PLOS Biology

BackgroundMultiple sclerosis (MS) is an immune-mediated demyelinating disease of the central nervous system affecting 2.8 million people worldwide. Both genetic and environmental factors contribute to MS risk, with Epstein-Barr virus (EBV) infection being an important environmental factor. To better clarify the role of EBV in MS, we examined its impact on gene expression, chromatin accessibility, and transcription factor binding in primary B cells and EBV-transformed B cells derived from patients with MS and healthy controls. ResultsRNA-seq and ATAC-seq analyses revealed extensive MS-dependent gene expression and chromatin accessibility differences in EBV-transformed, but not in primary B cells. These changes are largely accounted for by the expression levels of EBNA2, an EBV-encoded transcriptional regulator previously implicated in MS. ChIP-seq analysis revealed that EBNA2 binding with its interacting human partners RBPJ, EBF1, and PU.1 is highly enriched at MS genetic risk loci, with extensive EBNA2 allelic binding and increased enrichment at MS genetic risk loci in MS-derived cells. ConclusionsOur findings demonstrate that enhanced EBNA2 activity in MS alters human gene expression, chromatin accessibility, and transcription factor binding in an MS-dependent manner. Collectively, this study provides new insights into the molecular mechanisms through which EBV, particularly EBNA2, interacts with host genetic risk to contribute to MS pathogenesis.

The terms personalized, individualized and precision medicine are increasingly used to describe health interventions, yet their operational meaning in clinical research remains unclear. Despite extensive conceptual discussion, there is limited empirical evidence on how these labels are applied in randomized controlled trials (RCTs) and whether such trials meet standards of transparency and methodological rigor. We systematically examined 262 RCTs published between 2020 and 2022 that used the terms "personalized", "individualized", or "precision" in the title to describe an intervention. The term "personalized" was used most frequently (49.2%), followed by "individualized" (45.8%) and "precision" (5.0%). In most trials, personalization involved behavioral, digital, or pharmacological interventions, with few studies employing -omics approaches. Personalization was most often based on individual lifestyle factors, psychological characteristics, or disease classification. We also found that in most trials, personalization consisted of tailoring a single intervention to individuals (82.8%), often through individualized dosage (73.2%). Most included RCTs were judged to be at high risk of bias and showed limited transparency with respect to data and code sharing. Our study suggests that, in contemporary RCTs, the labels "personalized", "individualized", and "precision" are applied interchangeably to a wide range of heterogeneous interventions that are predominantly non-genomic. Greater conceptual clarity and stronger methodological standards are needed to ensure that claims of personalization in clinical research are empirically meaningful and reliable.

IntroductionBuruli ulcer (BU) is a neglected tropical disease primarily affecting skin and sometimes bone. Standard therapy consists of rifampicin (RIF, once daily) plus clarithromycin (CLA, twice daily) over 8 weeks. Adding amoxicillin-clavulanate (AMX/CLV) may shorten treatment, but predicting treatment success before clinical trial implementation is challenging. AimsTo assess the probability of bacterial eradication following treatment with novel investigational BU regimens over different intervals using a mechanism-based modelling and simulation approach. MethodsIn vitro time-kill assays with RIF, CLA, and AMX/CLV alone and in combination were performed with a range of clinical isolates of Mycobacterium ulcerans. Bactericidal activity was characterized using a bacterial growth dynamics model, including an Emax function to describe the drug effect. Subsequently, clinical trial simulations were performed to evaluate drug disposition and skin penetration in a cohort of virtual subjects, taking into account interindividual variability in pharmacokinetics and pharmacodynamics (n=70/arm). Several regimens, including standard therapy and AMX/CLV-containing combinations with higher RIF doses were assessed. The probability of eradication at 4-8 weeks was assessed across strains with different susceptibility and assuming varying bacterial load at start of treatment. ResultsBeta-lactam containing combinations resulted in higher potency and maximum killing rates relative to the currently recommended regimens. Consequently, regimens containing AMX/CLV with higher RIF doses (20 mg/kg q.d. or 10 mg/kg b.i.d.) outperformed standard therapy, achieving 100% eradication within 4 weeks for baseline loads up to 1,000 CFU/mL across most isolates, except one from China. At higher loads (10,000 CFU/mL), 6 weeks were required. ConclusionsThe use of mechanism-based modelling and clinical trial simulations provides a robust translational framework for the evaluation of novel therapies for neglected diseases, such as BU. Irrespective of differences in bacterial susceptibility, adding AMX/CLV or using RIF-AMX/CLV dual therapy may reduce BU treatment from 8 to 4 weeks.

