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Journal Article: explore 1078 source-linked works published from 2024 to 2028, with original documents and citations.
Includes records with this source-supplied label or an explicit phrase match in their metadata. Matches indicate a mention, not proof that a paper uses a method or tests a material. Source versions are consolidated by DOI.
Sources: pubmed. Collection updated 2026-09-15. Counts describe this index, not the complete source archives.
Inherited defects of DNA double-stranded break (DSB) repair can result in radiosensitive/radiation-sensitive (RS) SCID (RS-SCID). We applied base editing to reverse NHEJ1 mutations in patient fibroblasts, exemplifying how this technology can help interrogate RS sequence variants.
Various pathogenic microorganisms produce toxins that create pores in cell membranes, causing cell damage and disrupting the host epithelial barrier. Recently, we reported that mice lacking the G protein-coupled receptor leukotriene B4 receptor 2 (BLT2), which is expressed in vascular endothelial and alveolar epithelial cells, are highly susceptible to pneumolysin (PLY), a pneumococci-generated toxin. Although we clarified the protective roles of BLT2 in vascular endothelial cells, those in alveolar epithelial cells have not been elucidated. Here, we report that lipid mediator 12-hydroxyheptadecatrienoic acid (12-HHT), which is produced by membrane-damaged epithelial cells, prevents cell death by promoting membrane repair through BLT2. BLT2 promoted the release of PLY-bound plasma membranes as extracellular vesicles in a sphingomyelinase-dependent manner. Additionally, BLT2 activated Rac1 and subsequent actin polymerization, leading to resistance to cell death. Furthermore, inhibition of 12-HHT production by aspirin and treatment with a BLT2 antagonist abolished the protective effect of BLT2. These findings provide a new therapeutic strategy for bacterial infection.
The AAA ATPase VCP/p97 has emerged as a critical regulator of ubiquitin and chromatin-associated processes but progress in understanding has been hampered by the complexity of p97 functions and the various p97 cofactors involved. Here, we combined ubiquitin profiling with acutely induced degradation of the Ufd1 subunit of the p97 ubiquitin adapter, Ufd1-Npl4, in human cells. We identified a set of chromatin regulators, HUS1, XRCC1, MORF4L1, and the cohesin subunit RAD21 as targets of p97Ufd1-Npl4 We find that RAD21 is ubiquitylated and targeted by p97Ufd1-Npl4 specifically in S phase to remove a subpopulation of cohesin from chromatin. Acute degradation of Ufd1 in S phase, after replication licensing is completed, impedes replication and leads to replication-associated DNA damage. Our findings suggest that a fraction of cohesin rings need to be removed by p97Ufd1-Npl4 from DNA to allow unhindered replication and reveal a critical function of p97 that ensures genome stability.
BACKGROUND AND OBJECTIVES: Antifungal resistance represents an increasing global threat, driven by the rising burden of fungal disease. Antifungal stewardship (AFS) is a critical component of broader antimicrobial resistance (AMR) efforts, but education in this area remains less established than antibacterial stewardship initiatives. The scope and characteristics of the current landscape of online AFS resources have not yet been systematically described. To identify and evaluate online educational resources focused on fungal disease management and AFS, and assess their accessibility, format, educational design and implementation focus. METHODS: A structured search of internet search engines, distribution platforms and organizational websites was conducted to identify English-language web-based resources related to fungal disease management and stewardship. Resources were evaluated using predefined criteria including access model, format, length, educational design, interactivity and AFS content. An overall educational value score (1-10) was assigned. RESULTS: Twenty-three educational resources were identified. Most were delivered as online unfacilitated courses (11, 48%) and were short (<4 h) (12, 52%). Most focused on guidelines and syndromic management (18, 78%) and targeted doctors and/or nurses/midwives (22, 96%). Limited interactivity was reported in nine (39%) courses. Five courses (22%) had either a substantial or comprehensive focus on AFS. CONCLUSIONS: Online AFS educational resources are available and support awareness and knowledge development. However, they remain relatively few in number. Greater emphasis on implementation-focused learning, behaviour change components and broader global representation may enhance their impact.
