Infectious Diseases

Background: Inequalities exist for many health outcomes, but there is limited evidence regarding menstrual symptoms despite their importance for health and wellbeing. We aimed to investigate inequalities in menstrual symptoms according to socioeconomic position and childhood adversity. Methods: In two generations (G0 mothers and G1 offspring) from the Avon Longitudinal Study of Parents and Children (ALSPAC), a UK prospective cohort study, we examined associations of multiple indicators of socioeconomic position (SEP) and adverse childhood experiences (ACEs) with menstrual symptoms (pain, abnormal uterine bleeding, and premenstrual syndrome (PMS) measured 3-8-years post-birth in G0 and 17-21-years-old in G1), using multivariable logistic regression. Samples ranged from 4,828 to 9,335 G0 participants and 1,288 to 2,757 G1 participants depending on the exposure-outcome association. Missing data were addressed using multiple imputation and inverse probability weighting. Results: Financial difficulties were associated with greater odds of menstrual pain (G1 OR 1.41; 95% CI 1.07, 1.86: G0 OR 1.55; 95% CI 1.36, 1.76) and irregular cycles (G1 OR 1.60; 95% CI 1.12, 2.29: G0 OR 1.48; 95% CI 1.27, 1.72) in both generations, as well as with short/long cycle lengths in G0 only. Lower education and manual social class were also associated with these three menstrual symptoms in at least one generation. Conversely, higher SEP was associated with PMS in both generations. Higher cumulative ACEs were consistently associated with menstrual pain (4+ compared to none: G1 OR 2.15; 95% CI 1.48, 3.11: G0 OR 1.52; 95% CI 1.29, 1.80) and irregular cycles (G1 OR 1.92; 95% CI 1.20, 3.09: G0 OR 1.54; 95% CI 1.26, 1.87) but not cycle length. Lower parental education, financial difficulties, and cumulative ACEs were associated with heavy bleeding in G1 offspring only, whereas financial difficulties, own manual social class, and cumulative ACEs were associated with prolonged bleeding in G0 mothers only. Higher cumulative ACEs were also associated with PMS in G1 offspring only. Conclusions: We found evidence of inequalities according to socioeconomic disadvantage and childhood adversity for multiple menstrual symptoms, although some associations were only observed in one generation. Findings suggest that menstrual symptoms are disproportionately experienced by socially and socioeconomically disadvantaged women.

Introduction Miltefosine is the only approved oral antileishmanial agent, but its use in women of childbearing potential (WOCBP) is restricted due to preclinical teratogenicity. Current labeling recommends contraception during treatment and for at least five months thereafter, based solely on its long terminal elimination half-life. This study re-evaluated the required contraceptive duration using an exposure margin-based approach. Methods Virtual populations were generated from anthropometric data of 382 Indian, 4,462 Eastern African, and 4,019 Brazilian WOCBP with leishmaniasis. Published population pharmacokinetic models were used to simulate miltefosine exposure following 14-42-day regimens for visceral leishmaniasis (VL), post-kala-azar dermal leishmaniasis (PKDL), and cutaneous leishmaniasis (CL). A developmental safety exposure threshold was derived from the rat no-observed-adverse-effect level (0.6 mg/kg/day for 10 days) and adjusted using a 10-fold safety margin. Contraceptive durations resulting in median residual post-contraception exposure (AUCEOC-{infty}) below this threshold were considered supportive of contraceptive discontinuation. Results The developmental safety exposure threshold was estimated at 2.5 mg{middle dot}day/L. Despite pharmacokinetic differences across geographical regions and disease manifestations, required minimum contraceptive durations were consistent: three months from treatment initiation for the 14-day regimen, four months for 21- and 28-day regimens, and five months for the 42-day regimen. For the 14-day regimen, a single dose of a long-acting injectable contraceptive administered at treatment initiation would provide sufficient coverage. Conclusion An exposure margin-based approach supports shorter contraceptive durations than current recommendations. For the 14-day VL regimen, three months of contraception may provide a practical alternative to current labeling.

