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PubMed2026

Privacy, security, and reliability risks of artificial intelligence in healthcare: a systematic review of empirical evidence.

BACKGROUND: Artificial intelligence (AI) is increasingly integrated into healthcare information systems, supporting clinical decision-making, imaging analysis, and predictive modeling. While these applications offer operational and clinical benefits, they also introduce emerging risks to patient privacy, data security, and system reliability. OBJECTIVE: To systematically review empirical evidence on privacy breaches, security vulnerabilities, and misuse associated with AI applications in healthcare settings. METHODS: PubMed, Embase, Web of Science, Scopus, IEEE Xplore, and ACM Digital Library were searched for empirical studies published between January 2015 and November 2025 that evaluated AI use or misuse in clinical diagnosis, treatment, or decision-making. Two reviewers independently screened studies and extracted data using a standardized form. Findings were synthesized narratively due to heterogeneity in study designs, AI methods, and reported outcomes. RESULTS: Of 7,285 records identified through database searches and 205 through citation screening, 22 empirical studies met the inclusion criteria, spanning multiple clinical domains and data modalities, predominantly medical imaging applications. Five recurring threat categories were identified: patient re-identification, membership inference, unauthorized access and adversarial exploitation, input manipulation, and misuse or overinterpretation of AI outputs. Across studies, AI models were shown to encode latent biometric signals across diverse data types, limiting the effectiveness of traditional anonymization and synthetic data approaches. Adversarial attacks and input manipulation were also shown to compromise diagnostic performance and system integrity. CONCLUSION: This systematic review provides empirical evidence suggesting that contemporary AI systems in healthcare introduce privacy and security risks that may challenge traditional assumptions about data protection. These findings underscore the need for privacy- and security-by-design approaches and governance frameworks that address risks across the AI lifecycle.

Humans↗

PubMed2026

Nursing students' perspective of dignity: A systematic review.

AIM: This review synthesizes research on nursing students' perceptions of dignity shaped by education and clinical experiences. BACKGROUND: Respect for human dignity is central to nursing ethics. While dignity is well studied in patient care, less focus has been given to how nursing students perceive and experience dignity during their education. DESIGN: A systematic review of quantitative, qualitative, and mixed-methods studies. METHODS: A comprehensive search was conducted across the Medline, PubMed, ScienceDirect, Scopus, and Web of Science databases for English-language studies published from 2000 to 2025. Quality was assessed using the Joanna Briggs Institute Critical Appraisal Checklist and the Mixed Methods Appraisal. Tool. Data were synthesized thematically. Reporting followed PRISMA guidelines. RESULTS: A total of 24 articles were included. Students viewed patient dignity as linked to respect, privacy, and autonomy. Although students had solid theoretical knowledge, they encountered institutional and professional barriers in delivering dignified care. Supportive environments and participation in decision-making enhanced their sense of dignity. Finally, educational strategies such as role-playing, simulations, and empathy exercises enhanced emotional and psychosocial awareness. CONCLUSIONS: These findings indicate that dignity is crucial in shaping nursing students' professional identity and ethical awareness throughout their education. Reinforcing ethical values and dignity in nursing education is essential for high-quality care.

Humans↗

PubMed2026

Implementing a novel digital health platform for self-management of postmenopausal osteoporosis: A qualitative study of user experiences, perspectives and implementation outcomes.

BACKGROUND: Osteoporosis self-management requires scalable support, and digital health platforms may meet this need. This study aimed to characterise the experiences and perspectives of postmenopausal women who participated in a 12-month randomised controlled trial (RCT) of a digital voice assistant (DVA) delivered osteoporosis self-management intervention, and to assess key implementation outcomes. METHODS: This was a qualitative analysis of interviews with postmenopausal women from the intervention arm (DVA group) of the RCT. The DVA program broadcast education videos, medication reminders, home-based exercise, nutrition advice and monthly quizzes through a DVA device. Semi-structured interviews were recorded, transcribed and managed in NVivo through reflexive thematic analysis, guided by the Practical Planning for Implementation and Scale-Up and Proctor's implementation outcome taxonomy frameworks. Evidence weighting summarised participant coverage and code density. RESULTS: Twenty-two of 25 (88%) DVA group participants completed semi-structured interviews. Thematic analysis identified seven themes mapped to Proctor's implementation outcomes. Evidence weighting indicated strong support for the intervention's appropriateness and acceptability, moderate support for its adoption, fidelity, feasibility and sustainability, and limited support for costs. Participants valued clear audiovisual guidance, conversation-based interactions with natural language, and flexible home-based access to self-management. CONCLUSION: Digital health platforms for osteoporosis self-management appear feasible, acceptable and sustainable among postmenopausal women. Findings indicate that these platforms are approaching readiness for evaluation in implementation-focused settings, contingent on streamlined content, reliable delivery modalities, accessible user support, clear privacy regulations and pragmatic pricing models.