PURPOSE: The primary purpose of the study was to explore the impact of health workers' awareness of artificial intelligence (AI) on their workplace well-being, addressing a critical gap in the literature. By examining this relationship through the lens of the Job demands-resources (JD-R) model, the study aimed to provide insights into how health workers' perceptions of AI integration in their jobs and careers could influence their informal learning behaviour and, consequently, their overall well-being in the workplace. The study's findings could inform strategies for supporting healthcare workers during technological transformations. DESIGN/METHODOLOGY/APPROACH: The study employed a quantitative research design using a survey methodology to collect data from 420 health workers across 10 hospitals in Ghana that have adopted AI technologies. The study was analysed using OLS and structural equation modelling. FINDINGS: The study findings revealed that health workers' AI awareness positively impacts their informal learning behaviour at the workplace. Again, informal learning behaviour positively impacts health workers' workplace well-being. Moreover, informal learning behaviour mediates the relationship between health workers' AI awareness and workplace wellbeing. Furthermore, employee learning orientation was found to strengthen the effect of AI awareness on informal learning behaviour. RESEARCH LIMITATIONS/IMPLICATIONS: While the study provides valuable insights, it is important to acknowledge its limitations. The study was conducted in a specific context (Ghanaian hospitals adopting AI), which may limit the generalizability of the findings to other healthcare settings or industries. Self-reported data from the questionnaires may be subject to response biases, and the study did not account for potential confounding factors that could influence the relationships between the variables. PRACTICAL IMPLICATIONS: The study offers practical implications for healthcare organizations navigating the digital transformation era. By understanding the positive impact of health workers' AI awareness on their informal learning behaviour and well-being, organizations can prioritize initiatives that foster a learning-oriented culture and provide opportunities for informal learning. This could include implementing mentorship programs, encouraging knowledge-sharing among employees and offering training and development resources to help workers adapt to AI-driven changes. Additionally, the findings highlight the importance of promoting employee learning orientation, which can enhance the effectiveness of such initiatives. ORIGINALITY/VALUE: The study contributes to the existing literature by addressing a relatively unexplored area - the impact of AI awareness on healthcare workers' well-being. While previous research has focused on the potential job displacement effects of AI, this study takes a unique perspective by examining how health workers' perceptions of AI integration can shape their informal learning behaviour and, subsequently, their workplace well-being. By drawing on the JD-R model and incorporating employee learning orientation as a moderator, the study offers a novel theoretical framework for understanding the implications of AI adoption in healthcare organizations.
PURPOSE: Precision medicine, also known as personalized medicine, enables the provision of tailored health services to patients. In the prevention, early detection, and treatment of cancers, precision medicine is highly promising, given the increasing use of genomic profiling for diagnosis and adapting therapies in several tumor types. Artificial Intelligence (AI) can support this process by analyzing vast amounts of relevant data. However, high-quality data and financial investments in the health system are essential for the implementation of precision medicine and AI solutions in routine cancer care. DESIGN/METHODOLOGY/APPROACH: Building on the quantitative outcomes of a foresight exercise published in another study, this article collects qualitative data to gain more detailed insights into the future of precision oncology in Belgium and discusses the role of AI in this field. It reports the results of a series of expert workshops, focusing on four hypothetical future scenarios that are centered around technological and economic issues that must be overcome for the widespread use of precision oncology in Belgium. FINDINGS: The study concludes that all four scenarios discussed in the workshops would require supportive policy measures in Belgium, which should go beyond mere technological and economic considerations, such as involving patient associations and the public in policy design or creating multi-disciplinary expert groups for precision medicine. ORIGINALITY/VALUE: To the best of our knowledge, this is the first study to employ foresight methodology to illustrate possible future scenarios, scrutinize feasible approaches for implementing precision oncology in Belgium, and discuss the use of AI in this context.
