Nature Medicine

Background National childhood respiratory pathogen spectra are diversifying nearly everywhere - within-country diversity rose in 203 of 204 countries between 1990 and 2023 - yet whether countries are diversifying toward a common spectrum or along divergent paths is unknown. We quantified between-country compositional distance of national pathogen spectra over the same period. Methods We built national pathogen share vectors from Global Burden of Disease Study 2023 lower respiratory infection etiologic attributions (26 pathogens, 204 countries, ages 0-19 years) at five timepoints spanning 1990-2023. Between-country distance was measured as all pairwise Jensen-Shannon divergences (JSD; primary) and Bray-Curtis dissimilarities, with Baselga and Jaccard decompositions; robustness was assessed across metrics, pathogen panels, low-count thresholds and a balanced panel of 107 countries. Results Mean pairwise JSD rose from 0.0084 in 1990 to 0.0283 in 2023 (+238%; trend p = 0.030), peaking in 2021 (+283%) with a partial 2023 pullback. Bray-Curtis dissimilarity rose +120% and the balanced panel +423%. Divergence was entirely balanced variation (share reallocation), with spectrum richness rising from 18.5 to 21.1 of 26 pathogens. Dispersion rose fastest for influenza (coefficient of variation 0.03 to 0.55) and respiratory syncytial virus (0.08 to 0.48). Within-region distance rose in every computable GBD super-region (five of seven): divergence occurs within regions, not between blocs. Conclusions National spectra are re-sorting along country-specific axes as vaccine-preventable dominance recedes at different speeds. Diversification is universal, but convergence is absent: the transition at the etiologic-spectrum level is asynchronous and path-dependent, with implications for empirical treatment policy and pathogen surveillance.

Importance: Immune checkpoint inhibitors (ICIs) produce diverse immune toxicities, but whether checkpoint blockade also modifies associations between other drugs and adverse events is poorly understood. Objective: To define ICI-associated toxicity organization and determine whether drug-associated adverse events and onset vary with ICI exposure and checkpoint pathway. Design and Setting: Cross-sectional analysis of deduplicated FAERS reports from 2016 through 2025; analyses performed in 2026. Participants: Among 13,701,106 deduplicated reports, 2,365,269 were cancer associated and 256,940 contained an ICI. Median age among cancer reports with observed age was 66 years (IQR, 56-75 years); 1,031,999 (43.6%) were female and 1,003,154 (42.4%) were male. Exposures: ICI exposure in any reported drug role, individual primary-suspect drugs, and checkpoint-pathway exposure. Main Outcomes and Measures: Reporting odds ratios (ORs), cross-organ adverse-event communities, adjusted primary-suspect drug x ICI interaction ORs for Stevens-Johnson syndrome/toxic epidermal necrolysis (SJS/TEN), drug reaction with eosinophilia and systemic symptoms (DRESS), acute generalized exanthematous pustulosis (AGEP), interstitial nephritis, drug-induced liver injury (DILI), and vomiting (VOM), and accelerated failure-time model time ratios for documented onset. Results: Of 3001 eligible Preferred Terms in cancer-associated reports, 2091 differed at a false discovery rate (FDR) less than .05. Four cross-organ toxicity communities were identified. Of 138 eligible drug-phenotype pairs, 65 had FDR-significant interactions, including moxifloxacin-SJS/TEN amplification (interaction OR, 101.72; 95% CI, 39.11-264.55), enfortumab vedotin-SJS/TEN attenuation (interaction OR, 0.17; 95% CI, 0.13-0.23), and omeprazole-interstitial nephritis amplification (interaction OR, 10.35; 95% CI, 7.62-14.05). Among 60,324 reports contributing to temporal analyses, ICI exposure was associated with longer adjusted documented time to onset for 5 of 6 phenotypes (time ratios, 1.37-1.59) but not AGEP (time ratio, 0.99; 95% CI, 0.67-1.46). Temporal associations also differed across checkpoint pathways. Conclusions and Relevance: ICIs were associated with a structured cross-organ toxicity landscape, phenotype-specific modification of drug-associated adverse events, and distinct temporal patterns across checkpoint pathways. These findings support checkpoint blockade as a modifier of drug-associated toxicity and motivate longitudinal and mechanistic validation.