Humans↗

PubMed2026

Data-centric, robust, and explainable multimodal deep learning for clinical decision support: A systematic review.

PURPOSE: Multimodal deep learning is increasingly proposed for clinical decision support (CDS) under a "data-centric" framing that prioritizes label quality, missing-modality robustness, distribution shift, calibration, and explainability. Prior reviews have examined multimodal medical AI, CDS, and data-centric methods separately, but none address their intersection. We mapped the modalities, fusion strategies, and data-centric and explainability techniques used in this recent literature, quantified how often each is implemented rather than merely mentioned, assessed deployment-relevant evidence (external validation, clinical-outcome measurement, equity), and formally appraised study-level risk of bias. METHODS: Following the PRISMA 2020 statement (PROSPERO CRD420261427815; registered retrospectively), we screened 150 records and included primary, clinical, multimodal studies that applied machine or deep learning to a decision-support task and reported at least one quantitative result. Two reviewers screened and extracted data with consensus adjudication. Each study was coded against pre-specified operational definitions, separating implemented or empirically evaluated techniques from those only mentioned. Study-level risk of bias was assessed with PROBAST + AI. Synthesis was narrative. RESULTS: Thirty-one studies met inclusion; 30 (97%) were published between 2024 and 2026, with a median of three modalities (range 2-6), most commonly structured EHR (71%) and imaging (39%). Data-centric techniques were frequently reported (74-84% across label-noise, distribution-shift, calibration, missing-modality and class-imbalance handling; equity 61%). However, external validation was reported in only 4/31 studies (13%), a clinical or provider outcome in 3/31 (10%), and no study reported routine deployment. Overall risk of bias was high in 27/31 studies (87%), driven by the analysis domain. CONCLUSION: Within this recent, self-selected slice of the field, technical robustness and explainability techniques are widely reported but rarely validated out-of-distribution or against clinical outcomes, and the underlying evidence is at high risk of bias. Progress requires external multi-site validation, clinical-outcome measurement, formal bias appraisal, and adherence to AI reporting standards (e.g., TRIPOD + AI) before deployment can be justified.

Deep Learning↗

PubMed2026

Effects of H-coil TMS on suicidality in major depression: A secondary analysis of data from a multisite randomized trial comparing accelerated to once-a-day stimulation.

Suicide is the 10th leading cause of death in US adults. Standard once-daily repetitive transcranial magnetic stimulation (rTMS) can reduce suicidal ideation. Yet, antidepressant and anti-suicidal effects often take several weeks to emerge, while rapid improvement is often required. Accelerated TMS has been proposed as a strategy to hasten therapeutic response. A recent FDA-regulated multicenter trial evaluated accelerated intermittent theta burst Deep TMS with the H1-coil versus standard high-frequency Deep TMS in MDD. Both groups demonstrated high remission and response rates for depression, with the accelerated protocol showing non-inferiority and a shorter time to remission. The goal of this exploratory secondary analysis was to evaluate the impact of these two H-coil TMS dosing paradigms on suicidal ideation. The Scale for Suicide Ideation (SSI), as well as suicidality items of HDRS, MADRS and CUDOS were collected and analyzed. On all scales, both accelerated and standard Deep TMS protocols were associated with meaningful reductions in suicidality. The accelerated protocol achieved a faster onset of improvement. Comparison between the timeline of improvement in suicidality and in overall depressive symptoms found a trend for faster improvement in suicidality, especially with the accelerated protocol. These findings highlight the importance of treatment frequency in determining time to clinical benefit and support the use of scalable accelerated protocols for patients requiring more rapid symptom relief.

Humans↗

PubMed2026

Artificial neural network data fusion-mediated dual-mode sensor based on Fe3O4@PdIr for Salmonellatyphimurium detection in food.

