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ObjectivesDuring the COVID-19 pandemic, a large number of research papers were published, and some of them were retracted. The present study aims to reveal the characteristics of retracted papers before and during the pandemic.MethodsThe study investigated 24,542,394 publications from 1999 to 2022 and analyzed the profiles of retracted papers from the perspectives of year, disease category, country, and journal.ResultsRetraction rates were generally increasing at least until 2019, and were the highest for “Neoplasms.” The number of publications for “Infections” and “Respiratory Tract Diseases” dramatically rose during the COVID-19 pandemic; however, the retraction rates in the two categories or of COVID-19-related papers were not especially high compared to other diseases. The association with retraction was strongest for China in most disease categories, whereas for COVID-19 papers, other countries showed higher retraction rates than China. In recent years, retracted papers have become less likely to be published in high-impact journals.ConclusionThe COVID-19 pandemic does not seem to affect the retractions of research papers much. We should keep monitoring retractions and analyze the effects of pandemics for better science.

IntroductionStudies have not sufficiently clarified the differences in citation impact between funded and non-funded clinical research papers. Hence, this study seeks to evaluate the relation between research funding status and clinical research papers’ citation impact in different research fields using multiple evaluation indices.MethodsIn this cross-sectional bibliometric study, clinical research papers published by core clinical research hospitals in Japan were compared retrospectively in terms of times cited (TC), category normalized citation impact (CNCI), citation percentile (CP), journal impact factor (JIF), the Software to Identify, Manage, and Analyze Scientific Publications (SIGAPS) category, and whether they were the funded clinical research. The association between research funding status or the SIGAPS category and CNCI ≥ 2 was analyzed using logistic regression analysis.Results11 core clinical research hospitals published 553 clinical research papers, of which 120 were non-funded and 433 were funded (public institution-funded and industry-funded). The study found that funded clinical research papers (public institution-funded and industry-funded) had significantly higher TC, CNCI, CP, and JIF than non-funded ones [TC: 8 (3–17) vs. 14 (8–31), p < 0.001; CNCI: 0.53 (0.19–0.97) vs. 0.87 (0.45–1.85), p < 0.001; CP: 51.9 (24.48–70.42) vs. 66.7 (40.53–88.01), p < 0.001; JIF: 2.59 (1.90–3.84) vs. 2.93 (2.09–4.20) p = 0.008], while the proportion of A or B rank clinical research papers of the SIGAPS category was not significantly different between the two groups (30.0 vs. 34.9%, p = 0.318). In the logistic regression analysis, having a CNCI ≥ 2 was significantly associated with research funding (public institution-funded and industry-funded) and publication in A or B rank journals of the SIGAPS category [research funding: Estimate 2.169, 95% confidence interval (CI) 1.153–4.083, p = 0.016; SIGAPS category A/B: Estimate 6.126, 95% CI 3.889–9.651, p < 0.001].ConclusionAnalysis via multiple indicators including CNCI and the SIGAPS category, which allows for a comparison of the papers’ citation impact in different research fields, found a positive relation between research funding status and the citation impact of clinical research papers.

Large Language Models (LLMs) like ChatGPT, Gemini, and Claude gain traction in healthcare simulation; this paper offers simulationists a practical guide to effective prompt design. Grounded in a structured literature review and iterative prompt testing, this paper proposes best practices for developing calibrated prompts, explores various prompt types and techniques with use cases, and addresses the challenges, including ethical considerations for using LLMs in healthcare simulation. This guide helps bridge the knowledge gap for simulationists on LLM use in simulation-based education, offering tailored guidance on prompt design. Examples were created through iterative testing to ensure alignment with simulation objectives, covering use cases such as clinical scenario development, OSCE station creation, simulated person scripting, and debriefing facilitation. These use cases provide easy-to-apply methods to enhance realism, engagement, and educational alignment in simulations. Key challenges associated with LLM integration, including bias, privacy concerns, hallucinations, lack of transparency, and the need for robust oversight and evaluation, are discussed alongside ethical considerations unique to healthcare education. Recommendations are provided to help simulationists craft prompts that align with educational objectives while mitigating these challenges. By offering these insights, this paper contributes valuable, timely knowledge for simulationists seeking to leverage generative AI’s capabilities in healthcare education responsibly.

