ALS progression with varying observation and prediction window lengths, using machine learning (ML).

METHODS

We used demographic, clinical, and laboratory parameters from 5030 patients in the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) database to model ALS disease progression as fast (at least 1.5 points decline in ALS Functional Rating Scale-Revised (ALSFRS-R) per month) or non-fast, using Extreme Gradient Boosting (XGBoost) and Bayesian Long Short Term Memory (BLSTM). XGBoost identified predictors of progression while BLSTM provided a confidence level for each prediction.

RESULTS

ML models achieved area under receiver-operating-characteristics curve (AUROC) of 0.570-0.748 and were non-inferior to clinician assessments. Performance was similar with observation lengths of a single visit, 3, 6, or 12 months and on a holdout validation dataset, but was better for longer prediction lengths. 21 important predictors were identified, with the top 3 being days since disease onset, past ALSFRS-R and forced vital capacity. Nonstandard predictors included phosphorus, chloride and albumin. BLSTM demonstrated higher performance for the samples about which it was most confident. Patient screening by models may reduce hypothetical Phase II/III clinical trial sizes by 18.3%.

CONCLUSION

Similar accuracies across ML models using different observation lengths suggest that a clinical trial observation period could be shortened to a single visit and clinical trial sizes reduced. Confidence levels provided by BLSTM gave additional information on the trustworthiness of predictions, which could aid decision-making. The identified predictors of ALS progression are potential biomarkers and therapeutic targets for further research.

No causal association between serum vitamin D levels and diabetes retinopathy: A Mendelian randomization analysis

BACKGROUND AND AIM

Diabetes retinopathy (DR) is a common microvascular complication of diabetes, and it is the main cause of global vision loss. The current observational research results show that the causal relationship between Vitamin D and DR is still controversial. Therefore, we conducted a Mendelian randomization study to determine the potential causal relationship between serum 25-hydroxyvitamin D 25(OH)D and DR.

METHODS AND RESULTS

In this study, we selected aggregated data on serum 25(OH)D levels (GWAS ID: ebi-a-GCST90000615) and DR (GWAS ID: finn-b-DM_RETINOPATHY) from a large-scale GWAS database. Then use MR analysis to evaluate the possible causal relationship between them. We mainly use inverse variance weighted (IVW), supplemented by MR Egger and weighted median methods. Sensitivity analysis is also used to ensure the stability of the results, such as Cochran's Q-test, MR-PRESSO, MR-Egger interception test, and retention method. The MR analysis results showed that there was no significant causal relationship between 25(OH)D and DR (OR = 1.0128, 95%CI=(0.9593,1.0693), P = 0.6447); Similarly, there was no significant causal relationship between DR and serum 25 (OH) D levels (OR = 0.9900, 95% CI=(0.9758,1.0045), P = 0.1771).

CONCLUSION

Our study found no significant causal relationship between serum 25(OH)D levels and DR, and vice versa. A larger sample size randomized controlled trial is needed to further reveal its potential causal relationship.

Fragmentation of Care After Geriatric Trauma: A Nationwide Analysis of outcomes and Predictors

The health care system for the elderly is fragmented, that is worsened when readmission occurs to different hospitals. There is limited investigation into the impact of fragmentation on geriatric trauma patient outcomes. The aim of this study was to compare the outcomes following readmissions after geriatric trauma. The Nationwide Readmissions Database (2016-2017) was queried for elderly trauma patients (aged ≥65 years) readmitted due to any cause. Patients were divided into 2 groups according to readmission: index vs non-index hospital. Outcomes were 30 and 180-day complications, mortality, and the number of subsequent readmissions. Multivariable logistic regression was performed to analyze the independent predictors of fragmentation of care. A total of 36,176 trauma patients were readmitted, of which 3856 elderly patients (aged ≥65 years) were readmitted: index hospital (3420; 89%) vs non-index hospital (436; 11%). Following 1:2 propensity matching, elderly with non-index hospital readmission had higher rates of death and MI within 180 days (P = .01 and .02, respectively). They had statistically higher 30 and 180-day pneumonia (P < .01), CHF (P < .01), arrhythmias (P < .01), MI (P < .01), sepsis (P < .01), and UTI (P < .01). On multivariable binary logistic regression analysis, pneumonia (OR 1.70, P = .03), congestive heart failure (CHF) (OR 1.80, P = .03), female gender (OR .72, P = .04), and severe Head and Neck trauma (AIS≥3) (OR 1.50, P < .01) on index admission were independent predictors of fragmentation of care. While the increase in time to readmission (OR 1.01, P < .01) was also associated independently with non-index hospital admission. Fragmented care after geriatric trauma could be associated with higher mortality and complications.