The increasing incidence of H5N1 influenza virus transmission from animal species to humans has heightened concerns about an imminent H5N1 pandemic. Prior studies using recombinant hemagglutinin and neuraminidase proteins have reported age-dependent cross-reactivity to H5N1, attributed to immune imprinting from an individual's first influenza virus exposure. However, whether this pattern holds when using whole inactivated virus (WIV), capturing antibodies against diverse viral proteins, and is stable over time remains unknown. We therefore aimed to determine whether H5N1 cross-reactivity of pre-existing antibodies to whole virus follows an age-dependent or imprinting-specific pattern, and whether this pattern is stable over a five-year period. To this end, we measured serum antibody levels in adolescents, adults and seniors by ELISA using whole inactivated H5N1 virus as antigen rather than purified proteins. Detectable, albeit generally low, levels of H5N1-reactive antibodies were present in most individuals, irrespective of age. Comparison of antibody levels against H5N1 with those to five historical influenza virus strains revealed a consistent positive correlation between H5N1-reactive antibodies and responses to the H1N1pdm09 strain A/California/7/2009 (CA), across all age groups. Using unbiased clustering of antibody titers against H5N1, CA, and the H3N2 strain A/Perth/16/2009 (PE), we identified seven distinct age-transcending antibody profiles. These profiles covered individuals with varying titers to all three included viruses but also identified individuals with high anti-CA levels, yet low anti-H5N1 levels and vice versa. Moreover, despite stable antibody levels over a five-year interval in the study population, individual antibody levels and profiles fluctuated considerably over this period. Taken together, our results confirm the presence of H5N1-reactive antibodies in human sera and their association with previously circulating strains. However, they also caution against inferring antibody levels against a new strain based solely on responses to antigenically related strains and highlight the limitations of extrapolating immune status from single timepoint measurements.

Background. The clinical course of coronavirus disease 2019 (COVID-19), and the ability to anticipate which patients will require intensive care, were poorly characterized in Central Asia during the first pandemic wave. We aimed to describe the clinical features of hospitalized COVID-19 patients at the Tashkent State Medical University, Uzbekistan, and to identify risk factors for intensive care unit (ICU) admission. Methods. In this single-centre cross-sectional study, we reviewed the records of 2500 consecutive patients hospitalized between 11 April and 8 August 2020. Patients were grouped as asymptomatic or symptomatic, and symptomatic patients were compared by ICU versus non-ICU status. Groups were compared with chi-square or Fisher's exact and Mann-Whitney U tests. Univariable and multivariable logistic regression identified risk factors for ICU admission. Results. Of 2500 patients (median age 36 years; 60.9% male), 989 (39.6%) were asymptomatic and 1511 (60.4%) symptomatic. In total, 129 (5.2%) were admitted to the ICU and 38 (1.5%) died. ICU patients were older (median 56 vs 40.5 years) and more often had bilateral pneumonia, oxygen desaturation and cardiometabolic comorbidity. In the multivariable model (AUC 0.82), the independent predictors of ICU admission were ischemic heart disease (aOR 4.20), shortness of breath (aOR 3.22), hypertensive heart disease (aOR 2.93) and male sex (aOR 2.00). Conclusions. Older age, cardiometabolic comorbidity and respiratory compromise identified patients at high ICU risk. As one of the first clinical COVID-19 descriptions from Uzbekistan, these data provide a baseline for preparedness in Central Asia.

Background Severe acute respiratory infections (SARI) strain healthcare systems. Sentinel surveillance remains central to SARI monitoring, but routinely collected hospital discharge data offer a scalable, population-wide complement. In Finland, national registers now enable register-based surveillance, yet SARI case definitions remain unevaluated. Aim To evaluate whether routinely collected electronic health records can support register-based SARI surveillance and establish a national case definition. Methods We conducted a retrospective register-based study linking inpatient discharge data from the Finnish Care Register for Health Care (Hilmo) and laboratory-confirmed pathogen notifications from the National Infectious Diseases Register (NIDR). Admissions were aggregated into hospitalisation episodes using generic and pathogen-specific respiratory ICD-10 codes and linked to laboratory-confirmed respiratory pathogens within an admission-centred window. We assessed the impact of diagnostic coding position, laboratory linkage windows and alternative case definitions on age distribution, seasonality and epidemic trend detection. Results We included 145,435 respiratory hospitalisation episodes. Laboratory confirmations clustered around admission, and a -7-to-+3-day window was selected; 51,498 (35.4%) had a linked laboratory confirmation. Specific primary-position diagnoses preserved clear seasonality and age distributions consistent with SARI epidemiology, whereas secondary-position diagnoses showed attenuated seasonality. A combined case definition incorporating specific primary diagnoses and laboratory-supported syndromic episodes produced stable epidemic curves while improving sensitivity over laboratory confirmation alone. Conclusion National discharge and laboratory registers can support robust SARI surveillance in Finland when case definitions are carefully designed. A combined register-based definition balances specificity, sensitivity and feasibility, complementing sentinel surveillance and integrated respiratory monitoring. Keywords Severe acute respiratory infection (SARI); surveillance; electronic health records; ICD-10; case definition; Finland