Post-COVID-19 syndrome (PCS) is characterized by fatigue, neurological impairment and systemic symptoms. This heterogeneity of symptoms hinders biomarker development. Here, we profiled extracellular-vesicle (EV) surface markers in plasma and CSF from 61 participants with PCS (COVIDpost), 80 recovered controls (COVIDreco), and 10 participants with non-SARS-CoV-2 post-viral syndromes. EVs were analysed by bead-based multiplex flow cytometry using tetraspanin-directed (TSPN) and phosphatidylserine-directed lactadherin (PS) detection. Amongst 37 targets covering tetraspanins and vasculature-, immunity- and stemness-associated markers, none met a 1% false-discovery-rate threshold. However, L1-regularized logistic regression under fully nested 5x5 cross-validation identified a distributed plasma EV profile, with mean out-of-fold areas under the receiver operating characteristic curve (AUCs) of 0.788 (95% CI 0.715 - 0.852) for TSPN and 0.716 (95% CI 0.636 - 0.792) for PS detection. Across the pooled COVIDpost and COVIDreco population, EV classification scores covaried with clinical group differences, but did not track clinical severity within either cohort. These PCS-EV classification scores decreased at one-year follow-up in COVIDpost participants. Our findings identify an internally cross-validated multivariable EV surface profile associated with COVIDpost versus COVIDreco status and support independent validation and exploration of EV-based biomarkers in post-viral fatigue syndromes.

Background The historic decline in childhood pneumonia mortality was driven substantially by single-pathogen vaccines against Haemophilus influenzae type b (Hib) and Streptococcus pneumoniae. Yet the pathogen spectrum underlying child pneumonia deaths is diversifying: the effective number of pathogens rose from 5.57 in 1990 to 9.94 in 2023, and the residual burden is shifting toward opportunistic and hospital-associated pathogens for which no licensed childhood vaccines exist. This paper asks how resources should be sequenced between single-pathogen interventions and platform investments as this transition proceeds. Methods We analyzed Global Burden of Disease Study 2023 deaths from 29 pathogens in ages 0-19 years by super-region, combined with WHO/UNICEF Estimates of National Immunization Coverage (WUENIC) for PCV3 and Hib3. We quantified the spectrum transition under two denominators (26- and 29-pathogen calibers), constructed a share-by-intervenability matrix assigning each pathogen to a dominant intervention channel (vaccine-reachable, mixed, platform-sensitive) under explicit classification rules, compared platform-sensitive deaths with a transparently computed scenario of residual vaccine-preventable deaths, and cross-classified pathogens by age tropism and poverty lock. We anchored platform interventions to verified published evidence. Results The vaccine-preventable group share fell from 54.0% to 40.2% while the opportunistic/hospital group rose from 18.1% to 23.1% (29-pathogen caliber, 1990-2023). Super-region vaccine coverage showed no significant association with pathogen-share change (PCV3 Spearman rho = 0.108, p = 0.818; Hib3 rho = -0.036, p = 0.939), a null result we report as evidence that simple coverage-burden correlations do not hold at the regional level, not as evidence against vaccine value. In 2023, vaccine-reachable pathogens accounted for 441,410 deaths (45.7%, channel including COVID-19), mixed for 126,926 (13.1%), and platform-sensitive pathogens for 396,995 (41.1%). Platform-sensitive deaths were 2.9-5.1 times the scenario estimate of residual vaccine-preventable deaths (52,435-77,512). Nine of 14 classifiable pathogens fell into the poverty-locked, infant-tropic cell (480,922 deaths; Fisher OR = 9.0, p = 0.1758). Conclusions The marginal value of single-pathogen strategies declines as the spectrum diversifies and residual deaths concentrate in platform-sensitive, poverty-locked, infant-tropic pathogens. Vaccine scale-up remains a certain and sizeable opportunity; the next increment of marginal resources should increasingly fund platform capabilities (oxygen systems, antimicrobial access and stewardship, infection prevention and control, referral, and nutrition) delivered as a package to the populations where the residual burden is locked.