Digital health technologies are powerful-enhancing data collection, participant engagement, and personalized health interventions-yet their rapid proliferation has outpaced guidance for research participant protection. Current practice assists researchers in identifying risks but provides limited support for comprehensive risk management. To address this gap, we developed the Digital Health Checklist-Risk Management (DHC-RM) Tool, which integrates the established Digital Health Checklist with approaches from safety risk management. We conducted a study (n=40) comparing the DHC-RM Tool with current practice using a randomized experimental difference-in-differences design. Primary outcomes were the quantity, variety, and novelty of risks identified; secondary outcomes were the same constructs applied to risk control development. Compared with current practice, use of the DHC-RM Tool resulted in dramatically improved performance across all primary outcomes. Users identified on average 14.7 additional risks (compared to baseline) versus 0.26 in the control group and a higher number of risks in each of six pre-identified risk domains. Half of all distinct risks identified in the comparison phase were identified exclusively using the tool. The tool also improved risk control design, producing 9.63 additional risk control strategies per participant compared with 0.15 for current practice and yielding substantially greater novelty and variety. User feedback was also positive: 75% of participants reported they would use the tool again, citing its structured workflow, just-in-time examples, improved insight into risks, and its value for IRB communication. Suggestions for refinement focused primarily on expanding training examples and providing additional support for risk control development. The DHC-RM Tool significantly improves risk management practice in digital health research. By embedding structured, ethics-informed risk management into digital health research design, the DHC-RM Tool has the potential to improve participant protection while also streamlining ethics approval. Author SummaryDigital health research can put participants (and others) at risk in ways that dont always occur to the researchers who are designing a study. Researchers also face challenges in prioritizing risks and coming up with ideas to reduce those risks. We developed a new approach, the Digital Health Checklist - Risk Management Tool (DHC-RM Tool), to give researchers the support they need to identify, assess, and address research participant risks in this fast-moving field. Our experimental study found that use of the DHC-RM Tool led to a very large improvement in how well researchers managed the risks of digital health research studies. Using the toolkit, they were able to identify more risks than they identified using current practice-including risks they would not otherwise have considered. They were also able to come up with more changes to reduce the risks associated with digital health research studies, including changes they would not otherwise have considered. Those who used the toolkit found it beneficial and easy to use. The DHC-RM Tool fills an important gap in the science and practice of participant protection in digital health research.

BackgroundThe number of problematic randomized clinical trials (RCTs) has risen sharply in recent decades, posing serious challenges to the integrity of the healthcare evidence ecosystem. ObjectiveTo investigate whether retraction of problematic RCTs could reduce evidence contamination. DesignRetrospective cohort study SettingA secondary analysis of the VITALITY Study database. Participants1,330 retracted RCTs with 847 systematic reviews. MeasurementsThe difference in the median number (and its interquartile, IQR) of contamination before and after retraction. The association between time-to-retraction and likelihood of evidence contamination. ResultsAmong these retracted RCTs, 426 led to evidence contamination, resulting in 1,106 contamination events (251 after retraction vs. 855 before retraction). The time interval between RCT publication and first contamination ranged from 0.2 to 30.9 years, with a median of 3.3 years (95% CI: 3.0 to 3.9). The median number of contaminated systematic reviews was lower after retraction than before retraction (0, IQR: 0 to 1 vs. 1, IQR: 1 to 2, P < 0.01). Compared with trials retracted more than 7.5 years after publication, those retracted between 1.0 and 1.8 years (OR = 0.70, 95% CI: 0.60 to 0.80) and retracted within 1.0 year (OR = 0.69, 95% CI: 0.60 to 0.80) were associated with lower likelihood of evidence contamination. LimitationsOnly assessed contaminated systematic reviews with quantitative synthesis and limited to retracted RCTs. ConclusionsRetracting problematic RCTs can significantly reduce evidence contamination, and faster retraction was associated with less contamination. To safeguard the integrity of the evidence ecosystem, academic journals should act promptly in the retraction of problematic studies to minimize their downstream impact. Primary Funding SourcesThe National Natural Science Foundation of China (72204003, 72574229)