ObjectiveThis study analyzed literature on traditional Chinese medicine (TCM) in treating chronic kidney disease (CKD) to identify research trends and provide guidance for future studies and clinical practice.MethodsThe study used data from Web of Science from 2000 to 2024 to analyze English-language literature on CKD and TCM. Bibliometric analysis was done using R software and the bibliometric package, with scientific mapping and visualization analysis conducted using tools like Citespace, VOSviewer, and ScimagoGraphica to explore research trends and connections.ResultsThis study revealed that a total of 1,153 relevant documents were retrieved, and the number of published articles showed an increasing trend, reaching a peak in 2022. In terms of article publication, China ranked first with 760 articles, closely followed by the United States with 132 articles. Guangzhou University of Traditional Chinese Medicine published 60 papers, the most among academic institutions, followed by Shanghai University of Traditional Chinese Medicine with 54 papers. In terms of individual authors, Liu Xinhui holds the record for the highest number of published articles, totaling 17, followed by Li Ping and Li Shunmin. The prevalent keywords include “chronic kidney disease,” “TCM,” and “oxidative stress.” Currently, the prominent areas of research interest include network pharmacology, gut microbiota, oxidative stress, and related topics. The current research trend in this field is towards the adoption of novel methodologies such as network pharmacology and the emphasis on exploring the relationship between gut microbiota and CKD.ConclusionGlobal research on TCM in the treatment of CKD is showing a positive development trend, but further research on safety, efficacy evaluation, and international cooperation is still needed. The development trend is to adopt new scientific research methods and focus on exploring the mechanism of TCM in treating CKD.

BackgroundHealth systems based on primary healthcare (PHC) have reduced costs and are effective for improved health outcomes. Kenya’s health system grapples with providing equitable access to essential health services, but there is increasing commitment by the government to strengthen primary healthcare. The aim of this paper is to provide a baseline assessment of the capacity and training needs of healthcare workers (HCWs) in Nakuru and Nyeri Counties and identify priorities for intervention.MethodsA cross-sectional study was carried out among 171 healthcare workers in Nyeri and Nakuru counties. Multistage sampling was employed to select sub-counties in the first stage and health facilities by level within each sub-county in the second level. Systematic random sampling was then employed to select HCWs from each level of facility. We targeted healthcare workers of all cadres within the health facilities, and included all who consented to participating. Structured self-administered pen-and-paper questionnaires were used for data collection, and a five-point Likert scale was used to measure the perceived capacity of the healthcare workers to provide primary healthcare. As for the training needs data, the participants selected any of the 12 components that they needed training in. Descriptive statistics was employed, and stacked bar charts were used to visualize the capacity and training needs for the components of PHC adopted in Kenya.ResultsIn total, we obtained questionnaires from 95 participants in Nakuru and 76 participants in Nyeri. Nakuru HCWs rated themselves higher than their Nyeri counterparts in maternal and newborn child healthcare, local endemic disease control, appropriate treatment of common diseases and injuries, provision of essential basic medication, dental health, HIV/AIDs & TB management, and primary eye care. In both counties, there were significant differences in capacity between the different levels of health facilities. We observed substantial capacity gaps for HIV/AIDs & TB management, mental health and dental health services in both counties.ConclusionThis study found a substantial capacity gap in several of the elements of PHC, especially in Nyeri County. Critical areas for intervention are HIV/AIDs & TB management and mental health training for both counties. Within the health system, there is need to strengthen the capacity of HCWs in lower-level health facilities to reduce the volume of referrals to secondary care facilities. We strongly recommend training programs in dental health, mental health, primary eye care, nutritional services and HIV/AIDs &TB management, that are carefully designed to emphasize skills and abilities.