Autism Spectrum Disorder (ASD) is a heterogeneous neurodevelopmental condition defined by differences in social communication and restricted, repetitive behaviours. As diagnostic criteria have broadened, ASD is now recognised across a wider range of individuals, raising key questions about its structure: does ASD have discrete sub-types, or is it better conceptualised as a continuous, possibly multidimensional, condition? We aim to explore whether a multidimensional continuum model more accurately captures the variability within ASD. We analysed a large SPARK phenotypic dataset of medical history and diagnostic surveys (background history, SCQ, RBS-R; n=36,710 individuals). We apply and compare two traditional statistical approaches, Factor Analysis and Gaussian Mixture Models, with a modern machine learning technique, the Variational Autoencoder (VAE). VAEs reconstructed unseen test data with ~4-fold better accuracy than Factor Analysis, and ~8-fold better accuracy than Gaussian Mixture Models. We identified four stable latent factors across 100 independently trained VAEs. These four dimensions provide an individual behavioural profile that can be visualized using radar-plots, offering a compact way to compare profiles at the person level. Through further analysis, we found evidence for 3 overlapping clusters or subtypes of ASD identified within the 4D latent space. This work aims to inform new ways of modelling ASD using a VAE that will be able to discern between a continuum or a clustered output and that go beyond binary diagnosis, instead reflecting the complex range of trait profiles, with implications for personalised diagnosis and intervention.

Active Feature Acquisition (AFA) adaptively selects which diagnostic test to order next and offers a route to reduce unnecessary laboratory testing in acute care. Existing clinical AFA evaluations, however, assume every feature can be retrieved on demand and split data at the visit level, both of which inflate apparent performance. We re-evaluate cost-aware AFA under constraints designed to reflect deployment. From MIMIC-IV we constructed a cohort of 64,766 acute admissions (39,884 patients; 21 conditions; 55 features in 30 test panels) with a patient-level split, a 12-hour decision cutoff, and a per-patient availability mask from what was actually measured, and priced panels using the 2026 Medicare fee schedule under panel-level billing. We evaluated EIG-Cost, which scores each panel by Monte-Carlo Expected Information Gain penalised by its dollar cost, against eight published methods across budgets \30--$60 over five patient-level resamples. At a $30 budget, EIG-Cost achieved the highest macro-F1 (0.188, 95% CI [0.185, 0.191]) at the lowest cost ($17.28), exceeding the strongest baseline in all five resamples (p<0.001; Cohen's d=4.0), and led at every budget. Three of the eight methods collapsed to a vitals-only baseline (macro-F1 approx 0.040), acquiring nothing even at higher budgets, a genuine failure to adapt to availability rather than a budget limitation. Despite modest absolute accuracy, EIG-Cost's probabilities were well-calibrated (expected calibration error $0.048$). Under realistic availability constraints, clinical AFA is substantially harder than full-availability benchmarks imply, several published methods fail outright, and cost-aware information-gain scoring is a robust choice in this harder setting.

Machine-learning models of the human microbiome are trained mostly on stool samples from single cohorts, limiting cross-body-site representation and cross-study generalization. Progress is constrained less by algorithms than by the absence of a harmonized multi-body-site corpus carrying the technical metadata needed to model, rather than ignore, batch structure. Here we release Corpusome, a harmonized two-tier cross-body-site human microbiome corpus for representation learning: a harmonized corpus of 187,546 human microbiome samples integrating standardized profiles from curatedMetagenomicData, the American Gut Project, and the EBI MGnify platform. Corpusome follows a two-tier design preserving both functional depth and cross-body-site breadth: a shotgun tier (22,588 samples, 93 studies) with species- and pathway-level profiles, and a 16S tier (164,958 samples, from a full pull of 708 MGnify studies) with genus-level profiles extending coverage to oral, skin, respiratory, and urogenital sites. It spans six body sites and two modalities, with harmonized metadata for batch-aware modelling. Body-site signal exceeds technical/source variance in the 16S tier by approximately 2.4-fold.

Influenza vaccine effectiveness (VE) is estimated from a limited number of clinics using a test-negative design. These standard estimates face geographic, temporal, and operational constraints. Using Twitter/X data, we applied few-shot chain-of-thought prompting to identify self-reported vaccination status and influenza test results, then implemented a test-negative-like design to estimate VE. Our estimates fell within the range of interim reports and could complement current systems, improving feasibility, timeliness, and scalability.