BackgroundClimate change is increasingly recognised as a threat to population health and healthcare systems, yet the effects of environmental variability on pharmaceutical prescribing remain poorly characterised in the UK. Using a wide array of open-source datasets, we examine the effect of environmental, geographic and socioeconomic factors on prescribing habits in England. MethodsWe linked monthly, practice-level prescribing data for England (2010-2025) to meteorological, air-quality, flooding and demographic datasets using spatial nearest-neighbour matching. Prescribing volumes for cardiovascular, respiratory and antibiotic medications were analysed using log-transformed outcomes in mixed-effects models with practice-level random effects, adjusting for region, seasonality, deprivation and temporal trends, using both continuous environmental measures and extreme-condition indicators. A complementary Bayesian hierarchical model jointly estimated the conditional effects of multiple correlated environmental exposures, with partial pooling across practices and support for distributed lag effects. ResultsIn mixed-effects analyses, temperature showed the most consistent associations with prescribing, with higher temperatures linked to increased respiratory and cardiovascular prescriptions and reduced antibiotic use, while rainfall, flooding and most pollutants had small or negligible effects. Environmental predictors exhibited strong correlations, motivating multivariate modelling. Bayesian multivariate models confirmed temperature as the dominant environmental driver after adjustment for correlated exposures, with substantially larger variation attributable to regional and socioeconomic factors than to environmental conditions. ConclusionsTemperature is the most consistent environmental determinant of GP prescribing in England, with higher temperatures associated with increased cardiovascular and respiratory prescribing and reduced antibiotic use. Rainfall, flooding and most air pollutants show little evidence of meaningful effects once seasonal and meteorological structure is accounted for. Environmental associations are modest in magnitude relative to persistent socioeconomic and regional drivers of prescribing, indicating that climate-related influences operate within broader structural determinants of healthcare utilisation. These results suggest that, at monthly timescales, prescribing demand is relatively stable to environmental variability, supporting a focus on long-term adaptation and surveillance rather than short-term demand shocks in climate-resilient healthcare planning.

The host response to Neisseria gonorrhoeae is variable, and understanding its systemic and local components is critical to understanding anti-gonococcal immunity for vaccine development. We used a controlled human infection model of male gonococcal urethritis in naive volunteers in combination with multiplex cytokine analyte analysis of blood and urine specimens taken before infection, at the time of acute symptoms, and after curative treatment of N. gonorrhoeae to study responses to early infection. (This study utilized data and specimens from all 11 participants assigned to control arms of two previous randomized clinical trials). All 11 participants developed urethritis between 2 and 5 days post inoculation with N. gonorrhoeae strain FA1090, with a majority having visible discharge by day 3. In urine, we found increases in IL-1RA, G-CSF, and chemokines CXCL10, CCL4, CCL11, GRO/{beta}/{gamma}, and IL-8/CXCL8, with IL-1RA and CCL4 showing direct correlation with the degree of pyuria at the time of infection. Contrary to a prior study using the human challenge model and N. gonorrhoeae strain MS11mkC, we did not see similar increases in urine IL-6, TNF-, or IL-1{beta}, although differences in IL-6, TNF- were observed in participants with later development of infection. Additionally, plasma cytokine levels were unchanged in this cohort over the course of their infection, suggesting these infections were confined to the urethra. We propose that differences in strain virulence or the threshold to define a clinical case may be responsible for this discrepancy, meriting further study and continued use of non-invasive inflammatory markers to study local effects in addition to systemic effects of gonococcal infection. Author SummaryGonorrhea, caused by the bacterium Neisseria gonorrhoeae, remains a global public health concern, yet repeated infections are common and no vaccine is available. A key challenge for vaccine development is limited understanding of how the human immune system responds during early infection, when bacteria are confined to the urethra, vagina, or other mucosal sites. To address this gap, we studied immune responses in a controlled human infection model in which male volunteers with no prior exposure were experimentally infected with N. gonorrhoeae into their urethra. Immune signaling molecules were measured in urine and blood samples collected before infection, during symptoms, and after antibiotic treatment. All participants developed urethral inflammation within a few days of infection. We observed marked increases in multiple inflammatory cytokines in urine, some which correlated with the degree of neutrophils in their urine. In contrast, immune markers in the bloodstream remained largely unchanged. These findings suggest that early infection with the N. gonorrhoeae strain tested triggers a strong localized immune response without widespread systemic inflammation. Our results highlight the value of urine-based, non-invasive sampling and demonstrate the power of human challenge models for studying early immune responses that have been difficult to characterize in animal systems.