The ethical governance of pharmaceutical clinical trials in Europe, particularly under Regulation 536/2014, is intended to ensure the safety, rights, and well-being of participants. Despite this regulatory framework, significant gaps in ethical oversight remain. This paper identifies five key deficiencies: (1) European regulations only partially address ethical imperatives set by international guidelines, thereby restricting the ethical mandate of relevant entities; (2) the role of research ethics committees is largely limited to pre-approval activities, reducing continuous oversight during trials; (3) GCP inspectors operate within a narrow scope regarding ethical oversight, which limits their ability to identify a broad range of unethical practices; (4) there is insufficient transparency and collaboration between RECs and regulators, specifically GCP inspectorates, leading to fragmented oversight; and (5) there is minimal integration of ethical findings into the marketing authorization decision process by entities such as clinical assessors and the CHMP. To bridge these gaps, the paper suggests a shift from a prospective ethics review to a comprehensive end-to-end model of ethical governance.

The role of chronic stress in the development of chronic diseases, especially multimorbidity, through the pathways of increasing allostatic load, and finally, allostatic overload (the state when a compensatory mechanism is likely to fail) is being emphasized. However, allostatic load is a dynamic measure that changes depending on sex, gender, age, level and type of stress, experience of a stressful situation, and coping behaviors. Many other factors such as race, ethnicity, working environment, lifestyle, and circadian rhythm of sleep are also important. The aim of this paper was to synthesize the available information on allostatic load differences, especially those connected to sex/gender and age, and to provide a model for the future study of allostatic load, with a focus on these differences. By carefully studying allostatic load factors, we realized that many studies do not take this allostatic load difference into account in the analysis methods. In this paper, we also support the idea of further research to develop new allostatic load analysis strategies that will include all knowledge about sex/gender differences and that will, in more detail, explain numerous changeable social and educational factors that are currently accepted as biological ones. Furthermore, specific allostatic load biomarkers are expressed differently in different age groups, indicating that the discrepancies cannot be attributed solely to sex/gender disparities. This kind of approach can be valuable, not only for better explaining the differences in the frequency and age of onset of chronic diseases and multimorbidity, but also for the potential planning and development of preventive actions based on the aforementioned sex/gender and age disaparities, in order to prevent the most frequent diseases and to establish specific biomarker cut-off values for each sex/gender and age group.

Digital innovation can significantly enhance public health services, environmental sustainability, and social welfare. To this end, the European Digital Innovation Hub (EDIH) initiative was funded by the European Commission and national governments aiming to facilitate the digital transformation on various domains (including health) via the setup of relevant ecosystems consisting of academic institutions, research centres, start-ups, small and medium-sized enterprises, larger companies, public organizations, technology transfer offices, innovation clusters, and financial institutions. The ongoing goal of the EDIHs initiative is to bridge the gap between high-tech research taking place in universities and research centres and its deployment in real-world conditions by fostering innovation ecosystems. In this context, the smartHEALTH EDIH started its operation in Greece in 2023, offering technical consultation services to companies and public sector organizations to accelerate digitalization in precision medicine and innovative e-health services by utilizing key technologies such as artificial intelligence, high-performance computing, cybersecurity, and others. During its first 20 months of operation, over 50 prospective recipients have applied for consulting services, mainly seeking “test-before-invest” services. This paper aims to provide insights regarding the smartHEALTH initiative, preliminary outcomes and lessons learned during this first period of operation. To this end, this paper outlines smartHEALTH’s approach to attracting recipients and providing expert guidance on utilizing state-of-the-art technologies for innovative services, product development, and process creation to accelerate digital transformation.

