In forensic genetics, the allele and haplotype frequencies of relevant populations are required to determine the weight of evidence for DNA matches. In recent years, ethical concerns have been raised about population studies published in forensic journals and compiled in Forensic Genetics Frequency Databases (FGFDs), particularly regarding consent procedures for sample collection, the overrepresentation of vulnerable groups, and the risk of donor re-identification. A comprehensive review of ethical practices in forensic population genetics research on identity DNA polymorphisms and ancestry informative markers was conducted, focusing on papers published in Forensic Science International: Genetics and indexed in MEDLINE (2007-2025) and Forensic Science International: Reports (2019-2025). A decline in the number of published forensic population genetics papers was observed following the adoption of dedicated ethical guidelines in 2020. However, increased attention to ethical issues, such as the need for informed consent (IC) of sample donors and approval by pertinent ethical review boards (ERB), predates 2020, with a linear increase over time in the proportion of papers reporting IC and ERB approval. Among the included papers, 48.7% were conducted by or in collaboration with law enforcement laboratories, 39.1% involved minority populations, and 14.3% used forensic casework samples, all of which represent potential sources of ethical concern. In most studies conducted by multinational teams, researchers from the countries of the sampled populations were involved, with the notable exception of Africa, with 31.0% of studies having no African-affiliated authors. Furthermore, even after 2020, a substantial proportion of studies (11.8%) reported complete genotypes of DNA markers associated with a high risk of re-identification. The most common form of non-compliance with the 2020 ethical guidelines was the failure to report written informed consent (40.0%). Overall, the proportion of studies classified as ethically "low risk" according to the Forensic Database Advisory Board increased significantly after 2020, reaching 78.5% of the papers published in that period. Notably, during the preceding period (2010-2020), the proportion of low risk studies was significantly higher for DNA markers for which editorial guidelines required quality control by an FGFD (42.1%) than for other markers (30.3%). To support editors and peer reviewers, suggestions are proposed to improve ethical guidelines for the publication of forensic population genetics data, with particular attention to ERB approval documentation, secondary use of biological samples, and recognition of the role of researchers from the country of population samples collection in multinational studies.
Anatomical variations are common and often clinically important, yet their description frequently relies on isolated case reports or small series with heterogeneous reporting quality. Incomplete documentation of specimen characteristics, inconsistent anatomical definitions and terminology, insufficient description of visualization methods, limited quantitative measurements, and suboptimal figures all reduce reproducibility and limit comparison and data synthesis. To address the lack of an anatomy-specific quality assessment, the Anatomical Variation Quality Assessment Tool (AVQAT) is introduced as a structured instrument intended for authors, reviewers, and editors. AVQAT comprises two sections organized into nine domains. Section A evaluates technical and descriptive quality across six domains: general aspects, specimen characteristics, anatomical definition, methods of visualization, quantitative description and measurements, and quality and use of figures. Section B evaluates interpretative and contextual quality in three domains: differential diagnostic considerations, identification and integration of previous studies, and ethical and regulatory considerations. Each domain contains targeted items that can be assessed systematically using "Yes," "No," or "Not applicable" responses. The tool is designed to complement, not replace, generic case-report guidance by focusing on the specific demands of anatomical documentation and thereby facilitating higher-quality reporting and more reliable evidence synthesis in scoping and systematic reviews.
Interprofessional education (IPE) equips healthcare students with collaborative skills essential for patient-centred care. However, fostering engagement in online IPE remains challenging, even as technological advancements enable cross-border learning. This study investigates the effects of virtual reality (VR)-enhanced education on interprofessional learning and student engagement within an online clinical environment. A randomised controlled cross-over trial was undertaken with 102 undergraduate healthcare students across universities in Hong Kong, Mainland China, South Korea, and Thailand. Participants experienced two online orthopaedic modules and were initially assigned to either the intervention group (Group A: VR-based learning) or the control group (Group B: traditional case-based learning). After a three-week washout period, groups switched modalities so every student experienced both approaches. Self-perceived interprofessional skills (SPICE-R), learner empowerment (LES), and online student engagement (OSE) were measured at baseline (T0), after module one (T1), and after module two (T2). Data were analysed using a linear mixed model that accounted for intervention group, time point, and individual differences. At T1, both groups showed significant improvements in interprofessional skills, with Group A (VR) attaining higher scores (SPICE-R: 16.44; 95% CI: 14.07, 18.82; p < 0.001) compared to Group B (13.91; 95% CI: 11.52, 16.31; p < 0.001). Notably, VR learning produced significantly greater increases in online engagement (OSE: 5.65; 95% CI: 1.43, 9.87; p = 0.009) and learner empowerment (LES: 6.97; 95% CI: 1.44, 12.50; p = 0.014) versus case-based learning. At T2, following crossover, students who experienced VR first sustained higher interprofessional skill gains (d = 4.79; 95% CI: 2.00, 7.58; p = 0.001). Correlations between SPICE-R, OSE, and LES strengthened over time. VR-empowered educational technology significantly enhances interprofessional skills, learner empowerment, and online engagement in global healthcare education. VR integration offers a promising strategy for developing collaborative competencies in diverse, international cohorts. Virtual Reality based educational technology enhances learner empowerment, student engagement, and interprofessional learning in online global healthcare education.