The prospective design of vaccine efficacy trials for deployment in outbreaks requires advance consideration of plausible outbreak scenarios, anticipated vaccine characteristics, and logistical and ethical constraints. As part of CEPIs 100 Days Mission to accelerate vaccine development against a novel Disease X, we evaluated trial designs for a hypothetical Nipah-X outbreak. We assumed Nipah-X would share key features with Nipah, including high case fatality rates and substantial super-spreading, but with sustained human-to-human transmission. Using simulations based on infection models, including an extended chain-binomial model incorporating super-spreading, we compared ring-trials using cluster-randomisation with individual-randomisation within rings. High levels of super-spreading markedly reduced the power of cluster-randomised designs due to strong intra-cluster correlations in case numbers, whereas individual-randomisation retained power. These findings highlight that understanding and accounting for super-spreading is critical when designing ring-trials, as cluster-randomised designs may fail unless vaccine efficacy is nearly complete.

PurposeWe introduce PRE-CISE, a pre-calibration workflow that integrates coverage analysis, local sensitivity, and collinearity diagnostics to streamline model calibration and transparently address nonidentifiability. We demonstrate the benefits of PRE-CISE using a four-state Sick-Sicker Markov testbed and a COVID-19 case study. MethodsPRE-CISE begins with a coverage analysis to verify that model outputs generated with parameter sets drawn from their prior distribution span calibration targets, followed by local sensitivities to quantify the influence of parameters on model outputs, guiding the resizing of the prior distribution bounds to improve coverage. Identifiability is then assessed via collinearity analysis; large indices indicate practical nonidentifiability. For the testbed model, we calibrated 3 parameters to survival, prevalence, and the proportion of Sick to Sicker at 10, 20, and 30 years. For the COVID-19 model, we calibrated 11 parameters to match daily confirmed incident cases. Bayesian calibration was conducted on both analyses. ResultsCoverage analyses flagged initial misfits; local sensitivities identified the Sick-to-Sicker transition probability has a greater effect on model outputs, and resizing its prior distribution bounds improved coverage. Collinearity analyses showed that combining multiple calibration targets across time points enabled recovery of all three parameters. In the COVID-19 model, local sensitivity analyses prioritized time-varying detection rates and contact-reduction effects, reducing the search space, thereby improving calibration efficiency. Daily incident case calibration targets yielded collinearity indices below practical thresholds (e.g., < 15) for all parameter combinations, whereas weekly calibration targets were larger and closer to the cutoff. ConclusionsPRE-CISE provides a practical, transparent pathway that helps modelers refine prior distribution bounds and calibration targets before intensive calibration, improving uncertainty reporting and strengthening the reliability of model-based health policy analyses.

BackgroundInterleukin-6 (IL-6) is a cytokine that plays a key role in systemic hyperinflammation and may mediate the relationship between acute COVID-19 and severe long-term outcomes such as Long COVID or death. IL-6 modulating drugs may reduce patients risk of severe post-COVID-19 outcomes. MethodsWe conducted an emulated target trial in a retrospective cohort of patients with moderate-to-severe rheumatoid arthritis who were prescribed IL-6 receptor antagonists (sarilumab or tocilizumab, pooled treatment) or other biologic agents (anakinra or baricitinib, pooled comparator) in 2022. We compared the 12-month cumulative incidence of mortality and Long COVID (diagnosed and probable) between groups using Super Learner and targeted maximum likelihood estimation, adjusting for covariates of interest. ResultsIn our cohort of 3,553 patients, we found that prescription of IL-6 receptor antagonists was associated with a lower 12-month cumulative mortality (adjusted relative risk (aRR) 0.40, 95% CI 0.27, 0.59), diagnosed Long COVID aRR 0.42, 95% CI 0.23, 0.78), and probable Long COVID (aRR 0.71, 95% CI 0.61, 0.83), compared to prescription of other biologic agents, among rheumatoid arthritis patients. ConclusionsIL-6 receptor antagonists may prevent the incidence of severe post-COVID-19 outcomes, such as Long COVID or mortality. This supports the hypothesis that IL-6 may be a mechanistic biomarker of COVID-19 sequelae and that acute COVID-19 severity may mediate this relationship.