PURPOSE: A new paradigm, "healthcare's new iron triangle," has been developed to emphasise the technological perspective of healthcare delivery, focusing on automation, value and empathy. The study aims to build a conceptual model and to identify factors for the enhancement of the new iron triangle in healthcare organisations. DESIGN/METHODOLOGY/APPROACH: The healthcare organisation is the primary focus point of the current study. To determine the factors, a survey of the literature and healthcare experts' opinions was conducted. The healthcare professionals validated the identified factors. Data for this study were gathered using a closed-ended questionnaire and scheduled interviews. The study employed "Total Interpretive Structural Modeling methodology and Matriced' Impacts Croise´s Multiplication Appliqué´ a UN Classement/Cross-Impact Matrix Multiplication Applied to a Classification (MICMAC) analysis" to address the "why" and "how" the factors interact and prioritise the identified factors. FINDINGS: The study found that organisational structure (F8), artificial intelligence (F1), innovation (F2) and human resources (F5) are the driving or key factors of the study. RESEARCH LIMITATIONS/IMPLICATIONS: The study primarily focused on identifying factors for the enhancement of a new iron triangle in healthcare organisations. The scope could eventually be expanded to explore more areas. PRACTICAL IMPLICATIONS: Academics and other stakeholders will have a better understanding of the key drivers for the enhancement of the new iron triangle in healthcare organisations. ORIGINALITY/VALUE: In this study, total interpretive structural modeling and cross-impact MICMAC analysis are proposed as an innovative approach to address the new iron triangle in healthcare organisations.
BACKGROUND AND AIMS: Large-scale genome-wide association studies have identified common genetic variants that predict the risk of peripheral artery disease (PAD). This study assessed whether a polygenic risk score (PRS) is associated with PAD and the incidence of major adverse limb events (MALE) independent of clinical risk factors in patients with established cardiometabolic disease. METHODS: A genetic analysis was performed, pooling individual patient-level data from six TIMI trials. The association of a recently validated PAD PRS with prevalent PAD and the incidence of MALE (acute limb ischaemia, chronic limb-threatening ischaemia, major amputation, or peripheral revascularization) was assessed. RESULTS: A total of 68 816 patients were included in this analysis, with a median follow-up of 2.6 years. Of these, 5986 (8.7%) had known PAD at baseline. After adjusting for clinical risk factors, a higher PAD PRS was independently associated with a 15% greater odds of prevalent PAD (adjusted odds ratio per 1-SD: 1.15 [95% confidence interval 1.12-1.18], P < .0001), a magnitude of risk as strong as established clinical risk factors. A total of 577 patients experienced MALE during follow-up. A higher PAD PRS was associated with a 30% increased risk of MALE (adjusted hazard ratio per 1-SD: 1.30 [1.19-1.42], P < .0001). Adding the PAD PRS to clinical risk factors resulted in a statistically significant but modest improvement in discrimination (area under the curve went from 0.651 to 0.662 P < .0001). CONCLUSIONS: In a broad spectrum of patients with cardiometabolic disease, the PAD PRS is associated with an increased risk of PAD and the incidence of MALE beyond clinical risk factors; however, the improvement in discrimination was statistically significant but clinically modest.
PURPOSE: Financial challenges in healthcare systems worldwide, especially in low- and middle-income countries like Iran, have increased hospitals' reliance on insurance reimbursements. Unrecognized insurance deductions often cause severe financial shortages, making efficient deduction management crucial. This study aimed to design a hybrid intelligent system for identifying and predicting insurance deductions by combining machine learning and expert system frameworks. DESIGN/METHODOLOGY/APPROACH: A mixed-methods design was applied in four stages. First, a scoping review identified the causes and patterns of insurance deductions. Second, interviews with 15 insurance experts produced a validated checklist and a dataset from inpatient billing records. Third, using the CRISP-DM methodology, machine learning algorithms were developed and tested in SPSS Modeler alongside a fuzzy expert system developed in MATLAB. Finally, the model was validated using the holdout method. FINDINGS: Four categories of deduction drivers were identified: service provision, registration errors, document submission issues, and revenue conversion processes. The CHAID decision tree outperformed other algorithms with a 99% precision rate and the lowest Mean Absolute Error (9.43). A brief assessment of potential overfitting was conducted to ensure that the CHAID model's high accuracy was interpreted cautiously and supported by the validation results. The fuzzy expert system with validated rules was adaptable for deduction classification, especially for cases unsuitable for quantitative modeling. ORIGINALITY/VALUE: The hybrid model improves detection and prevention of deductions, offering actionable insights for hospital administrators, insurers, and policymakers. Its implementation can enhance hospital information systems, streamline claims processing, and optimize revenue management amid financial constraints.