BackgroundInflammation plays a crucial role in the pathophysiology of dry eye (DE). This study aims to provide a comprehensive overview of the current status, hotspots and trends in DE anti-inflammatory research through bibliometric analysis.MethodAll publications were searched using the Web of Science Core Collection (WoSCC) database from 2004 to 2024. Bibliometric analyses were performed using VOSviewer, R-bibliometrix, and CiteSpace, and data were managed using Microsoft Office Excel 2019.ResultsThere were 603 papers published between 2004 and 2024 included in this study, with the number of papers increasing each year. The United States was the major contributor, with the largest number of publications and the greatest impact. Baylor College of Medicine was the most influential research institution. Pflugfelder, Stephen C. and Tsubota, Kazuo were the most prolific authors in this area of research, while Dana, Reza was the most cited author in the field with the highest impact. The Journal with the highest number of publications was Investigative Ophthalmology & Visual Science, while the highest impact journal was Ocular Surface. Research hotspots were focused on the mechanisms of inflammation in DE and interventions for anti-inflammatory therapy. Future studies would favor more inflammation-related targeted therapies and physical therapies.ConclusionThis study is the first bibliometric analysis to comprehensively summarize research trends and developments in DE anti-inflammatory treatments, pointing out recent research frontiers and hot directions for scholars studying DE anti-inflammatory treatments.

This paper reports a case of pseudolithiasis caused by the use of ceftriaxone sodium. The patient, a 54-year-old female, presented with intermittent abdominal pain and distension for 1 month. Initial ultrasonography showed no abnormalities. However, after receiving ceftriaxone sodium treatment for an upper respiratory tract infection, she developed discomfort in the right upper abdomen. A CT scan subsequently diagnosed her with gallbladder stones. Six weeks after discontinuing the medication, a follow-up examination revealed the disappearance of the stones, confirming the diagnosis of drug-induced pseudolithiasis. Through a literature review, this paper summarizes the mechanism, clinical manifestations, diagnostic methods, and treatment recommendations of ceftriaxone sodium-induced pseudolithiasis, providing a reference for clinical safe medication use.

Atopic dermatitis (AD) is a prevalent inflammatory skin disorder characterized by chronic inflammation, skin barrier dysfunction, and microbial dysbiosis, with Staphylococcus aureus playing a significant role in its pathogenesis. This paper explores the strain diversity and microevolution of S. aureus within AD patients, emphasizing how specific strains adapt to the altered skin environment, exacerbating the condition. The review emphasizes the significance of variation in specific functional genes among S. aureus strains, which enhances their ability to adapt to different microenvironments and shapes their pathogenic potential. It also discusses how mobile genetic elements, particularly prophages, contribute to genetic diversity and drive the virulence and antibiotic resistance of S. aureus in AD, highlighting the clinical challenges posed by these strain-specific factors in managing the disease. The paper advocates for the integration of advanced genomic tools such as whole-genome sequencing and machine learning to develop targeted therapies. By focusing on the genetic adaptability of S. aureus and its impact on AD, this review underscores the need for strain-specific diagnostics and personalized treatment strategies to improve patient outcomes.

BackgroundIn the past decade, the application of machine learning (ML) in the clinical management of acute upper gastrointestinal bleeding (AUGIB) has received much attention and has become a hot research topic. However, no scientometric report has systematically summarized and outlined the research progress in this field.ObjectiveThis study aims to utilize bibliometric analysis methods to delve into the applications of machine learning in AUGIB and the collaborative network behind it over the past decade. Through a thorough analysis of relevant literature, we uncover the research trends and collaboration patterns in this field, which can provide valuable references and insights for further in-depth exploration in the same field.MethodsUsing the Web of Science (WOS) as the data source, this study explores academic development in a specific field from December 2013 to December 2023. The search strategy included terms related to “Machine Learning” and “Acute Upper Gastrointestinal Bleeding”. Only original articles in English focusing on ML in AUGIB were included. The analysis of downloaded literature with Citespace software, including keyword co-occurrence, author collaboration networks, and citation relationship networks, reveals academic dynamics, research hotspots, and collaboration trends.ResultsAfter sorting and compiling, we have collected 73 academic papers written by 217 authors from 133 institutions in 29 countries worldwide. Among them, China and AM J GASTROENTEROL have made significant contributions in this field, providing many high-quality research achievements. The study found that these papers mainly focus on three core research hotspots: deepening clinical consensus, precise analysis of medical images, and optimization of data integration and decision support systems.ConclusionsThis study summarizes the latest advancements in the application of machine learning to AUGIB research. Through bibliometric analysis and network visualization, it reveals emerging trends, origins, leading institutions, and hot topics in this field. While this area has already demonstrated significant potential in medical artificial intelligence, our findings will provide valuable insights for future research directions and clinical practices.