BackgroundJournals may respond to integrity concerns by publishing an editorial response (editorial notice, expression of concern (EoC) or retraction). We investigated whether the type of editorial response affected citation rates. MethodsWe obtained citations for 172 randomised controlled trials (RCTs) with integrity concerns (41 had editorial notices, 38 EoCs and 23 retractions) and control RCTs from the same journal and year. Monthly citation rates up to 60 months before and after editorial responses were compared by editorial response type, and to citation decline in control RCTs. Results172 RCTs had 10,603 citations from 6,376 articles. 3,330 control trials were identified for 151/172 RCTs (15,948 citations, 87,811 articles). For both groups, citations increased steadily, peaking 45-65 months post-publication. There were no statistically significant differences in citation decline post-editorial response for trials receiving a retraction, EoC, or notice. Citations were lower in controls than index trials, so analyses were restricted to 1598 highly cited (>25) controls. The rate of decline for highly cited control trials was not statistically significantly different from the post-editorial response rate for index groups. ConclusionCitation rate decline after editorial responses did not differ by type of editorial response nor from the natural decline in control trials. HighlightsO_LIJournals may respond to integrity concerns by issuing an editorial notice. C_LIO_LIThe effect of expressions of concern or other editorial notices on citation patterns is unclear. C_LIO_LIEditorial notices did not accelerate citation decline compared with control trials. C_LIO_LIThe type of notice was not associated with differences in citation decline. C_LIO_LILate editorial notices appear ineffective in preventing continued citation. C_LI

Linezolid is a critical last-resort antimicrobial for multidrug-resistant Enterococcus faecium, particularly against vancomycin-resistant lineages where therapeutic options are severely limited. While resistance has historically arisen through de novo chromosomal mutations, the global emergence of transferable resistance mechanisms threatens to render more infections untreatable. Here, we characterise a recent (2023-2024) hospital-associated outbreak of linezolid-resistant E. faecium in Queensland, Australia. Although the cohort comprised a variety of sequence types, the outbreak was primarily driven by the clonal expansion of an ST80 lineage carrying the plasmid-borne poxtA-Ef gene. Standard short-read genomic surveillance failed to resolve the genetic context of the resistance determinant. However, long-read sequencing revealed that poxtA-Ef was carried within a novel transposon, Tn8026, situated on a linear plasmid. Structural analysis defined Tn8026 as a unique element flanked by IS1678 and the novel insertion sequence ISEfa26. Furthermore, we identified an instance of Tn8026 integration into the chromosome, providing functional evidence of its mobility and capacity for stabilisation within the genome. Global genomic screening demonstrated that Tn8026 significantly predates the local outbreak, identified in a historical Norwegian isolate from 2012, indicating a long-standing yet unrecognised global reservoir. Phylogenomic analysis provided strong evidence that the linear plasmid was imported from the Indian subcontinent, initiating a chain of silent dissemination in eastern Australia where the lineage circulated undetected prior to clinical recognition. Crucially, we also confirmed the presence of the linear plasmid in Enterococcus gallinarum, demonstrating its capacity to mobilise transmissible linezolid resistance across enterococcal species boundaries. These findings emphasise the need for detailed long-read-based surveillance of mobile genetic elements, with a particular focus on identifying linear plasmids that are often overlooked.