Salmonella Typhimurium (S. typhimurium) is a major foodborne pathogen that poses a serious threat to public health. In this study, a colorimetric/electrochemical dual-mode biosensor assisted by artificial neural network (ANN) was developed for the sensitive detection of S. typhimurium. Fe3O4@PdIr nanocomposites with enhanced peroxidase-like activity and electrochemical performance were prepared and conjugated with an aptamer specific to S. typhimurium to obtain Fe3O4@PdIr-Apt. Through the sandwich binding of Fe3O4@PdIr-Apt and Apt to the target, the nanocomposites were attached to microplates or Au electrodes, thereby generating colorimetric and electrochemical signals. The ANN model deeply resolved the complex nonlinear relationship between the dual signals, enabling mutual correction and ultimately performing data fusion to output a single detection result, which significantly reduced the mean square error while improving detection sensitivity and reliability. This sensor exhibited a wide linear range of 2.7-2.7 × 108 CFU/mL and a low detection limit of 1.66 CFU/mL. Additionally, this method was successfully applied to the detection of S. typhimurium in pork and milk, with a recovery rate of 95.19% ∼ 104.07%. It indicated that the constructed sensor holds great practical potential for S. typhimurium detection.

Neural Networks, Computer↗

PubMed2026

Pembrolizumab-Chemotherapy Versus Pembrolizumab in Head and Neck Squamous Cell Carcinoma: A PD-L1 CPS-Stratified Analysis of Updated KEYNOTE-048 Data.

Based on KEYNOTE-048, pembrolizumab monotherapy and pembrolizumab-chemotherapy are established category 1 first-line treatments for recurrent/metastatic head and neck squamous cell carcinoma (HNSCC) with programmed death ligand-1 (PD-L1) combined positive score (CPS) ≥ 1. We compared their efficacy using updated trial data. We analyzed 4-year progression-free survival on next-line therapy (PFS2) and 5-year overall survival (OS) data from KEYNOTE-048 by reconstructing time-to-event data using KMSubtraction. Efficacy was compared in CPS 1-19 and CPS ≥ 20 subgroups using Kaplan-Meier estimates, Cox models, restricted mean survival time (RMST), and landmark analyses. Among 499 patients with CPS ≥ 1, 240 (48.1%) had CPS 1-19 and 259 (51.9%) had CPS ≥ 20. In the CPS 1-19 subgroup, pembrolizumab-chemotherapy showed numerically longer median PFS2 (10.1 vs. 8.0 months; hazard ratio [HR]: 0.81; 95% confidence interval [CI]: 0.62-1.06) and OS (12.8 vs. 10.8 months; HR: 0.87; 95% CI: 0.67-1.15) versus monotherapy, without statistical significance. For CPS ≥ 20 patients, efficacy was comparable between regimens, with similar median PFS2 (11.3 vs. 11.7 months; HR: 0.95) and OS (14.7 vs. 14.9 months; HR: 0.96). RMST and landmark analyses showed an early PFS2 benefit and a trend toward OS benefit with pembrolizumab-chemotherapy in CPS 1-19, with comparable outcomes in CPS ≥ 20. Pembrolizumab-chemotherapy showed a trend toward improved outcomes in the CPS 1-19 subgroup, with comparable efficacy in the CPS ≥ 20 subgroup, supporting a refined first-line strategy: monotherapy for CPS ≥ 20 to minimize toxicity, and combination therapy for CPS 1-19 to potentially enhance disease control.

Humans↗

PubMed2026

Genome-wide SNP data support species boundaries in sympatric Polylepis Ruiz & Pav. (Rosaceae) species from Bolivia and Ecuador.

Species delimitation in the South American genus Polylepis is notoriously challenging due to high morphological similarity and phenotypic plasticity, likely driven by hybridization and gene flow. Previous phylogenetic studies suggested that genetic structure aligns more strongly with geography than with taxonomy, questioning existing species concepts and hampering conservation efforts. We used double-digest RAD sequencing (ddRADseq) to generate genome-wide SNP data for 11 Polylepis species sampled across multiple localities in Bolivia and Ecuador. Population genetic analyses, phylogenetic inference, and network approaches were combined to assess whether genetic structure aligns more closely with taxonomy or geography. Morphologically defined species formed largely cohesive genetic lineages across regions, with species identity explaining substantially more genetic variation than locality. While localized admixture and reticulation were detected among closely related taxa, widespread species showed strong genetic cohesion and clear separation from congeners. Our results indicate that the sampled Polylepis species from Bolivia and Ecuador maintain distinct genetic identities despite localized signals consistent with gene flow. This genome-wide support for current taxonomy highlights Polylepis as a valuable model for studying speciation under gene flow and indicates that multiple geographic sampling will be essential in reconstructing a robust phylogeny of the genus, with important implications for conservation planning in Andean montane forests.