In primary healthcare centers lacking advanced imaging, community-based active tuberculosis (TB) case finding often relies on basic symptom screening. This approach often misses cases and leads to the inefficient allocation of rapid molecular testing (RMT). We aimed to develop and internally validate a simple clinical triage scorecard to improve TB detection and guide RMT use in resource-constrained settings. We conducted a retrospective cross-sectional study of 15,137 adults ([&ge;]18 years) evaluated within the Zero TB Yogyakarta program (2020-2025). Participants with complete clinical assessments and confirmatory GeneXpert results were included. Using multivariable logistic regression, we identified independent clinical predictors, which were subsequently transformed into an integer-based point scorecard. Model performance was evaluated via discrimination and calibration, utilizing bootstrap resampling (1,000 iterations) for internal validation. Among the 15,137 participants, 251 (1.7%) were GeneXpert-positive. The final multivariable model identified eight independent predictors: age, male sex, body mass index, prolonged cough, hemoptysis, unexplained weight loss, TB contact history, and diabetes mellitus. The model demonstrated strong predictive accuracy, with an optimism-adjusted AUROC of 0.836 and good calibration. When translated to the integer scorecard and compared directly to standard national symptom screening, the scorecard performed (AUROC 0.81 vs. 0.73; p<0.001). At a high sensitivity cut off score of [&ge;] 0, the tool achieved 93.63% sensitivity and 41.33% specificity. This point-of-care clinical scorecard provides higher diagnostic accuracy than standard symptom screening algorithms. By offering flexible operational thresholds, it empowers local health programs to dynamically balance the urgency of case detection with available diagnostic capacity, optimizing GeneXpert allocation where advanced radiological imaging is unavailable.

Background Little evidence is available on the epidemiology of different knee injuries at a whole-of-population level. The objective of this article is to provide accurate estimates of knee injury incidence by harnessing the unique comprehensive, population-wide data of New Zealand's universal no-fault injury insurance provider, the Accident Compensation Corporation (ACC). Methods We obtained insurance claims data from ACC covering all knee injury insurance claims approved between 2015 and 2024. We calculated the number of injuries and the incidence rate per 100 000 population, by injury type, year, sex, ethnicity, and age. Results The total number of injuries increased from 184 710 (4 067 per 100 000 population) in 2015 to 244 155 (4 701 per 100 000) in 2024. The most common injuries were other/unspecified ligament sprains, contusions, and collateral ligament sprains. Ligament and cartilage injuries were more common for males than for females, while contusions were more common for females. Ligament tears and dislocations were more common in younger people (15 to 35 years of age), while cartilage injuries were more common at older ages (40 to 65 years). Discussion and Conclusions The rate of knee injuries observed in this study was higher than previously reported in other settings, probably due to broader coverage of injuries treated in primary and community care settings. A broad range of injuries were common, including those that have received less attention in the epidemiological literature to date. More research is needed on the prevention, burden, and outcomes of different knee injuries, beyond a narrow focus on cruciate ligament injuries.

Objectives To analyse the long-term effects of a cruciate ligament (CL) injury on health and socioeconomic outcomes. Methods We used a comprehensive national injury insurance database to identify CL injuries occurring in New Zealand between 2009 and 2022, and employed a doubly robust staggered difference-in-differences research design to identify the effects of these injuries on outcomes up to 10 years after injury. The outcomes of interest were healthcare use (hospitalisations, emergency department visits, medications, knee replacement surgery for osteoarthritis), associated healthcare costs, and labour market outcomes (employment rates, income, and government benefit payments). Results We identified 61 344 CL injuries for inclusion in the analysis. Over 10-year follow-up, a CL injury resulted in increased healthcare use (0.6 more hospitalizations [95%CI 0.4 to 0.7], 1.7 more days spent in hospital [95%CI 1.3 to 2.1], 0.4 more emergency department visits [95%CI 0.3 to 0.6], 2.5 more outpatient visits [95%CI 1.8 to 3.2], and 4.7 more medications dispensed [95%CI -1.8 to 11.2]) and public healthcare costs ($7 537; 95%CI 5 888 to 9 186), reduced income (-$6 060; 95%CI -11 644 to -475), and increased benefit payments ($1 152; 95%CI 542 to 1 761). Conclusion CL injuries have long-term impacts on healthcare use and socioeconomic outcomes. Strategies to reduce the incidence of CL injuries have the potential to realise large health and economic benefits.