Mendelian randomization has emerged as a transformative approach for inferring causal relationships between risk factors and disease outcomes. However, applying Mendelian randomization to disease progression - a critical step in validating pharmacological targets - is hampered by index event bias. This form of selection bias occurs because analyses of disease progression are necessarily restricted to individuals who have already experienced the disease event. Here, we present a comprehensive evaluation of statistical methods designed to mitigate index event bias, including inverse-probability weighting, Slope-Hunter, and multivariable methods. We compare the performance of these methods in simulations and applied examples. Inverse-probability weighting methods reduce bias, but require individual-level data and will only fully eliminate bias when the disease event model is correctly specified. Slope-Hunter performed poorly in all simulation scenarios, even when its assumptions were fully satisfied. Multivariable methods worked best when including genetic variants that affect the incident disease event. However, if these genetic variants also affect disease progression directly, then the analysis will suffer from pleiotropy. Hence, if the same biological mechanisms affect disease incidence and progression, then multivariable methods will have little utility. But in such a case, analyses of disease progression are less critical, as conclusions reached from analyses of disease incidence are likely to hold for disease progression. Our findings indicate that no single method is a universal solution to provide reliable results for the investigation of disease progression. Instead, we propose a strategic framework for method selection based on data availability and biological context.

Drug repurposing offers the opportunity to identify promising drug targets efficiently using existing data, but there are currently limitations to these efforts; there is a particular need for versatile, but rigorous high-throughput approaches. As such, we developed a flexible, high-throughput, Mendelian randomization (MR)-based drug repurposing pipeline with three stages: 1) MR-based identification, 2) MR-based validation and prioritization, and 3) application. This pipeline can be applied to a broad range of clinical characteristics and diagnoses, including binary and continuous traits. Along with this flexibility, it offers rigorous quality control and validation. In Stage 1, the pipeline conducts MR analyses to identify proteins as potential drug targets (exposures) for a specified trait/condition (outcome). The MR analysis includes quality control steps, such as testing for heterogeneity, horizontal pleiotropy, and Bayesian colocalization. In Stage 2, MR analysis with quality control is conducted with significant results from Stage 1 (exposures) for either the same (external cohort only) or a related outcome. Drug targets with a consistent direction of association in Stages 1 and 2 are then assessed in Stage 3, which queries DGIdb, a database of druggable therapeutic targets. To demonstrate the utility and flexibility of this pipeline, we applied it to atherosclerotic cardiovascular disease. Using UKB-PPP cis-pQTLs as instruments for 2,923 circulating proteins, we assessed causal effects on LDL-C and triglycerides levels from the GLGC (Stage 1) and validated lipids-associated targets with a large coronary artery disease GWAS (Stage 2). Stage 3 mapped 6 proteins that interact with approved drugs, highlighting drug repurposing opportunities.

ImportanceFunding of womens health research has been low, with a narrow focus on what is considered womens health. Understanding which lifespan stages and areas of womens health are funded is essential to determine the breadth of womens health research and identify where gaps in research are concentrated. ObjectiveTo examine which lifespan stages and areas of womens health were more likely to be funded in open Canadian grant competitions. Evidence ReviewPublicly available funded Canadian Institutes of Health Research (CIHR) Project Grant abstracts from 2009 and 2023 were coded for mention of a hormonal transition period (puberty, menstrual cycle, pregnancy/postpartum, perimenopause/menopause), exogenous hormone use (hormonal contraception, fertility treatments, menopause hormone therapy), and/or a female-specific health condition. Abstracts were also coded for Indigenous health and Two Spirit, Lesbian, Gay, Bisexual, Trans, Transgender or Trans Identified, Queer, Intersex, Asexual, Plus (S2/LGBTQIA+) populations. Remaining grant abstracts were grouped by common theme.Abstracts were analyzed for changes in research representation and funding over time and whether funding was lower than expected based on population prevalence or proportion of the lifespan spent in that stage. FindingsNearly 50% of female-specific research focused on cancers (breast, gynecologic) or pregnancy and did not significantly increase in funding or representation over time. Of the funded grant abstracts that focused on pregnancy, ~22% examined outcomes pertaining only to the fetus/offspring, not the birthing parent. Over 15 years, 2.37% of all CIHR abstracts over 15 years were devoted to pregnancy, whereas only 0.24% was devoted to other hormonal life stages (menstrual cycles, menopause). For all hormonal transition stages except pregnancy, the proportion of grants and funding devoted to that stage was lower than expected based on the proportion of the lifespan spent in that stage. Conclusions and RelevanceThese findings reflect the narrow breadth of womens health, which largely focused on cancers (breast, gynecologic) or pregnancy, rather than being distributed across key life course stages that shape womens health. To advance science for all, the heterogeneity and complexity in womens health across the lifespan must be embraced and barriers for womens health research must be removed. Key PointsO_ST_ABSQuestionC_ST_ABSWhich areas and life stages of womens health are most likely to be funded in Canadian open grant competitions, and where are funding gaps concentrated? FindingsNearly half of female-specific grants focused on cancer or pregnancy, with little change over time. Pregnancy dominated hormonal-stage research, often excluding maternal outcomes, while menstrual and menopausal stages were rarely funded. For most life stages, funding was lower than expected based on lifespan representation. MeaningWomens health research funding remains narrowly focused. Broader, life-course-inclusive investment is needed to address critical gaps and advance equitable health science.