Advancing the allergenicity assessment of new proteins using a text mining resource

With a society increasingly demanding alternative protein food sources, new strategies for evaluating protein safety issues, such as allergenic potential, are needed. Large-scale and systemic studies on allergenic proteins are hindered by the limited and non-harmonized clinical information available for these substances in dedicated databases. A missing key information is that representing the symptomatology of the allergens, especially given in terms of standard vocabularies, that would allow connecting with other biomedical resources to carry out different studies related to human health. In this work, we have generated the first resource with a comprehensive annotation of allergens' symptomatology, using a text-mining approach that extracts significant co-mentions between these entities from the scientific literature (PubMed, ∼36 million abstracts). The method identifies statistically significant co-mentions between the textual descriptions of the two types of entities in the literature as indication of relationship. 1,180 clinical signs extracted from the Human Phenotype Ontology, the Medical Subject Heading terms of PubMed together with other allergen-specific symptoms, were linked to 1,036 unique allergens annotated in two main allergen-related public databases via 14,009 relationships. This novel resource, publicly available through an interactive web interface, could serve as a starting point for future manually curated compilation of allergen symptomatology.

Comparison of the PCPLC Database to NSQIP-P: A Patient Matched Comparison of Surgical Complications Following Repair of Anorectal Malformation

BACKGROUND

Anorectal malformations (ARM) are rare and heterogenous which creates a challenge in conducting research and offering recommendations for best practice. The Pediatric Colorectal and Pelvic Learning Consortium (PCPLC) was formed in 2016 to address this challenge and created a shared national data registry to collect information about pediatric colorectal patients. There has been no external validation of the data collected. We sought to evaluate the database by performing a patient matched analysis comparing 30-day outcomes identified in the PCPLC registry with the NSQIP-P database for patients undergoing surgical repair of ARM.

METHODS

Patients captured in the PCPLC database from 2016 to 2021 at institutions also participating in NSQIP-P who underwent ARM repair younger than 12 months old were reviewed for 30-day complications. These patients were matched to their NSQIP-P record using their hospital identification number, and records were compared for concordance in identified complications.

RESULTS

A total of 591 patient records met inclusion criteria in the PCPLC database. Of these, 180 patients were also reviewed by NSQIP-P. One hundred and fifty-six patient records had no complications recorded. Twenty-four patient records had a complication listed in one or both databases. There was a 91 % concordance rate between databases. When excluding complications not tracked in the PCPLC registry, this agreement improved to 93 %.

CONCLUSION

Including all patients evaluated for this subpopulation, a 91 % concordance rate was observed when comparing PCPLC collected complications to NSQIP-P. Future efforts can focus on further validating the data within the PCPLC for other patient populations.

LEVEL OF EVIDENCE

V.

Performance of ChatGPT in Diagnosis of Corneal Eye Diseases

PURPOSE

The aim of this study was to assess the capabilities of ChatGPT-4.0 and ChatGPT-3.5 for diagnosing corneal eye diseases based on case reports and compare with human experts.

METHODS

We randomly selected 20 cases of corneal diseases including corneal infections, dystrophies, and degenerations from a publicly accessible online database from the University of Iowa. We then input the text of each case description into ChatGPT-4.0 and ChatGPT-3.5 and asked for a provisional diagnosis. We finally evaluated the responses based on the correct diagnoses, compared them with the diagnoses made by 3 corneal specialists (human experts), and evaluated interobserver agreements.