Retinal vessel segmentation is a critical task in fundus image analysis, providing essential insights for diagnosing various retinal diseases. In recent years, deep learning (DL) techniques, particularly Generative Adversarial Networks (GANs), have garnered significant attention for their potential to enhance medical image analysis. This paper presents a novel approach for retinal vessel segmentation by harnessing the capabilities of GANs. Our method, termed GANVesselNet, employs a specialized GAN architecture tailored to the intricacies of retinal vessel structures. In GANVesselNet, a dual-path network architecture is employed, featuring an Auto Encoder-Decoder (AED) pathway and a UNet-inspired pathway. This unique combination enables the network to efficiently capture multi-scale contextual information, improving the accuracy of vessel segmentation. Through extensive experimentation on publicly available retinal datasets, including STARE and DRIVE, GANVesselNet demonstrates remarkable performance compared to traditional methods and state-of-the-art deep learning approaches. The proposed GANVesselNet exhibits superior sensitivity (0.8174), specificity (0.9862), and accuracy (0.9827) in segmenting retinal vessels on the STARE dataset, and achieves commendable results on the DRIVE dataset with sensitivity (0.7834), specificity (0.9846), and accuracy (0.9709). Notably, GANVesselNet achieves remarkable performance on previously unseen data, underscoring its potential for real-world clinical applications. Furthermore, we present qualitative visualizations of the generated vessel segmentations, illustrating the network’s proficiency in accurately delineating retinal vessels. In summary, this paper introduces GANVesselNet, a novel and powerful approach for retinal vessel segmentation. By capitalizing on the advanced capabilities of GANs and incorporating a tailored network architecture, GANVesselNet offers a quantum leap in retinal vessel segmentation accuracy, opening new avenues for enhanced fundus image analysis and improved clinical decision-making.

Humanity currently faces an ecological crisis with devastating consequences to all living species. While climate change is estimated to lead to 250,000 extra deaths per year between 2030 and 2050, pollution is known to cause 9 million premature deaths: a figure much greater than the deaths caused by AIDS, tuberculosis and malaria combined. The healthcare sector is both burdened by, and contributes to, the impact of climate change and environmental degradation. Amidst glaring evidence of the interdependence of human health and the eco system, there is an urgent call for healthcare professionals to concern themselves with the triple planetary threat humanity currently faces. Without immediate mitigative measures, the future seems uncertain. Some healthcare systems at local, national and global levels have taken numerous initiatives to address, mitigate and adapt to these changes, however, these are not sufficient. A lack of awareness among healthcare professionals of the ecological crisis, its interconnectedness, and the role of healthcare in it, plays a significant role in the lack responsibility of healthcare professionals in this space. Therefore, this paper presents a discussion of the current landscape of the triple threat of climate change, loss of biodiversity, and pollution, while emphasising the contribution of healthcare professionals to it. Furthermore, interrelated concepts such as planetary health and eco-anxiety are briefly discussed. This perspective paper also presents several key prospective research areas that may lay the foundation for motivating healthcare professionals to play an active role in preventing and mitigating the ecological crises humanity currently faces.