Natural language processing was applied to 3,586 Australian health practitioner tribunal decisions (1999-2026) to identify patterns in professional misconduct, outcomes, and temporal trends at a scale impractical through manual analysis. A text classification approach categorised 2,428 disciplinary decisions across seven misconduct types with acceptable accuracy for the major categories (per-class F1 0.47-0.82). Boundary violations were the most prevalent misconduct type (30.2%), followed by dishonesty/fraud (29.7%) and professional conduct breaches (28.0%). Reprimand was the most common outcome (53.0%), followed by cancellation (40.2%). Significant increasing trends were identified for boundary violations, dishonesty/fraud, professional conduct breaches, and communication failures. Boundary violations were associated with higher cancellation odds (OR = 1.36, p < 0.001). Opioid medications appeared in 67% of prescribing misconduct decisions. Significant jurisdictional variation in both misconduct types and outcomes was observed, with large effect sizes between major jurisdictions. The findings provide an empirical foundation for monitoring disciplinary trends under the National Law.

BackgroundVariation in the HLA loci, located on human chromosome 6p, has been associated with hundreds of diseases and conditions. However, high levels of polymorphism that characterize the HLA system, coupled with generally modest effect sizes for most phenotypes, necessitate relatively large sample sizes to power association studies; meanwhile, high resolution HLA genotyping remains relatively resource intensive. These constraints limit identification of novel associations. While phenome-wide association studies (PheWAS) in the context of large registries with available electronic health records (EHR) have revealed new insights into the role of HLA in disease, many common health conditions are poorly represented in EHR due to the temporal nature of their occurrence or general underreporting. Further, these studies have generally been conducted with HLA genotyping data imputed from microarrays, rather than direct measurement of high-resolution genotypes. ObjectiveTo overcome these limitations and reveal novel HLA associations we undertook a PheWAS in many previously understudied health conditions. MethodsWe queried over 300 hundred conditions, diseases and traits from 70,724 subjects registered with NMDP with available high-resolution HLA genotyping (HLA-A, HLA-B, HLA-C, HLA-DRB1, and HLA-DQB1). After stratifying according to ancestry, we performed a logistic regression analysis adjusting for sex and age for HLA-phenotype association. ResultsWe identified 48 significant HLA associations across ancestry groups, confirming several known associations and uncovered fifteen novel associations. Most novel associations pertained to common infectious or allergic phenotypes that often go under-reported in the EHR. Of particular translational importance, we identified a previously undetected yet very strong association between HLA-DRB1*04:01 and sensitivity to cefaclor, a specific class of cephalosporin (OR = 3.74, p-value 5.10E-28). Molecular docking simulations predict cefaclor binding in the P4 pocket of HLA-DRB1*04:01, with substantially greater affinity than non-associated antibiotics, including other cephalosporins. This pharmacogenomic signal highlights an opportunity for risk stratification and targeted prevention of adverse drug reactions. Other novel associations found, such as susceptibility to genital warts (HPV) and allergic rhinitis, reveals new insights into the role of specific HLA alleles in immune-mediated disease. The vast majority of these novel associations were replicated in the independent All of Us cohort, confirming the validity of this approach. ConclusionCollectively, our findings demonstrate the value of integrating population-scale, high-resolution HLA genotypes with phenotyping beyond the EHR to reveal immunogenetic influences on common health outcomes. They also point to immediate translational avenues - particularly for drug hypersensitivity - while motivating future functional studies and prospective clinical validation to refine mechanistic understanding and clinical utility.