Bolivia↗

PubMed2026

Novelty seeking and rapid symptom improvement across active and sham accelerated iTBS conditions: A pooled individual-patient data analysis.

INTRODUCTION: Major depressive disorder (MDD) is highly prevalent and often treatment-resistant. Accelerated intermittent theta burst stimulation (aiTBS) is a promising intervention for treatment-resistant depression (TRD), though outcomes vary. Personality traits have been examined in relation to rTMS outcomes, yet their role in aiTBS remains underexplored. This pooled individual-patient-data analysis of two randomized, sham-controlled trials examined associations between baseline Temperament and Character Inventory (TCI) traits and one-week symptom change, and whether they differed by condition. METHODS: The left dorsolateral prefrontal cortex was targeted for 20 sessions over 4 days. Personality was assessed with the TCI, depression severity with the 17-item Hamilton Depression Rating Scale (HDRS-17). TCI-symptom-change associations were examined with a robust linear mixed-effects model, adjusting for age, gender, repeated measurements, and study membership. RESULTS: 104 participants were included (M/F 45/59; mean age 40.9 ± 12.7; active/sham 50/54). The model yielded a Time × Novelty Seeking interaction (β = -1.70, p = 0.021): higher baseline Novelty Seeking was associated with faster symptom reduction, without a between-arm difference. However, the interaction did not survive Holm correction across 14 trait-interaction tests (adjusted p = 0.294) and is therefore exploratory. No other interaction reached the uncorrected threshold. CONCLUSIONS: Higher baseline Novelty Seeking showed a nominal association with faster symptom reduction, without a difference between active and sham conditions. Because it did not survive multiplicity correction and was not reproduced in within-arm analyses, it is preliminary and may reflect contextual or nonspecific processes. Independent replication is required before temperament assessment can be clinically informative.

Humans↗

PubMed2026

Quo vadis, BGA? A collaborative EDNAP exercise on the challenges and progress in forensic biogeographical ancestry inference.

There is a broad consensus that forensic tests for the prediction of externally visible characteristics (EVC) and analysis of biogeographic ancestry (BGA) of an individual are technically reliable. However, interpretation of the results and population-specific genotype distribution patterns remains challenging. EVC and BGA analyses provide valuable information for population genetics studies and as investigative leads for criminal cases, as well as for historical and contemporary identification tests. However, inaccurate or incorrect predictions, for example, from subjective bias in the interpretations made, have the potential to misdirect police investigations. The legal situation regarding EVC and BGA testing varies by country: ranging from countries where it is explicitly prohibited, to those without specific regulations on biogeographic ancestry prediction, and others that have already enacted laws governing its use. The reluctance to utilize these analyses is not only due to legal restrictions and data protection concerns, but also to initial limited sets of sufficiently comprehensive forensic DNA assays. Forensic BGA marker panels typically contain up to ∼300 SNPs. This relatively small number of genetic markers, along with limited reference population data, complicates the interpretation of results from donors of unknown origin. This paper presents the results of a collaborative EDNAP study, which, for the first time, evaluated the approach to reporting EVC and BGA data between international laboratories. For the study, DNA from nine individuals with self-reported ancestry was collected and analysed using various forensic panels differing in the number and composition of ancestry-informative markers genotyped, comprising: the Precision ID mtDNA Whole Genome Panel, the VISAGE Basic Tool and the VISAGE Enhanced Tool for Appearance and Ancestry Prediction, and the Ion AmpliSeq™ PhenoTrivium Panel. To ensure full data protection, all SNP genotypes and uniparental marker haplotypes obtained were not shared with third parties. Instead, the genetic data were analysed using a range of commonly used population analysis software packages. These analysis outcomes were then distributed to twelve European forensic laboratories (both academic and law enforcement institutions), who were asked to prepare reports based on their interpretation of the phenotypes and ancestry they inferred from the analysis data. A questionnaire sent alongside the genetic information, aimed to evaluate which difficulties were encountered by the participants in processing the BGA analysis data they were given.

Humans↗

PubMed2026

Applications of quantum AI in brain disorder diagnosis: A systematic review.