Background: Endometriosis affects approximately 10% of reproductive-age women and is associated with substantial diagnostic delay and heterogeneous symptom presentation. Prior machine-learning prediction models have relied on comorbidity data alone or on small candidate-variant genetic scores, with inconsistent or incompletely reported performance. No study has combined a well-powered, multi-ancestry polygenic risk score (PRS) with environmental, reproductive, and symptom data in a single hybrid model. We developed and evaluated hybrid risk-prediction models integrating a genome-wide, multi-ancestry PRS with clinical and symptom data for endometriosis in the US-based All of Us Research Program. Methods: Among 69,376 participants (15,382 endometriosis cases, 53,994 controls) across six genetically inferred ancestry groups, we computed individual-level PRS values using PRS-CS weights derived from an independent, multi-ancestry GWAS. Five nested logistic regression, random forest, and XGBoost models progressively added age, ancestry, and within-ancestry genetic principal components (Model 1), environmental and reproductive factors (Model 2), symptom and comorbidity indicators (Model 3), all covariates combined (Model 4), and PRS x environment interactions (Model 5). Performance was assessed by AUROC in a held-out test set and 5-fold cross-validation, with class-weighted, Youden-optimized thresholds used for sensitivity, specificity, and predictive values; permutation importance identified top contributors. Pairwise AUROC differences were tested with a Holm-corrected DeLong-type test. Results: Discrimination improved from AUROC 0.63 (PRS, age, ancestry, principal components) to 0.72 for the full model, driven mainly by symptom and comorbidity data. XGBoost consistently outperformed logistic regression and random forest. The PRS ranked among the top individual predictors by permutation importance in nearly every model, alongside age, while genetic and demographic information alone gave only modest discrimination, and PRS x environment interactions did not improve on environmental factors alone. Threshold optimization yielded balanced sensitivity and specificity (~0.67/0.65) versus near-zero sensitivity at a default threshold. Conclusions: Combining the PRS with symptom and comorbidity data gave the best discrimination compared to solely a well-powered, multi-ancestry PRS as a predictor of endometriosis. This study clarifies both the promise and current limits of hybrid genetic-clinical prediction for endometriosis and points to symptom-based phenotyping, molecular subtyping, and external validation as priorities.

Background: Polymorphisms in the N-acetyltransferase 2 (NAT2) gene explain much of the interindividual variation in isoniazid (INH) metabolism and determine risk of toxicities. However, there is limited evidence to guide INH dose adjustment according to the NAT2 acetylator profile in weekly rifapentine-INH tuberculosis preventive therapy (TPT). Methods: In a prospective, multicenter, within-subject PK trial (NCT05413551), adults initiating 3HP in Brazil were assigned genotype-guided INH doses (slow: 5 mg/kg <=300 mg; intermediate: 15 mg/kg <=900 mg; rapid: 25 mg/kg <=1,500 mg) alongside a standard 900 mg flat dose on an alternate occasion. AUC0-24 and C24 were estimated from serial blood samples; a two-compartment Michaelis-Menten population PK model characterized NAT2 effects on clearance. Results: Among 228 participants, 47.4% (108/228) were intermediate, 43.4% (99/228) slow, and 9.2% (21/228) rapid acetylators. Genotype-guided dosing reduced AUC0-24 variability approximately two-fold versus standard dosing (CV 58.8% vs 76.8%) and increased exposure uniformity (median AUC0-24 27.2 [IQR 18.8-41.3] vs 43.2 [27.3-71.0] mg h/L). Among slow acetylators, C24 >0.15 ug/mL decreased from 27/42 (64%) with standard dosing to 1/42 (2%) with genotype-guided dosing (P<0.0001). In 104 participants with intensive PK sampling, rapid acetylators receiving guided doses had AUC0-24 similar to standard-dose intermediate acetylators (42.8 vs 39.5 mg h/L; P=.63). Monte Carlo simulations supported doses of 600, 900, and 1,200 mg for slow, intermediate, and rapid acetylators, respectively. Conclusions: NAT2-guided isoniazid dosing reduced variation in drug levels, averting very low and high AUC and C24. These findings inform genotype-stratified dosing of INH for TPT, which might reduce toxicities and improve outcomes.