Coronary computed tomography angiography (CCTA) is emerging as a valuable adjunct for chronic total occlusion (CTO) percutaneous coronary intervention (PCI), particularly for lesions in which angiography incompletely defines procedural anatomy. In this systematic review, the authors evaluated the role of CCTA in CTO diagnosis, lesion characterization, prediction of guidewire crossing and procedural success, and procedural guidance. CCTA provides a detailed assessment of key features that directly influence CTO PCI strategy and outcomes. Randomized and observational data suggest that preprocedural CCTA can improve procedural planning, increase technical success in complex lesions, and support safer and more efficient CTO PCI through fluoroscopic co-registration and other real-time guidance applications. These findings highlight the clinical value of CCTA as a tool that can enhance case selection, optimize crossing strategy, and improve procedural success in contemporary CTO PCI.
Radiomics holds promise for extracting quantitative biomarkers from CT images of hepatocellular carcinoma (HCC), but its clinical translation is hampered by poor reproducibility caused by variations in image acquisition, manual segmentation and feature extraction. This multicenter study assessed the reproducibility of both segmentation and radiomic features in patients with early, intermediate and advanced HCC. Forty-five patients (15 per centre; 5 per tumor stage) from three institutions underwent contrast-enhanced CT. Three radiologists independently delineated lesion and perilesion regions on arterial (HAP) and portal venous phases (PVP) using 3D‑Slicer. Segmentation quality was assessed with the Jaccard index; masks with < 25% concordance were manually revised. Using the SlicerRadiomics module, 851 features per phase were extracted, and reproducibility was measured via the generalized concordance correlation coefficient (GCCC) before and after revision. Median Jaccard values were 0,40, 0,56and 0,74, between center 1 and 2, between center 1 and center 3, and between center 2 and 3, respectively. Twenty-seven segmentations underwent manual revision based on Jaccard index. After manual revision Lesion features (mean GCCC 0,65) were more reproducible than perilesional ones (0,52), and PVP produced slightly higher concordance than HAP. Before revision, mean GCCC ranged from 0.45 to 0.68 (median 0.48-0.77); early-stage lesions had the highest reproducibility and advanced-stage the lowest. After revision, lesion mean GCCC improved to 0.71 (HAP) and 0.67 (PVP), with larger gains in intermediate-stage tumours, while perilesional concordance improved modestly. Radiomic feature reproducibility depends on HCC stage and region. Early HCC lesions yield more reproducible features compared to advanced tumours. Manual revision enhances concordance, particularly for lesion regions, but defining peritumoral tissue remains challenging.
This study compared 3 methods for teaching caries detection in bitewings: a prerecorded lecture, a preannotated dataset, and an artificial intelligence (AI)-based web application. Fifty-two dental students annotated carious lesions in 50 bitewings using minimum bounding boxes. After initial annotations, students were divided into 3 groups according to the training method: the Lecture Group (n = 16) received a prerecorded lecture on caries detection in bitewings, the Dataset Group (n = 17) had access to 50 bitewings annotated by a dentist, and the AI Group (n = 19) used an AI-based web application. After training, all students annotated caries in 50 previously unseen bitewings. Student annotations before and after training were compared to a reference standard of 3 experienced dentists. The evaluation was stratified according to the training method and stage of studies: preclinical (n = 16), junior clinical (n = 15), and senior clinical (n = 21). All training methods significantly improved the mean number of errors, intersection over union of matching annotations, and accuracy. Sensitivity increased significantly in the Dataset Group (from 0.62 ± 0.14 to 0.78 ± 0.08) and the AI Group (from 0.68 ± 0.15 to 0.73 ± 0.12), as opposed to the Lecture Group, where a significant increase in specificity was observed (from 0.94 ± 0.09 to 0.96 ± 0.05). The stage of studies impacted the results; the extent of improvement decreased with increasing clinical experience. While the 3 training methods varied in their impact on the confusion matrix components, they yielded comparable overall improvements. The AI-based web application could serve as an educational tool for caries detection in bitewings, especially for dental students with limited clinical experience. This study shows that learning bitewing caries detection with an AI tool yields improvements comparable to other tested e-learning methods. Evaluating and comparing established e-learning and AI teaching methods is key to optimising AI-assisted education for better learning outcomes in dental training.