BACKGROUND AND OBJECTIVE: Brain disorder diagnosis and prediction remain challenging because neuroimaging, electrophysiological, behavioral, and multimodal data are high-dimensional, noisy, heterogeneous, and limited by small clinical cohorts. This systematic review synthesised applications of quantum artificial intelligence (QAI) for brain disorder diagnosis, prediction, detection, and monitoring. METHODS: Following PRISMA guidelines, studies published from 2016 to 13 January 2026 were retrieved from Scopus, Web of Science, and IEEE Xplore. After screening, 36 studies met the eligibility criteria and were qualitatively analysed according to disorder category, data modality, QAI method, implementation setting, validation strategy, and performance. RESULTS: At the broader disease-group level, neurodegenerative disorders were the most frequently investigated, followed by mental health and psychiatric disorders. At the individual level, Parkinson's disease and schizophrenia were the leading applications, followed by depression, anxiety, Alzheimer's disease, and stress-related tasks. MRI-based modalities were the most frequently used data source, followed by multimodal data and EEG. Methodologically, primary QAI approaches were dominated by quantum neural and QDL architectures, followed by quantum-inspired optimization or feature-selection methods and quantum-kernel/conventional QML classifiers. Qiskit/IBM Quantum and PennyLane were the most frequently reported quantum software frameworks. However, most studies relied on simulators, classical quantum-inspired implementations, or unclear implementation settings, with limited real-hardware evaluation. CONCLUSIONS: QAI shows emerging potential for brain disorder analysis, particularly through hybrid quantum-classical learning, quantum neural architectures, quantum-kernel methods, and quantum-inspired optimization. Nevertheless, current evidence remains preliminary and requires larger datasets, subject-level and external validation, fair classical benchmarking, noise-resilient circuits, real quantum hardware evaluation, explainability, and clinical validation.

Humans↗

PubMed2026

Trends in demographic and health survey publications based on a bibliometric analysis.

BACKGROUND: The Demographic and Health Surveys (DHS) Program, launched in 1984, provides high-quality population health data that underpins a vast body of global health research. However, the scale and growth patterns of DHS-based publications remain underexplored, particularly as donor funding uncertainties threaten program sustainability. OBJECTIVE: We examine temporal trends in DHS-based research output from 1984 to 2025, quantifying growth patterns and publication delays to inform understanding of the program's global research expansion. METHODS: A systematic bibliometric review was conducted following PRISMA guidelines across PubMed, Scopus, Web of Science, Dimensions, Wiley, and CINAHL. Eligible peer-reviewed articles using DHS data between 1984 and 2025 were identified. Annual publication counts were analyzed, segmented regression identified growth inflection points, and timeliness was assessed by calculating lag between survey completion and publication. RESULTS: Over 10,000 DHS-based publications were identified. Annual output rose from isolated studies in the 1980s to several hundred annually by the 2010s. Segmentation analysis revealed two rapid growth phases: a 56-publications/year increase from 2004-2012, and a 71-publications/year increase from 2012 to 2024. Despite this growth, median lag from survey completion to publication remained approximately 5 years, with only a modest recent improvement (Kendall's τ =  -0.623, p < 0.001). CONCLUSION: DHS data have fueled exponential growth in global health research over four decades, confirming their vital role in evidence generation. However, persistent publication delays highlight the need to shorten the pathway from data collection to dissemination through strengthened research capacity in low- and middle-income countries. Sustained funding is essential to maintain this critical evidence source.

Bibliometrics↗

PubMed2026

Analysis of deep learning techniques in computer-aided diagnosis for meniscus injuries: a systematic literature review.

Meniscus informatics is a growing subject of study in the healthcare industry. One of the major hindrances to the healthcare system's transformation is obtaining knowledge and meaningful information from complicated, high-dimensional and diverse sources. Modern biomedical research, for instance, has seen an increase in the use of complex, dissimilar, poorly documented, and generally unstructured electronic health records, imaging, sensor data and text, even after many current techniques have been used to extract more robust and useful elements from the data for analysis. New efficient standards for building end-to-end learning models from complex data are therefore needed. Therefore, the current study aims to examine the most recent research on the use of deep learning techniques for diagnosing meniscus tears and recommend creating comprehensive and meaningful interpretable structures that might benefit the healthcare industry. We also draw attention to shortcomings and the need for better technique development, and we provide new perspectives about this exciting new development in the field.

Humans↗

PubMed2026

Prioritizing Parkinson's disease risk-associated mitochondrial candidate genes via multi-omics integrative analysis.