PURPOSE: This study aims to assess AI literacy and attitudes among medical students and explore their implications for integrating AI into healthcare practice. DESIGN/METHODOLOGY/APPROACH: A quantitative research design was employed to comprehensively evaluate AI literacy and attitudes among 374 Lusaka Apex Medical University medical students. Data were collected from April 3, 2024, to April 30, 2024, using a closed-ended questionnaire. The questionnaire covered various aspects of AI literacy, perceived benefits of AI in healthcare, strategies for staying informed about AI, relevant AI applications for future practice, concerns related to AI algorithm training and AI-based chatbots in healthcare. FINDINGS: The study revealed varying levels of AI literacy among medical students with a basic understanding of AI principles. Perceptions regarding AI's role in healthcare varied, with recognition of key benefits such as improved diagnosis accuracy and enhanced treatment planning. Students relied predominantly on online resources to stay informed about AI. Concerns included bias reinforcement, data privacy and over-reliance on technology. ORIGINALITY/VALUE: This study contributes original insights into medical students' AI literacy and attitudes, highlighting the need for targeted educational interventions and ethical considerations in AI integration within medical education and practice.
OBJECTIVES: This study aimed to explore the most impactful clinical practice experiences of final-year nursing students and the future-oriented actions developed in response to these experiences. METHODS: A retrospective descriptive qualitative design was used. Following reflection training in the internship practice course, 134 final-year nursing students were asked to describe the experience that affected them most during clinical practice. A total of 123 written reflections were analyzed using content analysis. RESULTS: Three themes emerged: near-miss events, incivility behaviors, and positive preceptoring roles. Negative experiences were mainly related to patients, relatives, and nurses and often led students to feel fear and inadequacy. Students reported action plans focused on effective communication, safe patient care, and becoming positive role models. CONCLUSIONS: These findings highlight the importance of supportive clinical learning environments and positive professional socialization during the transition from student to nurse. IMPLICATIONS FOR INTERNATIONAL AUDIENCE: Nursing students worldwide may encounter incivility and near-miss events during clinical practice, potentially adversely affecting their learning experiences and professional development.
The ecological effects of large herbivores are shaped by their spatial and temporal patterns of activity (i.e. where, when and how intensely they use specific locations). When large herbivores' ecological influences are perceived to be undesirable, the traditional approach has been to reduce their population size. This numbers-first logic assumes that ecological effects scale primarily with abundance. We argue that this framing provides an incomplete understanding of large herbivores' ecological impacts. Using African elephants (Loxodonta africana) as a well-documented case study, we show that ecological effects on plants, animals and ecosystem processes correlate more with spatio-temporal patterns of activity than with population size. In large, open systems characterized by strong gradients of water availability, forage quality, shade and risk, elephants concentrate into predictable hotspots while relaxing activity elsewhere, generating localized impacts and opportunities for recovery. By contrast, in small, fenced or fragmented landscapes, where movements are constrained, and gradients are weak, spatial self-regulation breaks down, producing homogenized use and widespread ecological effects. We contend that understanding where, when and under what constraints herbivores use space provides a more general and mechanistic basis for interpreting ecological influence than abundance alone, with implications that extend beyond elephants to large herbivores globally.
Fecal microbiota transplantation (FMT) holds therapeutic promise beyond recurrent Clostridioides difficile infection, but clinical outcomes remain unpredictable and donor-selection strategies remain limited, in part because the role of donor‒recipient metabolic interactions in shaping the post-FMT community remains poorly understood. Here, we leverage metagenome-scale metabolic modeling to quantify metabolic niche complementarity between donor and recipient microbiomes and predict post-FMT community composition. Using MICOM-derived metabolic models, we show that donor genomes whose metabolic flux profiles are more dissimilar from the recipient community colonize at significantly higher rates in a murine FMT model. In a human IBS trial, the same metric predicted post-FMT community composition via leave-one-out cross-validation and captured known disease-associated alterations in short-chain fatty acid, sulfur, and gas metabolism. We then performed 2,548 in silico FMT simulations between IBS-D/M patients and donors from the OpenBiome biobank to evaluate personalized donor screening, identifying super-donors characterized by high taxonomic diversity, broad metabolic niche coverage, and community interaction networks dominated by cross-feeding rather than competition. Together, these results support metabolic niche complementarity as a potential determinant of post-FMT community composition and provide a mechanistic basis for evaluating donor-recipient metabolic compatibility. This framework offers a scalable approach for generating testable hypotheses for personalized donor selection.