Accurate identification of unknown pathogens is critical for medicine and public health, yet current metagenomic workflows remain heavily dependent on specialized bioinformatics expertise and manual interpretation, creating substantial bottlenecks in time-sensitive diagnostic settings1. The key challenges lie in achieving precise species identification amidst high background noise and translating complex microbial data into clinically actionable insights2,3. Here we present the Global Pathogen Analysis System (GPAS), an integrated computational framework that combines rapid and accurate pathogen identification with large language model (LLM)-based semantic interpretation. Central to GPAS is a dynamic-library alignment mechanism informed by prior probabilities of inter-species misclassification. By integrating a hybrid machine learning model that couples elastic neural networks with Bayesian inference, this approach substantially reduces both false positives and false negatives, achieving species-level accuracy superior to existing state-of-the-art tools. To enable clinical interpretation, we constructed a unified microbial knowledge graph integrating global metagenomic and metaviromic sample repositories, and trained a pathogen-specialized LLM agent. Through end-to-end reinforcement learning, the agent autonomously executes multi-step reasoning workflows extracting pathogen-specific insights from complex data and generating human-readable, evidence-based reports. Application to throat swab samples demonstrates that GPAS not only accurately identifies pathogenic microorganisms but also reveals how SLE-associated immune dysregulation reshapes the respiratory microbiome and promotes pathobiont overgrowth, providing clinically instructive interpretations. By substantially lowering technical barriers to pathogen identification, GPAS offers an accessible yet powerful platform for clinical diagnostics, public health surveillance, and microbiome research. The system is freely available at: https://gpas.nh.ac.cn/.

An H3N2 variant, named subclade K, continues to circulate widely during the 2025-2026 influenza season. This virus possesses a hemagglutinin (HA) protein that has eleven substitutions relative to the HA of the Northern Hemisphere 2025-2026 H3N2 vaccine strain. Many of these substitutions are in epitopes in well-characterized HA antigenic sites. Despite this, interim vaccine effectiveness studies indicate that the 2025-2026 influenza vaccine provides moderate protection against H3N2 subclade K infection. We previously reported that many individuals who received the 2025-2026 influenza vaccine produced antibodies that inhibit H3N2 subclade K virus cellular attachment. Here, we show these individuals also produced antibodies that neutralize H3N2 subclade K virus infection, and we observed a strong correlation between hemagglutination-inhibition titers and neutralizing antibody titers. We completed additional specificity studies using samples from individuals who did or did not have antibodies that cross-reacted to H3N2 subclade K viruses. Using high-throughput neutralization assays, we determined that antibodies that bound to the vaccine strain but not H3N2 subclade K viruses typically targeted antigenic site B of HA. Conversely, we found that cross-reactive neutralizing antibodies elicited by vaccination commonly targeted antigenic site A, D, and E of HA that are conserved between the vaccine strain and H3N2 subclade K viruses. Additional electron microscopy-based polyclonal epitope mapping studies confirmed that cross-reactive antibodies elicited by vaccination typically target epitopes on the side of HA. Together, our studies provide an immunological explanation of why the 2025-2026 influenza vaccine was partially effective against antigenically advance H3N2 subclade K viruses. Our data suggest that vaccine strains for subsequent seasons need to be carefully considered, since subclade K viruses have already started to acquire additional substitutions in HA antigenic sites targeted by cross-reactive antibodies.

Inflammatory bowel disease (IBD) frequently co-occurs with immune-mediated and metabolic disorders, but whether these associations reflect shared genetics or causal effects remains unclear. We performed two-sample Mendelian randomization (MR) using large-scale genome-wide association study (GWAS) summary statistics to investigate potential causal effects of immune-mediated diseases and lifestyle traits on IBD, Crohns disease (CD), and ulcerative colitis (UC). SNP-based heritability and genetic correlations were estimated to contextualize findings. Following false discovery rate correction, genetically predicted psoriasis was positively associated with IBD (OR 1.15), CD (OR 1.23), and UC (OR 1.10), with the strongest effect observed for CD. Genetically predicted type 2 diabetes mellitus (T2DM) showed a modest inverse association with UC (OR 0.88). No lifestyle-related traits remained significant after correction. Sensitivity analyses indicated heterogeneity across instruments and evidence of directional pleiotropy in selected models, whereas no pleiotropy was detected for the T2DM-UC association. These findings support a role of psoriasis-related immune pathways in IBD susceptibility and suggest a potential inverse association between genetic liability to T2DM and UC.