Background: Seasonal influenza, commonly known as flu, is an acute respiratory, highly contagious illness caused by influenza viruses. A clear understanding of influenza seasonality is crucial for guiding prevention and treatment strategies, including decisions on vaccination timing to prevent outbreaks. While well documented in temperate regions, data on influenza epidemiology in tropical areas, particularly sub-Saharan Africa, remain limited. We described the types, subtypes and positivity rate of seasonal influenza in Uganda during 2019-2023. Methods: We abstracted data from the National Influenza database on positive seasonal influenza cases confirmed by Polymerase Chain Reaction. The cases were disaggregated by age group, sex, region, month and year of reporting. Using Microsoft excel, we calculated the influenza positivity rate and disaggregated it by strain, sex, age, region and time. Test positivity rate was computed as the number of positive cases as a percentage of the total samples tested. Results: Among 17,957 individuals tested, the overall positivity rate for seasonal influenza was 5% (936 cases). Positivity was higher among males compared to females (7% vs. 4%), with children aged 5-9 years having the highest positivity rate (16%), while individuals aged 50-54 years had the lowest (1%). The median positivity rate was 4%, with a range of 1-16%. Regionally, the central region reported a positivity rate of 5%, with rates across all regions ranging from 5% to 8%. Over time, there was a gradual decline in positivity rates, decreasing from 16.5% in 2019 to 5.3% in 2023. Seasonal influenza exhibited bimodal peaks, with the primary peak occurring between March and May and a secondary peak from October to December. Influenza A was the predominant strain, accounting for 70% of seasonal influenza cases (669/936). Among the Influenza A subtypes, H3N2 was most common, representing 63% of cases (425/669). Conclusions: The declining seasonal influenza positivity rates from 2019 to 2023 and the predominance of Influenza A and H3N2 highlight the need for sustained surveillance in Uganda. Given Influenza A's high genetic variability and potential for novel strain emergence, monitoring circulating strains, informing vaccine development, and implementing targeted interventions for high-risk groups and regions are critical to controlling and preventing outbreaks.

The global dissemination of antimicrobial resistance is increasingly driven by bacterial clones combining antimicrobial resistance with enhanced virulence and environmental adaptability. Escherichia coli sequence type 131 (ST131) has historically been regarded as a major disseminator of the extended-spectrum {beta}-lactamase (ESBL) blaCTX-M-15. However, the emergence of E. coli ST1193 carrying blaCTX-M-15 may represent an ongoing shift in the epidemiology of this resistance determinant. Here, we investigated the prevalence, genomic characteristics, virulence and antimicrobial resistance potential of ST1193 in comparison with ST131. A total of 1,136 E. coli isolates were recovered from touristic and non-touristic environments, hospital-associated samples, and aircraft toilets in Guadeloupe. Isolates were whole-genome sequenced and analysed for antimicrobial resistance and virulence determinants. Additionally, publicly available genomic data comprising 1,215 blaCTX-M-15-positive ST131 and ST1193 isolates were analysed to assess temporal and geographical trends. ST1193 was significantly associated with aircraft-associated samples and exhibited a higher antimicrobial resistance gene burden than ST131, while maintaining a comparable virulence factor content. Analysis of publicly available genomes revealed similar temporal emergence patterns for blaCTX-M-15-positive ST1193 and ST131, with ST1193 showing a more recent distribution and a higher number of deposited isolates in recent years, consistent with a potential ongoing clonal replacement. Comparative genomic analysis identified numerous virulence and adaptation-associated genes shared between both sequence types, while ST1193 additionally carried distinct determinants, including components of the transmissible locus of stress tolerance. Furthermore, quinolone resistance-associated mutations were strongly linked to blaCTX-M-15 carriage, particularly among ST1193 isolates. Together, these findings identify E. coli ST1193 as an emerging high-risk clone with substantial potential for blaCTX-M-15 dissemination. Its association with aircraft-associated samples further highlights the potential role of air travel in long-distance transmission and underscores the need to reconsider current surveillance strategies focused predominantly on ST131.

Background. Urine-based testing offers a promising non-sputum approach for diagnosing paediatric tuberculosis. However, the currently available lipoarabinomannan (LAM) assay shows limited sensitivity in children and is primarily indicated for those living with HIV. Co-detection of LAM with Mycobacterium tuberculosis (Mtb) proteins in urine could provide complementary pathogen-derived biomarkers that improve diagnostic performance. Methods. We developed an ultrasensitive multiplex electrochemiluminescence (ECL) immunoassay to measure Ag85B, CFP-10, ESAT-6, MPT32, and MPT64 in urine. We determined the analytical limits of detection and evaluated the diagnostic performance of individual proteins and LAM using urine samples from children with Confirmed, Unconfirmed, and Unlikely pulmonary tuberculosis enrolled across five high-burden countries (The Gambia, India, Peru, South Africa, and Uganda). Performance was assessed overall, by HIV and nutritional status, and across biomarker combinations. Findings. Urine samples from 630 children were analysed (median age was 4 years [IQR 2-8]; 44% female, 15% living with HIV, 19% underweight, 24% with Confirmed tuberculosis). The ECL assay achieved femtomolar limits of detection (1.5 to 4.0 fM). The sensitivity and specificity of individual Mtb proteins were 12-33% and 98-100%, respectively. Ag85B had the highest sensitivity (33%, 95% CI 26-41) for Confirmed tuberculosis and was similar to LAM. A four-antigen signature (Ag85B, MPT64, MPT32, LAM) was 50% sensitive (95% CI 42-58) and 94% specific (95% CI 90-96), and was significantly more sensitive than LAM alone, in particular among those without HIV. An additional sixteen (10%) of children with Unconfirmed TB had at least one Mtb protein or LAM detected. Interpretation. Multiple Mtb proteins are detectable in paediatric urine with high specificity, and multi-antigen signatures can augment sensitivity versus LAM alone. These findings demonstrate the potential of multi-antigen urine detection for childhood TB and define analytical targets for the development of future point-of-care diagnostics. Funding. National Institutes of Health.