Dissecting the complexity of antibody responses in orthopoxvirus (OPXV) infected individuals is essential for elucidating protective mechanisms and identifying candidate protective immunogens. Here, we profiled the acute humoral response in 51 mpox cases, showing distinct IgG trajectories among multiple antigens alongside the rise of plasma neutralizing activities to plateau within 6 weeks after symptom onset. Utilizing a single-cell transcriptomic and BCR sequencing based antigen-agnostic mAb isolation workflow, we further generated monoclonal antibodies (mAbs) from 254 expanded peripheral B cell clones of 3 patients. We discerned 97 specific mAbs recognizing at least 12 different OPXV proteins via integrated screening approaches, which comprised neutralizing antibodies binding unconventional viral targets and antibodies exhibiting extraordinary in vitro and in vivo anti-OPXV effects. The number of OPXV-specific mAbs recovered per donor reflected the percentage of expanded clones among circulating B cells. More interestingly, we demonstrated that the inferred unmutated common ancestors (UCAs) of neutralizing antibody clones did not necessarily react with OPXV, implying that OPXV neutralizing antibodies might frequently originate from B cells previously activated by unknown antigens. Our work establishes an efficient workflow for antigen-agnostic isolation of pathogen specific mAbs and reveals previously unclarified features of antibody responses induced by acute MPXV infection.

Predicting disease risk from prior diagnoses is fundamental to clinical decision-making, particularly during health emergencies such as the COVID-19 pandemic, when individuals with long-term conditions may be disproportionately vulnerable to adverse outcomes. Despite intense interest in developing models to predict disease risk from prior diagnoses (1-3), most prediction models do not estimate effects of each prior diagnosis on disease risk conditional on other diagnoses, limiting interpretability and clinical utility. We developed the Comorbidity Risk Score (CRS), trained on 13 million individuals (age 40-69) from linked electronic health record (EHR) datasets of the entire population of England, to predict COVID-19 hospitalisation and 87 other disease outcomes. CRS was trained at close to saturated sample size and precisely estimated the effects of 212 prior diagnoses on the 88 disease outcomes, conditional on all other prior diagnoses. Correlations of CRS effect sizes across outcomes (e.g. 0.76 for myocardial infarction vs. hyperlipidaemia) matched the corresponding genetic correlations (e.g. 0.79 for myocardial infarction vs. hyperlipidaemia), confirming that comorbidity architectures capture disease aetiology. On average, CRS identified 5% of the population with 3.4-fold higher disease risk, including myocardial infarction (4.4-fold), lung cancer (6.5-fold), and COVID-19 hospitalisation (6.3-fold). Using prior diagnoses alone, CRS outperformed state-of-the-art clinical COVID-19 models (4). Furthermore, CRS (N=13 million) substantially outperformed state-of-the-art AI (1) (N=0.5 million) and linear (3) (N=0.5 million) models in predicting disease risk, suggesting that training sample size outweighs model complexity. CRS attained near-perfect transferability across self-reported ethnicities (e.g., Black vs. White: AUROC ratio = 97.3%). Finally, CRS distinguished independently predictive comorbidities from indirect associations, e.g., lipid metabolism disorder was a strong predictor of myocardial infarction risk but not ischaemic stroke, after conditioning on other prior diagnoses. In conclusion, CRS provides a comprehensive resource for understanding the impact of comorbidities on COVID-19 and other future diseases, revealing disease aetiology while enabling powerful prediction of disease risk.

Biological aging occurs heterogeneously across individuals and organs. However, current measures of biological age incompletely capture organ-specific differences in health and disease risk. Because chest CT visualizes multiple thoracic organs, it offers an opportunity to quantify structural aging across organ systems. Here, we developed MOSAIC-Age, a framework characterizing eight organ-specific aging clocks on chest CT. The clocks were developed and validated using 9,971 CT scans from CT-RATE and MIDRC, and subsequently locked and applied to two independent prospective cohorts with 35,293 participants from the National Lung Screening Trial and Genetic Epidemiology of COPD study. CT-derived biological age gaps (BAGs) were examined in relation to lifestyle and socioeconomic factors, prevalent comorbidities, incident chronic diseases, and all-cause and cause-specific mortality. Higher BAGs, indicating organs that appeared older on CT than expected for their chronological age, were broadly associated with adverse health characteristics, chronic disease burden, and increased mortality risk. Multiple disease outcomes were associated with aging across several organs, whereas in multivariable analyses including all eight organ-specific BAGs, the remaining associations were more organ specific. A greater number of markedly older-appearing organs and a faster pace of aging were each associated with higher mortality. Together, these findings demonstrate that routine chest CT captures both shared and organ-specific patterns of biological aging and establish CT-derived organ aging as a quantitative imaging biomarker for assessing multi-organ health and long-term disease risk.

