BMC Genomics

As genetic testing becomes increasingly integrated into Parkinson disease (PD) research, including targeted testing for variants in LRRK2 and GBA1, the return of individual research results is becoming more common. However, limited qualitative data exists regarding how research participants experience genetic results disclosure and post-test genetic counseling in PD research settings. We conducted semi-structured qualitative interviews with participants (n=13) enrolled in the Parkinson Precision Medicine Initiative (formerly Parkinson Progression Markers Initiative; PPMI) who had received PD-related genetic test results and post-test genetic counseling. Interviews were conducted 1 to 3 weeks following result disclosure and analyzed using thematic analysis with a primarily deductive coding approach informed by study aims and inductive identification of emergent themes. Four primary themes were identified: (1) personal connection and motivations for participation, (2) centrality of result disclosure and information preferences, (3) emotional experiences and support needs, and (4) communication quality and alignment with participant needs. Overall, our findings underscore the importance of person-centered genetic counseling within PD research. As return of genetic and biomarker results in research and clinical trial contexts expand, thoughtful integration of relational, informational, and communication-focused practices will be essential to support participant engagement and trust.

Background Healthcare-associated infections pose a major burden to neonatal health worldwide and remain difficult to track in low-resource hospitals because patient movement data and pathogen genomic data are rarely integrated into actionable transmission models. Existing approaches are often restricted to specific settings, highly structured electronic health records (EHRs), or analyses focused on either patient movements or pathogen characteristics alone. To address this gap, we developed PathoPath, an open-source integrative modelling platform, and evaluated its utility in a high burden paediatric hospital in Dhaka, Bangladesh. Methods PathoPath is an open-source R package that combines electronic health records with whole genome sequencing data to generate contact networks from direct and indirect contacts using minimal structured inputs. We retrospectively applied PathoPath to 373 cases of Klebsiella pneumoniae species complex (KpSC) infection identified in 2021 at the largest paediatric referral hospital in Dhaka, Bangladesh. Ward level patient movement trajectories were used to reconstruct contact networks, and genomic data from isolates from children <60 days were integrated to identify probable dissemination of bacterial clones and antimicrobial resistance plasmids. Findings PathoPath identified 750 direct contacts among 317 patients, forming 25 connected components, with the largest including 93 patients. KpSC infections were identified across 21 of 37 wards, with the neonatal intensive care unit accounting for 77.9% of all cases. Integration of genomic and network data distinguished sustained clustering of ST147 from multiple probable inter-clonal dissemination events involving IncFII plasmids carrying blaNDM-5 and/or blaOXA-181 within ST16. Four dominant sequence types accounted for 65.6% of sequenced isolates, and carbapenemase genes were detected in 95.8%. Interpretation PathoPath reconstructs hospital-wide contact networks and integrates them with pathogen genomics to map probable dissemination of pathogens and antimicrobial resistance using minimal structured clinical data. It could support more targeted infection prevention and control in hospitals where granular digital records are not available.