Epithelial ovarian cancer (EOC) is a heterogenous disease with frequent late-stage diagnosis and high mortality rates, for which no reliable screening tests exist. In recent years, epigenetic biomarkers in the form of DNA methylation in CpG-rich regions have gained increased attention in the scientific community due to their robust nature and accessibility, allowing for diagnosis without the need for invasive surgery. In this study, we investigated the aberrant methylation of promoter regions in early stage EOC through non-parametric methods, with the purpose of characterizing candidate epigenetic biomarkers. The approach was used on a cohort of early stage EOC samples, and results were compared to existing programs for differential methylation. Significant regions were then used to construct a CpG panel for stratifying EOC histotypes through predictive classification in external data. Identified promoter regions were highly reproducible across cohorts, and the constructed CpG model stratified histotypes in external cohorts through predictive classification. Comparisons against other DMP and DMR callers showed a degree of homogeneity between results but also revealed promoter regions that were overlooked despite clear signs of aberrant methylation. Finally, EOC histotypes were found to differ in their methylation distribution types, and results indicate that methods sensitive to non-normally distributed data may be poorly suited to compare groups with different distribution types. The non-parametric approach identified aberrantly methylated promoter regions that were highly reproducible across cohorts. Results from predictive classification indicate that these regions may be useful for the purpose of EOC histotype stratification.
BACKGROUND: The global health threat of antimicrobial resistance involves the human, animal and environmental sectors. Data from Cameroon are scarce. OBJECTIVES: This study aimed to define extended-spectrum beta-lactamase-producing Escherichia coli (ESBL-Ec) rates and associated risk factors across the three sectors in Douala, Cameroon, and to define molecular characteristics of isolates. METHODS: From June 2022 to May 2023, we collected blood cultures from hospitalized patients, rectal swabs from healthy pregnant women, caeca from broiler chickens and environmental wastewater. Samples were screened for ESBL-Ec using CHROMAgar™ ESBL and cefotaxime-supplemented Tryptone Bile X-glucuronide agar. Antimicrobial susceptibility testing was performed by disk diffusion following EUCAST guidelines. Whole-genome sequencing was carried out using Illumina technology. RESULTS: Of 628 samples, 374 yielded ESBL-Ec. Prevalence was 54.6% (131/240) in pregnant women, 70.4% (169/240) in chickens and 93.1% (67/72) in wastewater. The proportion of ESBL-Ec among E. coli-positive-blood cultures was 9.2% (7/76). Multi-family household living was independently associated with ESBL-Ec carriage among pregnant women (adjusted odds ratio = 1.7, 95% CI 1.0-3.1, P = 0.03). High co-resistance (>70%) was observed for tetracycline, ciprofloxacin and trimethoprim/sulfamethoxazole. Sequencing of 32 isolates revealed 45 distinct resistance genes, including blaCTX-M-15 (n = 13, 40.6%), blaCTX-M-55 (n = 11, 34.4%) and last-resort antibiotic resistance genes mcr-1 and bla OXA-181. High-risk sequence types included ST131 (pregnant women) and ST10 (chickens). Notably, ST48 was shared between pregnant women and chickens, and ST155 between pregnant women and wastewater. CONCLUSION: Cross-sectoral ESBL-Ec in Douala exhibits high genomic diversity and alarming resistance. The occurrence of last-resort genes requires immediate One Health surveillance and coordinated interventions.