RESULTS

The provisional diagnosis accuracy based on ChatGPT-4.0 was 85% (17 correct of 20 cases), whereas the accuracy of ChatGPT-3.5 was 60% (12 correct cases of 20). The accuracy of 3 corneal specialists compared with ChatGPT-4.0 and ChatGPT-3.5 was 100% (20 cases, P = 0.23, P = 0.0033), 90% (18 cases, P = 0.99, P = 0.6), and 90% (18 cases, P = 0.99, P = 0.6), respectively. The interobserver agreement between ChatGPT-4.0 and ChatGPT-3.5 was 65% (13 cases), whereas the interobserver agreement between ChatGPT-4.0 and 3 corneal specialists was 85% (17 cases), 80% (16 cases), and 75% (15 cases), respectively. However, the interobserver agreement between ChatGPT-3.5 and each of 3 corneal specialists was 60% (12 cases).

CONCLUSIONS

The accuracy of ChatGPT-4.0 in diagnosing patients with various corneal conditions was markedly improved than ChatGPT-3.5 and promising for potential clinical integration. A balanced approach that combines artificial intelligence-generated insights with clinical expertise holds a key role for unveiling its full potential in eye care.

Trends in Pediatric Anterior Cruciate Ligament Reconstruction in Australia: An Analysis of Australian Medicare Benefits Schedule Database From 2001 to 2020

BACKGROUND

Anterior cruciate ligament (ACL) injuries are common and increasingly prevalent in the pediatric population. However, there remain sparse epidemiological data on the surgical treatment of these injuries. The objective of this study is to assess the trends in the rate of pediatric ACL reconstruction in Australia over the past 2 decades.

METHODS

The incidence of ACL reconstruction from 2001 to 2020 in patients 5 to 14 years of age was analyzed using the Australian Medicare Benefits Schedule (MBS) database. Data were stratified by sex and year. An offset term was introduced using population data from the Australian Bureau of Statistics to account for population changes over the study period.

RESULTS

A total of 3719 reconstructions for the management of pediatric ACL injuries were performed in Australia under the MBS in the 20-year period from 2001 to 2020. There was a statistically significant annual increase in the total volume and per capita volume of pediatric ACL reconstructions performed across the study period ( P <0.0001). There was a significant increase in the rate of both male and female reconstructions ( P <0.0001), with a greater proportion of reconstructions performed on males (n=2073, 56%) than females (n=1646, 44%). In 2020, the rate of pediatric ACL reconstructions decreased to a level last seen in 2015, likely due to the effects of COVID-19.

CONCLUSIONS

The incidence of ACL reconstruction in skeletally immature patients has increased in Australia over the 20-year study period. This increase is in keeping with evidence suggesting poor outcomes with nonoperative or delayed operative management.

Mendelian randomization study on causal association of FAM210B with drug-induced lupus

OBJECTIVE

Due to the complexity of drug-induced lupus (DIL) pathogenesis, more susceptibility factors need to be discovered. FAM210B is a new mitochondrial protein whose function has not been fully elucidated. This study will explore whether there is a correlation between FAM210B and the risk of DIL.

METHODS

At first, we extracted three FAM210B genetic variants from the GTEx database (n = 948), and extracted their corresponding genome-wide association study (GWAS) summary statistics from DIL (101 DIL cases and 218691 controls). Then, we performed a Mendelian randomization (MR) study to evaluate the causal association of the expression of FAM210B with DIL using inverse-variance weighted (IVW), the weighted median, MR-Egger, and MR-PRESSO test.

RESULTS

We successfully extracted three FAM210B single-nucleotide polymorphisms (SNPs) (rs116032784, rs34361943 and rs33923703) from the GTEx_Analysis_v8_eQTL data that can reduce FAM210B expression. The results of the MR analysis showed that genetically reduced expression of FAM210B was significantly associated with increased risk of DIL in European ancestry based on the IVW method (β = 1.037, p = 0.001, odds ratio [OR] = 2.821, 95% confidence interval [CI]:1.495-5.322).