Background: Pro-inflammatory and high-environmental-impact diets both threaten population and planetary health, but whether the two objectives align or conflict across countries is unresolved. We tested whether a supply-based dietary inflammatory index (sDII) is coupled to greenhouse-gas (GHG), land and freshwater footprints, and whether a nutrition-feasible reallocation can lower both simultaneously. Methods: From FAO Food Balance Sheets we built sDII (12 inflammatory-weighted components; construct validity r=0.9999) and five per-capita footprints using three independent life-cycle inventories for 182 national food-supply series. For each country, constrained optimisation reallocated 13 food-group supplies under isoenergetic, protein-preserving and food-group-bound constraints, minimising sDII and GHG jointly (Pareto frontier). Health burden was estimated via pooled relative-risk meta-analysis and 2023 World Bank population data. Results: sDII was only weakly associated with GHG (Spearman rho=0.14), land (rho=0.13) and freshwater (rho=0.28) in 2023. The balanced-Pareto reallocation lowered both sDII and GHG in 182/182 series (100% synergy): population-weighted delta sDII=-0.235, GHG -37.5%, land -49.3%, water -17.2%, i.e. 4.28 Gt CO2e/yr avoided. The associated reduction in metabolic-syndrome burden was directionally consistent but modest (~1.1% of the prevalent pool, ~2.84 million cases). Results were robust to three life-cycle inventories and three feasibility-bound regimes. Conclusions: Anti-inflammatory and low-carbon goals are decoupled rather than conflicting, and an isoenergetic, protein-preserving reallocation reconciles them in every country. Environmental gains are large and robust; health gains are directionally consistent but modest--triangulation, not a causal claim.

Objective: Whether a scheduled antiseizure medication (ASM) continues on schedule across the ICU-to-floor transfer has not been characterized. We quantified ASM administration-gap frequency across this transfer and compared it with gap frequency during matched non-transfer intervals in the same patient and drug. Methods: In this retrospective MIMIC-IV (version 3.1) cohort study, we identified epilepsy and status-epilepticus admissions with an ICU stay followed by floor transfer and a scheduled ASM order active at ICU departure. A gap was defined as an interval exceeding 1.5 times the expected dosing interval between the last ICU dose and first floor dose, or no further dose before discharge, and compared with a matched non-transfer control interval in the same patient and drug (paired McNemar test). A multivariable model evaluated six prespecified clinical predictors; sociodemographic variables were summarized descriptively. Results: Among 2,469 ASM transition-by-drug observations (1,583 admissions, 1,335 patients), an administration gap occurred in 251 (10.2%; 95% CI, 8.7%-11.7%). Gap frequency across the transfer exceeded frequency during matched non-transfer control intervals in the same patient and drug: a paired rate difference of 5.8 percentage points (95% CI, 4.4-7.1; 7.5% vs 1.7%; P = 7.3 x 10^-22) before the transfer and 6.4 percentage points (95% CI, 4.9-7.9; 8.9% vs 2.5%; P = 1.9 x 10^-23) after. Gap rates were similar for intravenous-available (9.9%) and oral-only (11.4%) drugs (rate difference, 1.5 percentage points; 95% CI, -1.6 to 4.5; P = .34). None of six prespecified predictors reached significance after correction. Significance: An antiseizure medication administration gap occurred in approximately 1 of every 10 drug-transition observations at the ICU-to-floor transfer, exceeding matched non-transfer gap rates by 5.8 to 6.4 percentage points. This transfer-associated excess, rather than any single medication or patient characteristic, supports a structured medication-continuity check.