Routine childhood growth surveillance is a cornerstone of pediatric care. Growth pattern abnormalities are often early manifestations of chronic disease. Yet subtle abnormalities are frequently underrecognized, leading to diagnostic delays and avoidable morbidity. We introduce SPROUT (System for Pediatric Recognition Of Undiagnosed Trajectories), a generalized, multi-agent large language model (LLM) reasoning system designed to identify a broad spectrum of pediatric growth-related conditions from longitudinal electronic health records (EHRs) earlier than standard clinical practice. Using a large pediatric primary care EHR dataset, we developed and validated SPROUT as a two-stage system. First, a highly specific LLM screener flags concerning longitudinal growth patterns. Second, an Orchestrator module coordinates a multidisciplinary panel of LLM agents to generate a ranked differential diagnosis. To correct systemic reasoning errors, a Trainer module injects meta-knowledge into the panel via a dedicated "Learner" agent. Diagnostic capability was evaluated using a walk-forward, visit-by-visit simulation leading up to the diagnosis date. The SPROUT screener model achieved 98% (83/85) specificity and 28% (9/32) sensitivity on a gold-standard dataset of pediatric primary care patients when evaluated one year before the index date, and 100% specificity and 47% sensitivity when evaluated using longitudinal data up to the day of diagnosis. When applied to 300 control patients (i.e., healthy or undiagnosed), the screener flagged 15. Subsequent expert panel review confirmed high suspicion for undiagnosed pathology in 33% (5/15) of these cases. In chronological walk-forward validation on disease cases, the diagnostic engine identified conditions well before standard-of-care documentation. One year prior to clinical diagnosis, the system achieved sensitivities of 81% for type 1 diabetes mellitus, 56% for pituitary disorders, and 44% for celiac disease. The SPROUT multi-agent system demonstrates the ability to detect a significant portion of latent growth-related pediatric conditions months to years before current clinical standards while minimizing false positives. These results support its potential as a decision support tool for reducing diagnostic delays in pediatric care.

Improvements in data availability, sharing, and integration, together with the development of explainable artificial intelligence (XAI) techniques, are advancing precision medicine for pediatric cancer by facilitating diagnosis, biomarker discovery, and drug development. Data sharing commons and initiatives like the Childhood Cancer Data Initiative (CCDI) provide access to pediatric-specific genomic and clinical data cohorts and improve data availability for pediatric cancer research. Based on CCDI, a scalable AI platform, Graph Artificial Intelligence for Pediatric Oncology (GAIPO), integrates various data modalities from bulk and single-cell omics data to clinical information. Such multi-modal data facilitates the training and development of advanced XAI models for pediatric cancers. We then developed an end-to-end multi-modality framework, PCGS, for pediatric cancer by incorporating omics-specific representation learning via GNN models with cross-attention fusion and multi-objective learning for downstream tasks such as classification, clustering, and survival analysis. This framework outperforms previous supervised multi-omics integration baseline approaches based on glioma and Wilms tumor cohorts and enables GNN model explainability via Shapley value-based feature attribution approaches to explain the contributions of gene-level features across various biomedical tasks, including classification and survival. Given specific background samples (e.g., age groups, sex, grades) as baselines, this explainable GNN model estimates and ranks the importance scores for input features from each omics modality. It identifies background-specific key features for biomarker discovery, risk group identification, and survival analysis in glioma and Wilms tumor, with potential applicability to other pediatric cancers.

Migraine is a leading cause of disability, yet preventive treatment remains largely empirical despite the availability of several mechanistically distinct therapies. Genetic data can clarify mechanisms and therapeutic hypotheses when association signals are integrated with molecular and clinical data. We meta-analyzed migraine GWAS data from 12 European ancestry cohorts (206,893 cases and 2,093,175 controls) and four African ancestry cohorts (22,115 cases and 178,626 controls). We identified 311 lead variants in European-ancestry analyses and 316 lead variants in trans-ancestry analysis. Fine-mapping and transcriptome-wide analyses prioritized variants and genes implicated in sensory neuronal signaling, vascular tone, and immune regulation, with convergent evidence at several established loci including TRPM8 and PHACTR1. Drug-repurposing analyses identified therapeutic targets and compounds, including established migraine treatments and candidates requiring experimental validation. Genetic correlations, Mendelian randomization, and a phenome-wide scan linked migraine liability to psychiatric, pain, and gastrointestinal phenotypes. Together, these findings expand the known genetic architecture of migraine across ancestries and provide a genetics-led map connecting association signals with biological pathways, multimorbidity and candidate therapeutic mechanisms, providing a foundation for future functional and translational studies.