Health-related quality of life (HRQoL) is a vital indicator of evaluating care outcomes and prognosis, yet little is understood about its developmental trajectories in older patients with chronic pain. This study aimed to identify latent HRQoL trajectories and their predictors, and to develop explainable machine learning models for predicting HRQoL deterioration. This prospective cohort study assessed 608 older patients with chronic pain at admission and at 1, 3, and 6 months post-admission, collecting data on HRQoL, general characteristics, pain level, activities of daily living (ADL), depression, and perceived social support. Growth mixture modeling was applied to identify trajectories of physical and mental HRQoL. Predictors were selected using LASSO regression and SVM-RFE. Nine explainable machine learning models were developed for both components, and SHAP interpreted the outputs. An HRQoL decision-support dashboard was developed to facilitate potential clinical application. Three physical HRQoL trajectories were identified: Stable High, Decline and Low Stability, alongside two mental HRQoL trajectories: Improvement and Decline. Key predictors included education level, pain duration, pain level, ADL, depression, and perceived social support, with ADL and pain level being the most influential for physical and mental HRQoL, respectively. This dual-trajectory study identified five distinct HRQoL patterns in older patients with chronic pain, elucidating key predictors via explainable machine learning. The proposed HRQoL decision-support dashboard may provide an interpretable tool to support understanding of predictive relationships and assist healthcare professionals in HRQoL assessment. Not applicable.
Interferon beta therapy (IFN-β) can induce production of antibodies (NAbs) that may neutralize both therapeutic IFN-β and endogenous IFN-β, potentially impairing antiviral immunity. To investigate whether NAbs against IFN-β occur in multiple sclerosis patients that developed natalizumab-associated progressive multifocal leukoencephalopathy (NTZ-PML). Seven NTZ-PML patients were matched with natalizumab-treated patients with prior IFN-β exposure. All were tested for NAbs against IFN-β in serum. NAbs against IFN-β were absent in all NTZ-PML cases, but present in three matched patients. These findings do not suggest a contributing role for IFN-β NAbs in natalizumab-associated PML.
Major advances have broadened the therapeutic landscape of inflammatory skin diseases. In psoriasis, alongside the tyrosine kinase 2 (TYK2) inhibitor deucravacitinib, the United States Food and Drug Administration (FDA) has approved the first oral interleukin-23 (IL-23) receptor antagonist peptide, icotrokinra. In atopic dermatitis, new data have emerged on stapokibart, an interleukin-4 receptor alpha subunit (IL-4Rα) inhibitor introduced in China. In hidradenitis suppurativa, glucagon-like peptide-1 receptor agonists (GLP-1 RA) are also gaining increasing attention as adjunctive therapy.
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Stochastic models can be highly computationally expensive. This limits the range of parameters and scenarios that can be realistically explored. Previously, a queuing network model was developed for the insulin-stimulated intracellular translocation of the glucose transporter GLUT4. Whilst one hypothesis of insulin action was tested, alternative hypotheses were too computationally expensive for parameter inference. In this study, a deterministic surrogate model is developed for the queuing network. The surrogate model uses feedback terms in a system of differential equations to approximate the blocking mechanisms seen in the queuing network. A sensitivity analysis of the surrogate model was performed and its correspondence to the queuing network assessed. This surrogate model may be useful in a parameter inference recalibration process, allowing posteriors for the queuing network to be acquired with lower computational cost.
Soil microbiomes are increasingly recognized as valuable indicators in forensic investigations, but microbial dynamics in mass graves remain poorly understood. This study investigates differences in microbial succession between individual graves (IG) and mass graves (MG) with human body donors and evaluates the potential of soil microbiome data to predict post-burial interval (PBI). Using ASV-level assessment, we analysed soil samples collected over time from both grave types in a controlled decomposition experiment. At the final timepoint (M18), MG and IG soils exhibited significantly different microbial compositions, with specific taxa, some associated with specific decomposition stages, enriched in each context. A regression model trained on IG samples predicted PBI with a mean error of 2.68 months when adjusted for seasonal variation but performed poorly on MG samples (RMSE = 7.12 months), highlighting ecological complexity and reduced generalisability. These findings underscore the importance of studying MG-specific microbial processes and caution against applying models developed from single-body burials to mass grave contexts. As mass graves are encountered in humanitarian and criminal investigations and establishing the duration of burial can be an important component of forensic reconstruction, our findings highlight the value of further research into context-specific microbiome models and their integration alongside existing methods for detection and time estimation in complex burial environments.
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Here, we introduce the potential of plasma technology for future lunar agriculture. The use of discharge plasma to convert nitrogen, oxygen, and water into reactive species for nitrogen fixation is discussed. This study demonstrated the effectiveness of dinitrogen pentoxide as a nitrogen fertilizer for rice cultivation in lunar regolith simulants. This technology also shows promise in regulating plant growth and enhancing plant immunity, addressing the challenges of lunar agriculture.
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