BACKGROUND: Mitochondrial dysfunction has been implicated in Parkinson's disease (PD), but the genetically regulated mitochondrial genes associated with PD risk remain incompletely defined. METHODS: We conducted a summary-data-based genetic epidemiology study integrating summary-based Mendelian randomization (SMR), Heterogeneity in dependent instruments (HEIDI) filtering, and Bayesian colocalization to prioritize mitochondrial-related molecular features associated with PD risk. Mitochondrial-related genes were defined using MitoCarta3.0. Genetically predicted gene expression and plasma protein abundance were evaluated using expression quantitative trait loci (eQTL) data from eQTLGen and GTEx v8, and protein quantitative trait loci (pQTL) data was assessed using International Parkinson's Disease Genomics Consortium (IPDGC) as the discovery genome-wide association study (GWAS) and FinnGen as the replication dataset. Prespecified QTL analyses were interpreted using FDR correction, HEIDI filtering, and colocalization support. DNA methylation QTL analysis, mitochondrial phenotype MR, and single-nucleus RNA-seq analysis were performed as complementary analyses. RESULTS: In the primary eQTL analysis, higher genetically predicted TTC19 expression was associated with lower PD risk (OR = 0.80, 95% CI: 0.74-0.87, PPH4 = 0.80), whereas higher MALSU1 expression was associated with increased PD risk (OR = 2.21, 95% CI: 1.59-3.06, PPH4 = 0.96). Both associations survived FDR correction, passed HEIDI filtering, and showed colocalization support. GTEx whole-blood data supported the direction of the TTC19 association. No mitochondrial protein reached significance after FDR correction and colocalization filtering in the primary pQTL analysis. Complementary methylation analysis highlighted cg06270993 as an exploratory regulatory signal for MALSU1. CONCLUSIONS: This MR-colocalization study prioritizes TTC19 and MALSU1 as genetically supported mitochondrial-related candidate genes associated with PD risk. Further validation is required to define their functional roles in PD pathogenesis.

Humans↗

PubMed2026

Educational interventions to improve medical students' bad news communication skills: A systematic review and meta-analysis.

OBJECTIVES: This systematic review aimed to both determine whether educational interventions improve medical students' ability and/or confidence in Bad News Communication (BNC), as well as assess the relative efficacy of instructional formats. METHODS: Performed according to the PRISMA guidelines, four databases were searched for articles describing education-based interventions to improve medical student's BNC ability and/or confidence, published in English between 2001 and 2024. Data on students' self-reported or observer-assessed level of competence/ability in BNC (primary outcome), and students' self-assessed confidence in BNC skills (secondary outcomes), were analysed. Meta regression explained the influence of several categorical moderators on heterogeneity in relation to intervention effects on competence/ability. RESULTS: 27 studies met the criteria for inclusion in the systematic review and 17 studies for the meta-analysis. Interventions described in controlled studies were associated with a moderate and significant increase in BNC ability (13 data sets; standardized mean difference [SMD] = 1.09, 95% CI = 0.52 - 1.66). Interventions detailed in pre-post design studies were associated with a significant increase in BNC ability (20 data sets; SMD = 0.92, 95% CI = 0.52 - 1.32), and student confidence/comfort in their BNC skills (12 data sets; SMD = 1.16, 95% CI = 0.57 - 1.75). Subgroup analysis demonstrated better skills/competence outcomes in studies that included simulation-based training (SBT). CONCLUSIONS: Educational interventions improve the BNC ability and confidence of medical students. Interventions should include an SBT element as this leads to greater improvements in BNC ability. Further research is needed to determine to what extent these interventions translate to positive patient outcomes. PRACTICE IMPLICATIONS: Diverse educational programme, especially those including simulation-based training, are effective in improving BNC skills, although the longetivity of these improvements is at present unclear. Therefore, we recommend that refresher courses or practice opportunities should be scheduled throughout students' medical education to ensure retention of BNC skills.

Humans↗

PubMed2026

Complex evolutionary history of Rosales mediated by extensive incomplete lineage sorting and hybridization.