PURPOSE: To determine whether ATF3 polymorphisms can serve as genetic biomarkers for machine learning-based precision analgesia by establishing a genotype-phenotype association suitable for predictive modeling of postoperative opioid requirements. METHODS: In a prospective cohort of 167 adults undergoing abdominal surgery, ATF3 SNPs rs3122721 and rs3125293 were genotyped. A structured dataset architecture was developed to represent genetic profiles as input features for supervised learning models, enabling translational analysis of genotype‑dependent opioid consumption over 72 h. RESULTS: Patients with homozygous genotypes of the ATF3 SNPs had significantly higher opioid requirements than non‑carriers, despite reporting similar subjective pain scores. This consistent genotype‑dependent pattern provided a clinically relevant phenotype suitable for integration into predictive algorithms. CONCLUSION: ATF3 genotyping offers a promising biomarker for computationally informed precision analgesia. By linking genomic variability to clinically meaningful outcomes within a structured clinical and genomic framework, this approach supports the future development of risk-stratified clinical decision-support systems to optimize postoperative pain management.Trial registration ChiCTR1900021991, registered 30 April 2019. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at https://doi.org/10.1007/s13755-026-00480-9.
AIMS: To describe exercise haemodynamics across cardiac damage stages and evaluate the incremental prognostic impact of cardiac damage stage and exercise-induced pulmonary hypertension (exPHT) in patients with symptomatic moderate aortic stenosis (AS) and asymptomatic severe AS. METHODS AND RESULTS: A total of 436 consecutive patients with ≥ moderate AS (74 ± 10 years, 32% women, 56% severe AS) underwent cardiopulmonary exercise testing with echocardiography. The primary endpoint was heart failure (HF) death and HF hospitalizations. Cardiac damage stage was 0 in 93 patients, 1 (LV damage) in 135, 2 (LA/mitral damage) in 135, and 3-4 (pulmonary vasculature/tricuspid or RV damage) in 73. Higher stages were associated with worse exercise capacity and haemodynamics. Over a median follow-up of 37 months, 65 patients met the primary endpoint. After adjustment for age, AS severity, and aortic valve replacement, cardiac damage stage and exPHT were independently associated with HF outcomes [HR per stage increase 1.51 (1.26-1.82); P < 0.001; exPHT HR 2.36 (1.10-5.07); P = 0.03]. exPHT improved risk stratification in early-stage disease (stages 1-2), conferring an approximately five-fold higher risk of HF events in patients with exPHT [HR 4.45 (1.58-12.59); P < 0.01]. CONCLUSION: In patients with ≥ moderate AS and discordant symptoms, cardiac damage stage and exPHT independently refined HF risk stratification. ExPHT provides incremental prognostic value in early damage stages (1-2), representing over half of the cohort, supporting a stepwise approach of routine damage staging with selective with exPHT assessment with exercise echocardiography in this subgroup to guide more personalized management and potentially optimize AVR timing.
AIMS: This study investigated the incremental prognostic value of cardiovascular magnetic resonance (CMR)-derived extracellular volume fraction (ECV), a marker of diffuse interstitial fibrosis, beyond late gadolinium enhancement (LGE) in hypertrophic cardiomyopathy (HCM). METHODS AND RESULTS: We analysed 990 consecutive HCM patients (median age 58 years, male 68.3%) who underwent CMR between 2012 and 2024. LGE and global ECV were quantified, and their associations with the primary endpoint of HCM-related events-a composite of sudden cardiac death (SCD) events, heart failure (HF) events, and HCM-related death-were assessed. During a median follow-up of 3.2 years, 64 (6.5%) patients experienced the primary endpoint. While LGE (median 7.1%, IQR 2.3-16.9%) and ECV (median 29.0%, IQR 26.6-32.0%) were moderately correlated (R = 0.604, P < 0.001), both were significantly associated with increased risk of the primary endpoint and individual outcomes of SCD and HF events, and optimal cutoffs were determined as LGE ≥ 27% and ECV ≥ 35%. Patients with ECV ≥ 35% had more symptoms, a more severe phenotype with greater systolic and diastolic dysfunction, and more pathogenic gene variants. Notably, ECV remained a significant predictor of the primary endpoint (adjusted HR 1.08, 95% CI 1.02-1.15, per 1%) after adjustment for key disease variables, including left ventricular ejection fraction and LGE. Elevated ECV effectively identified high-risk individuals even among lower-risk subgroups, including those with low LGE burden. CONCLUSION: Increased ECV is an independent predictor of HCM-related outcomes. ECV may serve as a novel imaging biomarker to refine risk stratification in HCM patients who do not meet traditional LGE-based high-risk criteria.