CONCLUSION

MR analysis showed a causal relationship between FAM210B expression and the risk of DIL disease. Our results suggested that FAM210B may be a marker that can mark susceptibility of DIL in the future. It provides evidence for the study of DIL, but its specific mechanism of action in DIL needs to be further studied. Key Points •This is the first MR analysis to examine the association between FAM210B and DIL. •The findings of this study suggested that reduced FAM210B expression is associated with the increased risk of DIL. •FAM210B may be a marker that can mark susceptibility of DIL in the future.

Research hotspots and development trends in volume management of peritoneal dialysis patients: a bibliometrics and visual analysis up to 2022

OBJECTIVES

Among different renal replacement therapies (RRTs), peritoneal dialysis (PD) is a family based treatment method with multiple advantages, which allowing patients to maintain autonomy, avoiding frequent hospital visits, and preventing the spread of the disease virus. To visually analyze the literatures related to volume management of PD patients through bibliometric methods, to explore research hotspots and development trends in this field.

METHODS

The relevant literatures of PD patient volume management in the Web of Science core collection database were retrieved with the terms of peritoneal dialysis, volume management, capacity management, fluid status, and volume overload. The retrieval time was from the establishment of the database to October 2022. CiteSpace 6.1.R3 software was used to visually analyze Country, Institution, Author, Keyword, and draw keyword clusters and keyword emergence maps.

RESULTS

A total of 788 articles were included in the analysis, and the annual number of papers was on the rise, with the American, China, and Brirain in the top three, and Peking University and University College London in the top. Keywords cluster analysis showed 11 clusters. In the keyword emergence analysis, the keywords with higher emergence intensity rank are continuous cyclic peritoneal dialysis, ambulatory peritoneal dialysis, and icodextrin. The current research hotspots and trends are in the evaluation of peritoneal dialysis patients' volume status, the selection and adjustment of dialysis prescriptions, and adverse health outcomes.

CONCLUSION

The research on peritoneal dialysis volume management in China started late, but it has developed rapidly, and has a firm grasp of current research hotspots. However, there is less cooperation with other countries, so international exchanges and cooperation should be strengthened. At present, the volume assessment methods and dialysis modes are still the research hotspots, paying more attention to the adverse health outcomes of patients.

Should the Breast Cancer Staging System be Revised?

OBJECTIVE

The purpose of this study was to determine whether breast cancer patients at stage T2N0 with tumor size ≥4 cm and <4 cm.

METHOD

Patients with T2N0 stage breast cancer diagnosed between 2010 and 2019 were analyzed in 2 groups as <4 cm (T2a) and ≥4 cm (T2b) in the study using the SEER 17 Research Plus database. The patients' clinicopathological characteristics and oncological outcomes were included. Group comparisons of prognostic factors, overall survival (OS), and cancer-specific survival (CSS) were made.

RESULTS

In this study, which involved 70971 patients, the T2a group had higher 5-year OS rate (87.2 ± .2 vs 80.8 ± .5%) and 5-year CSS rate (93.7 ± .1% vs 89.4 ± .4%) than the T2b group (P < .001). Univariate analysis revealed that the overall risk of death was 1.5 times higher in T2b than T2a (HR: 1.533 [95% CI: 1.450-1.622], P < .001), whereas multivariate analysis demonstrated the risk was 1.4 times higher (HR: 1.384 [95% CI: 1.307-1.466], P < .001). The risk of cancer-specific death was 1.7 times higher in univariate analysis (HR: 1.691 [95% CI: 1.561-1.832], P < .001) and 1.4 times higher in multivariate analysis (HR: 1.420 [95% CI: 1.309-1.541], P < .001).

CONCLUSION

Overall survival and BCSS rates in stage T2b breast cancer patients are significantly lower than in T2a patients. Tumor size ≥4 cm in breast cancer is a negative predictor of prognosis.