Stroke remains a leading cause of mortality and long-term disability worldwide, yet rapid diagnosis is often limited by the shortage of trained radiologists, particularly in resource-constrained settings. Automated analysis of CT imaging offers a potential solution, but existing methods often struggle to achieve clinically generalisable performance while jointly addressing multiple diagnostic tasks. Here we present the Intelligent Integrated Stroke Diagnosis System IISDS, an end-to-end deep learning framework built upon StrokeGNN, a graph-based architecture that integrates 3D contextual feature extraction with U-Net-based 2D lesion segmentation to enable comprehensive stroke analysis from non-contrast CT scans. IISDS performs stroke subtype classification, lesion segmentation and lesion volume estimation within a unified pipeline. To develop and validate the system, we collected and curated BGD-ISD through a collaboration between AI researchers, neurologists, radiologists and clinicians, resulting in a large multi-centre dataset comprising 1,507 CT scans from 597 stroke cases acquired across six hospitals and medical centres in Bangladesh. Across BGD-ISD and multiple publicly available datasets, IISDS achieves state-of-the-art performance on all tasks, improving segmentation accuracy by [&ge;]0.011 Dice score, reducing lesion volume estimation error by [&ge;]0.3 average symmetric surface distance (ASSD), and increasing classification performance by [&ge;]0.018 area under the receiver operating characteristic curve (AUC) compared with existing approaches. These results demonstrate the potential of graph-based deep learning to enable clinically generalisable, automated and scalable stroke diagnosis from CT imaging, supporting rapid clinical decision-making, particularly in healthcare environments with limited access to expert radiological interpretation.

Background: Aerobic exercise is recommended during stroke rehabilitation to improve cardiorespiratory fitness and support recovery; however, participation rates remain low. While institutional and system-level barriers have been widely examined, less is known about how individual patient factors influence engagement in aerobic exercise during rehabilitation. Objectives: We aimed to determine whether depressive symptoms, apathy, self-efficacy and outcome expectations for exercise, perceived barriers, or past exercise history were associated with aerobic exercise participation in stroke rehabilitation. Methods: In this prospective cohort sub-study, adults admitted to in- or out-patient stroke rehabilitation at three urban hospitals completed validated questionnaires assessing depressive symptoms, apathy, exercise self-efficacy, outcome expectations for exercise, perceived barriers to being active, and premorbid exercise history. Participants were separated into two groups for analysis: those who completed aerobic exercise during rehabilitation and those who did not. Equivalence testing and between-group comparisons were performed. Results: Sixty-two participants were enrolled; 16 participated in aerobic exercise and 46 did not. Groups were not equivalent on any individual-level factors. Compared to non-participants, those who performed aerobic exercise had significantly higher depressive symptom scores (p=0.0025) and lower self-efficacy for exercise (p=0.0087). Non-participants demonstrated significantly higher apathy (p=0.0007). No significant differences were found for outcome expectations, perceived barriers, or exercise history. Conclusion: Depressive symptoms and lower self-efficacy did not impede aerobic exercise participation during rehabilitation. Increased apathy, however, was associated with non-participation. Findings highlight the need for individually tailored aerobic exercise prescriptions that consider motivational and affective factors to optimize engagement during stroke rehabilitation.

Background and Objectives: Recurrence following burr-hole drainage of chronic subdural haematoma (cSDH) occurs in 10-25% of cases, sustained by neovascularisation of the subdural neomembrane supplied by the middle meningeal artery (MMA). MMA embolisation reduces recurrence; whether incidental burr-hole intersection of MMA branches during drainage confers similar benefit is unknown. Methods: We performed a multicentre retrospective cohort study of consecutive adults undergoing burr-hole drainage for cSDH at two UK tertiary neurosurgical centres. Postoperative thin-slice CT was used to classify burr-hole intersection of the underlying MMA groove (no hit, distal-branch hit or main-branch hit) and measure perpendicular burr-hole-to-MMA-groove distance. Co-primary outcomes were radiological recurrence and recurrence requiring intervention. Patient-clustered multivariable logistic regression adjusted for prespecified clinical covariates and treating site. Results: 227 patients (284 operated hemispheres) were included. Radiological recurrence decreased from 34.4% with no branch hit to 22.9% with main-branch intersection, with the gradient confined predominantly to unilateral cSDH. Main-branch intersection was associated with lower adjusted odds of radiological recurrence in unilateral cSDH (adjusted OR 0.30, 95% CI 0.11- 0.81; P = .018), with a similar but non-significant association in the overall cohort (adjusted OR 0.53, 95% CI 0.26-1.07; P = .075). Burr-hole-to-MMA-groove distance demonstrated a more consistent association: in the overall cohort, each 5-mm increase independently increased the odds of radiological recurrence (adjusted OR 1.38, 95% CI 1.04-1.82; P = .025). In unilateral cSDH, each 5-mm increase was independently associated with both radiological recurrence (adjusted OR 1.45, 95% CI 1.03-2.04; P = .034) and recurrence requiring intervention (adjusted OR 1.52, 95% CI 1.05-2.20; P = .027). Conclusion: Main-branch intersection of the middle meningeal artery during routine burr-hole surgery is associated with lower recurrence of unilateral cSDH, while the accompanying burr-hole-to-MMA-groove distance gradient provides biologically plausible support for a dose-response relationship. Together, these findings provide mechanistic rationale for prospective evaluation of intentional neuronavigation-guided MMA targeting (BURR-MMA; NCT07549893).