The angiosperm order Rosales still represents a major challenge for phylogenetic reconstruction. Although its circumscription is now well-defined, phylogenetic relationships among families are still uncertain. Here, we used nuclear, plastid, and mitochondrial genomic data from 33 species representing all nine families to further clarify interfamilial relationships and the group's evolutionary history. We detected significant phylogenetic conflict among the three datasets. Further analyses at the nuclear level identified incomplete lineage sorting (ILS) as the main cause of unstable phylogenetic positions among families. The discordant placements of Rhamnaceae and Elaeagnaceae based on plastid and mitochondrial data are caused by ancient hybridization events, potentially involving differences in organellar inheritance. Our molecular dating confirms earlier suggestions that the ancient rapid diversification of the three Rosaceae subfamilies could be the main reason for the difficulties in resolving their phylogenetic relationships. Our findings provide new insights into the interfamilial relationships of Rosales and demonstrate that the evolutionary history of this order was shaped by ancient and rapid radiation as well as extensive ILS and reticulate evolution. They also suggest that previous attempts to clarify interfamilial relationships in this order were hampered by combining nuclear and organellar sequence data, leading to inconsistent topologies observed across earlier studies.

Phylogeny↗

PubMed2026

Do vaccinated cases transmit measles? A systematic review and meta-analysis.

INTRODUCTION: We reviewed evidence on whether vaccinated individuals can transmit measles and conducted a meta-analysis to understand transmission characteristics. METHODS: We searched and extracted data from peer-reviewed and gray literature and included studies reporting measles transmission events from vaccinated individuals. We meta-analyzed the data to calculate the median number of transmissions from vaccinated cases by measles burden, number of doses, and time since first and last dose. RESULTS: We identified 11,911 peer-reviewed and 22 gray literature records and included 33 articles in our review. Seventy individuals who had received 1 or more doses of measles-containing vaccine transmitted measles virus, resulting in 237 secondary cases. Vaccinated transmitters in eliminated areas were older (median age of 19 years (IQR: 3, 21)) than those in non-eliminated areas (median age 16 years; IQR 13, 18) (p = 0.12). Additionally, 91% (30/33) of studies provided data on subsequent transmission generations, leading to 812 measles cases traced back to vaccinated individuals, with a median of 4 (IQR: 1, 10) cases per vaccinated transmitter. CONCLUSION: Measles transmissions from vaccinated cases, although relatively uncommon, must be considered in public health investigations, as such transmissions can contribute to outbreaks.

Adolescent↗

PubMed2026

Comparative effectiveness of torsemide vs furosemide in the management of heart failure patients: Win-ratio reanalysis of the TRANSFORM-HF trial.

BACKGROUND: Loop diuretics are widely used for managing congestion in patients with heart failure (HF). The TRANSFORM-HF trial is a multicenter randomized study that enrolled heart failure patients, comparing a strategy of torsemide vs furosemide. The time-to-event analysis demonstrated neutral effects on all-cause death at 30 months and the composite of all-cause death and first rehospitalization at 12 months. We evaluated whether a hierarchical win-ratio (WR) framework integrating mortality, recurrent hospitalization, and patient-reported health status provides additional interpretive insight. METHODS: This study is a secondary analysis of the pragmatic, multicenter, open-label, randomized TRANSFORM-HF trial, conducted across 60 US hospitals that randomized 2,859 patients hospitalized with HF to torsemide or furosemide. The primary 12-month hierarchical composite outcome was defined as (1) all-cause mortality, (2) recurrent all-cause hospitalizations, and (3) lack of improvement in the Kansas City Cardiomyopathy Questionnaire Clinical Summary Score (KCCQ-CSS). The primary statistical method was a WR analysis adjusting covariates via inverse probability weighting. Subgroup analyses evaluated potential heterogeneity across patient demographics and clinical characteristics. RESULTS: In the primary 12-month intention-to-treat analysis, the adjusted WR was 1.07 (95% CI, 0.98-1.16; P = .13), indicating no significant difference between torsemide and furosemide. A supplementary 30-month analysis with extended mortality follow-up yielded a similar estimate (adjusted WR, 1.06; 95% CI, 0.98-1.16; P = .14); hospitalization and KCCQ-CSS components were assessed through 12 months. As-treated sensitivity analyses were consistent with the neutral primary findings. Exploratory subgroup analyses were not adjusted for multiplicity and should be considered hypothesis-generating. CONCLUSIONS: The overall WR comparison between torsemide and furosemide showed no statistically significant difference in the primary 12-month analysis. The WR framework provided an interpretive decomposition across outcome domains but did not establish superiority of either loop diuretic strategy. All findings should be considered exploratory. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03296813, https://clinicaltrials.gov/study/NCT03296813.

Aged↗