Vaccine responses vary across individuals due to differences in ageing and health status. Using transcriptomic profiling, we analyzed early gene expression profiles after influenza (QIV) followed by pneumococcal (PCV13) vaccination in 148 participants spanning young, middle-aged, and older adults. The two vaccines induced distinct immune signatures: QIV elicited innate and interferon immune activation, while PCV13 triggered inflammation-based responses. Older adults showed weaker but similar transcriptomic profiles compared to young adults. Among older adults, frailty, in addition to age, was strongly associated with reduced innate responses. In addition, we identified associations between early-stage transcriptomic profiles and later-stage antibody responses for QIV; however, no such associations were observed for PCV13. Importantly, observed group differences arose not from altered immune modules but from differences in the magnitude of gene expression, paving the way for immune-boosting interventions to enhance early gene expression in at-risk populations.

Obesity is a significant public health concern. Early-onset obesity in the context of rare disease can reflect genetically-mediated pathology or elevated susceptibility through indirect mechanisms. Mapping the diverse characteristics and needs of young people with obesity in the rare disease population is a first step toward mechanistic and translational research. We carried out a retrospective comparative analysis of demographic, genotypic, phenotypic and health service utilisation data for young people with obesity (cases: n=500) and without obesity (controls: n=11,444) from the UK 100,000 Genomes Project rare disease cohort. Cases and controls were recruited prior to genomic diagnosis, across clinical disorder categories. We observed significant association between socioeconomic deprivation and obesity risk. Young people with obesity had significantly higher utilisations of acute care and mental health services, indicating an overall higher health burden. A curated panel of 519 candidate obesity-associated genes demonstrated aggregate association with obesity, although no single gene reached significance. Phenotypic comparison between cases and controls highlighted increased multi-organ and neurological system involvement, highlighting the overlap between neurodevelopmental and obesity risks. Within the case group, we conducted cluster analysis to identify early-onset obesity groups with different phenotypic profiles, potentially arising from different causal pathways - this identified six obesity subgroups of interest, with differing involvement of neurodevelopmental and other systems. Our study confirms that obesity co-occurs with a wide range of factors within the rare disease population, and is associated with significant physical and mental health needs, requiring holistic lifelong care.

Public genomic repositories can support antimicrobial resistance (AMR) surveillance, but unequal sampling can bias interpretation. We characterised AMR determinants, multicountry genomic cluster overlap and surveillance gaps across Africa using an NCBI Pathogen Detection snapshot retrieved on 24 August 2026 for 55 African Union member states. Records were validated and deduplicated by BioSample, and complete AMRFinderPlus calls were summarised across five United Nations M49 subregions and eight overlapping regional economic communities (RECs). Country-pair cluster overlap was assessed using the Jaccard index, while project-based and composition-standardised sensitivity analyses evaluated repository bias. The dataset contained 86,829 unique BioSamples from 51 states; South Africa, Malawi and Kenya contributed 55.8%. Complete extended-spectrum {beta}-lactamase calls were detected in 21,513 isolates and carbapenemase calls in 4,642. blaCTX-M-15 dominated the ESBL profile, while NDM and OXA types predominated. Seventy clusters contained carbapenemase-positive isolates from at least two countries. A shared REC covered all participating countries in 38 clusters, while 32 crossed REC boundaries. Normalised country-pair overlap was low, with a maximum Jaccard index of 9.5%. Project balancing reduced the Northern African carbapenemase estimate from 32.3% to 17.9% and the Eastern African ESBL estimate from 36.9% to 12.5%. Public repositories identify determinants and clusters for investigation but do not estimate prevalence or transmission. AMR surveillance should combine national confirmation, regional institution-led investigation where countries share an REC, and continent-wide coordination through Africa CDC for cross-REC signals, supported by representative One Health sampling, standardised metadata and sustained African sequencing capacity.