Disseminating study results to participants is an ethical responsibility for researchers but remains uncommon in low- and middle-income countries, and participants preferences for receiving study results are poorly understood. This study examined study result dissemination preferences among pregnant women in a phase III malaria prevention trial in Papua New Guinea (PNG). Participants completed an interviewer-administered questionnaire (survey) assessing their interest in and motivation for receiving trial results and preferences for dissemination methods and content. Associations between participants characteristics and dissemination preferences were explored using multivariable logistic regression analysis. Of 1172 trial participants, 96.0% (1125/1172) completed the survey, and of these 99.6% (1121/1125) wanted to learn about the trial results. The main motivation factors driving participants interest were an acknowledgment of their contribution to research (51.7%; n=579) and a better understanding of the study (45.0%; n=505). Most participants (78.9%; n=884) wanted to learn about the trial findings through written summary and a group meeting with other participants at the nearest clinic (31.1%, n=349). Multivariable regression analysis indicated that participants from rural/peri-urban clinics were more likely to choose non-electronic media dissemination approaches such as a group meeting as compared to urban-dwelling participants. Frequently selected items (>50% of participants) for content included information regarding good results of the study, purpose of the study, medical treatment advances, results specific to me, and how study was conducted. There was heterogenicity in the preference for dissemination content: compared to urban clinics rural clinics are less likely to want to learn about how and why study was conducted and medical and scientific advances. Overall, the majority wanted to learn about trial results, highlighting the importance of integrating dissemination into research activities in PNG. Variation in preferences for mode and content of dissemination between study clinics suggests that dissemination activities could be tailored to local context and preferences.

Background: Fibrosis, characterised by excessive accumulation of collagen type I (COL1), is a common feature of chronic diseases, including liver diseases (LDs), chronic kidney disease (CKD) and heart failure (HF). COL1 degradation products can be detected in urine by proteomics/ peptidomics analyses and may serve as non-invasive biomarkers of fibrosis. We aimed to identify a common molecular signature of fibrosis across these diseases that may ultimately guide interventions to slow disease progression and prevent organ damage. Methods: Using capillary electrophoresis coupled to mass spectrometry (CE-MS), naturally occurring COL1 degradation products (peptides) in the urine of patients with fibrotic disease, LDs (n=127), CKD (n=263) or HF (n=187), were investigated and compared with the same number of matched controls. Disease-associated COL1 peptides were identified separately for each condition, and peptides showing consistent associations across the three diseases were selected to define a common fibrosis signature. A support vector machine model based on the selected peptides was developed and validated in independent cohorts of patients with LDs (n=110), CKD (n=93), HF (n=32) and controls (n=643). Results: We identified a common fibrotic signature consisting of 50 COL1 degradation products, mainly downregulated in fibrosis. A model based on these peptides achieved a strong performance, with an area under the receiver operating characteristic curve (AUC) of 0.935 (95% confidence interval (CI) 0.917-0.953, p<0.0001) in an external validation cohort comprising pooled disease groups (LDs, CKD, and HF) and controls. Performance was maintained in LDs, CKD and HF, with AUCs of 0.917 (95% CI 0.890-0.944, p<0.0001), 0.951 (95% CI 0.931-0.971, p<0.0001) and 0.950 (95% CI 0.903-0.997, p<0.0001), respectively. The model scores were significantly associated with fibrosis stage in LDs (p=0.0097) and with interstitial fibrosis and tubular atrophy in CKD (p=0.045). Conclusion: A model of urinary COL1 peptides captures a shared collagen degradation signature across organs and diseases, enabling the non-invasive assessment of fibrosis irrespective of its origin. As these peptides exclusively reflect collagen degradation, the findings suggest impaired collagen degradation as a driver in fibrosis. Future clinical studies are warranted to evaluate the utility of this model for early fibrosis detection and earlier implementation of anti-fibrotic interventions.