Background Childhood respiratory infectious deaths are partitioned across four Global Burden of Disease cause modules-26 etiological attributions within lower respiratory infections, tuberculosis, COVID-19, and whooping cough-never jointly reported. Whether the structure of this combined mortality spectrum has changed over time, and with what implications for intervention design, has not been quantified. We assembled and analyzed the integrated spectrum for children and adolescents aged 0-19 years, 1990-2023. Methods We integrated Global Burden of Disease Study 2023 (release v8352) estimates into a 29-node spectrum-26 lower respiratory infection etiologies plus tuberculosis, COVID-19, and pertussis-globally and across seven super-regions, with uncertainty propagated by summing bounds. We computed Shannon diversity, Herfindahl concentration, and effective cause counts; phenotyped pandemic-window collapse and rebound per cause; linked pathogen shares to WHO/UNICEF vaccine coverage; and mapped geographic concentration in sub-Saharan Africa and South Asia. Reporting follows GATHER. Results In 2023 the 29 causes jointly accounted for 965,330 deaths (95% uncertainty interval [UI] 680,096-1,342,437). Shannon diversity rose from 2.336 to 2.711 (+16.1%) between 1990 and 2023; the effective number of causes nearly doubled (5.57 to 9.94), inversely coupled to total deaths (Spearman rho = -0.997). Whooping cough ranked second (112,954 deaths; 95% UI 64,576-185,708; 11.7%) and showed the spectrum's only rebound above 100% (-57.4% collapse, +111.0% rebound). Tuberculosis ranked third (87,764; 57,779-124,912; 9.1%) with the highest concentration in sub-Saharan Africa and South Asia (87.1%). COVID-19 entered at rank five (52,899; 47,275-59,183; 5.5%). Nineteen of 29 causes exceeded the poverty-lock threshold (>80.59% of deaths in sub-Saharan Africa plus South Asia). Conclusions Childhood respiratory infectious mortality has become more diverse and more concentrated in poverty as it has declined. Single-pathogen interventions now address a shrinking share; the spectrum's structure argues for platform interventions-oxygen, antimicrobial access, referral-tailored jointly by age and geography, implying that pathogen-specific strategies alone cannot finish the remaining mortality agenda.

Clonal hematopoiesis (CH) increases with age, but whether different somatic clones represent an ageing phenotype or exert distinct systemic effects is unclear. In 450,587 UK Biobank participants, including 46,324 with plasma proteomics, we compared clonal hematopoiesis of indeterminate potential (CHIP) and mosaic loss of chromosome Y (mLOY) or X (mLOX) across biological ageing, incident disease, and circulating proteins. Despite shared age dependence, these alterations showed distinct disease spectra: non-DNMT3A CHIP was associated with broad multisystem disease burden, mLOY with a more focused respiratory, musculoskeletal and cardiovascular profile, whereas mLOX lacked broad age-related disease associations. Clone burden mapped to distinct proteomic programs: mLOY to neutrophil degranulation and extracellular-matrix remodeling, non-DNMT3A CHIP to myeloid immune regulation, and mLOX unexpectedly to cytotoxic lymphocyte/NK-cell responses. Mendelian randomization supported selected protein-disease relationships. Thus, age-related hematopoietic clones are not interchangeable markers of ageing but define alteration-specific systemic programs associated with distinct disease vulnerabilities.

Objective: Published P300-speller fusion schemes fix prior trust regardless of trial reliability; we tested whether a reliability estimate improves on it. Methods: We reanalyzed 3,373 archived P300-speller selections from 47 people with ALS (BigP3BCI). A fair, matched-search-space comparison, tuning both a fixed weight and an adaptive policy out-of-fold, was evaluated across 22 evaluable language-model priors up to 46.7B parameters. Two representative priors, GPT-2 and a classical 5-gram, additionally received detailed naive and mechanistic analyses. Results: No prior's 95% CI favored adaptive fusion under the fair comparison, despite unexploited oracle headroom at every scale. Under GPT-2, the naive comparison was significantly worse for adaptive fusion; both anchors converged to a degenerate or near-degenerate fair-comparison solution. For the representative anchors, three further controllers failed to convert that headroom into benefit; the fixed-fused posterior's output probability outperformed the best controller for flagging errors (2.8- to 3.8-fold enrichment). Conclusion: A tuned fixed weight is a difficult-to-beat default across the tested scale range; reliability estimation gave no deployable adaptive advantage. Significance: Adaptive weighting should be validated against a fairly tuned baseline across model families and scales; in this dataset, the fused output's confidence identified high-risk selections better than the tested purpose-built ranker.