Background. Type 2 diabetes mellitus (T2D) is defined by progressive pancreatic {beta}-cell dysfunction whose molecular underpinnings remain incompletely understood. Single-cohort transcriptomic analyses of donor islets have yielded heterogeneous gene lists of limited cross-study reproducibility, constraining both mechanistic interpretation and biomarker development. Methods. We combined two complementary analytical strategies applied to four public human islet transcriptomic cohorts (GSE25724, GSE20966, GSE38642, and GSE164416; n = 7-57 donors per contrast). For the integrative arm, three microarray datasets and one bulk RNA-seq dataset were processed independently and unified through gene-level random-effects meta-analysis, hallmark pathway scoring (GSVA/MSigDB), and iterative module refinement, yielding a two-axis disease framework. For the diagnostic arm, a consensus multi-method machine learning pipeline, combining LASSO penalized logistic regression, Support Vector Machine Recursive Feature Elimination (SVM-RFE), and Random Forest importance scoring, was applied to 184 differentially expressed genes from the RNA-seq cohort, with all normalization steps performed within leave-one-out cross-validation (LOOCV) folds to prevent data leakage. Machine learning classification of the RNA-seq cohort was additionally subjected to external transportability testing in the independent bulk human islet RNA-seq cohort GSE50244 using an overlap-restricted reduced score and a threshold fixed in the discovery cohort. Results. Meta-analysis across all four cohorts identified 337 high-confidence T2D-associated genes (96.1% directional concordance in beta-cell-enriched tissue). These were distilled into two refined 14-gene modules: ImmuneStress (MICB, HLA-DRA, HLA-DPA1, IL1R2, and others) and BetaCellIdentitySecretion (RASGRP1, PPP1R1A, SLC2A2, and others), whose composite IsletDysfunctionScore provided the most stable cross-platform separation of non-diabetic from T2D islets (Hedges' g = 1.80, p = 9.83 x $10^-17$, $\text{I}^2$= 0%). Consistent with progressive disease, IsletDysfunctionScore increased monotonically from non-diabetic to impaired glucose tolerance to T2D. Separately, the machine learning pipeline derived a 10-gene diagnostic panel: GABRA2, SLC2A2, ARG2, DKK3, PRIMA1, TAFA4, HHATL, PARVG, RNU1-70P, and the novel lncRNA ENSG00000284653, that achieved perfect discrimination in LOOCV (AUC = 1.000, sensitivity = 1.000, specificity = 1.000, zero misclassifications across all 57 donors). A leakage-verification experiment confirmed that this performance reflected genuine biological signal: global quantile normalization prior to cross-validation collapsed AUC to 0.380. External testing showed that 8 of the 10 panel genes were measurable in GSE50244. The frozen 8-gene reduced score retained strong discrimination (external AUC = 0.907), with 6 of 8 genes preserving directional concordance, but the discovery-derived threshold did not transfer because the external score distribution was shifted upward and compressed, yielding complete sensitivity but zero specificity at the frozen cutoff Conclusions. Integrating pathway-level meta-analysis with machine learning classification, we present a coherent two-axis model: immune/stress activation and loss of beta-cell identity/secretory competence, together with a compact, biologically interpretable 10-gene diagnostic signature. Panel genes converge on GABA signaling, glucose transport, arginine metabolism, WNT pathway inhibition, and a novel lncRNA, providing both mechanistic hypotheses and high-priority targets for external validation. These findings offer a reproducible transcriptomic scaffold for future mechanistic, biomarker, and clinical translation studies of human islet dysfunction. They also support external transportability of the core biological signal, while indicating that absolute operating thresholds are cohort-dependent and would require recalibration before deployment in independent datasets.

While microbiome is increasingly recognized as crucial for human health, translating this knowledge into effective healthcare and preventive strategies remains challenging. Many studies focus on identifying changes in microbiome composition associated with disease and evaluating the potential of such disease-associated microbial profiles as biomarkers for disease diagnosis. Under the hypothesis that microbiome dysbiosis may reflect physiological alterations present long before disease onset, in this work, we analyse the potential of disease-specific microbial signatures not as a diagnostic tool when the disease is already present, but as a means of health assessment in the general population. Moreover, instead of trying to define a single health measure, we believe it is necessary to consider several ways in which the microbiome departs from health, according to different disease-related physiological changes. To evaluate our assumptions, we designed a two-stage study: the identification of disease-specific microbial signatures (discovery stage) and, subsequently, the study of their distribution in the general population to assess associations with general health (external validation stage). Specifically, in the discovery phase we characterized 16 disease-specific bacterial signatures from large public microbiome data using a compositional data analysis methodology. In the second phase, we quantified these microbial signatures in the Lifelines-DMP cohort, a large population-based cohort, and evaluated their association with self-reported health status. Results indicate that most disease-specific microbial signatures associate with health status, supporting our assumption that microbial composition can capture physiological alterations before disease onset, and highlighting the importance of considering multiple ways in which microbiome departs from a healthy state. These findings reaffirm the potential of microbial information as an additional tool in preventive medicine.

Abstract Objective: The onset of hypertension occurs at a younger age in China, and the relationship between health literacy and quality of life among middle-aged and older hypertensive patients remains unclear. This study explored whether perceived social support and self-efficacy mediate the association between health literacy and quality of life in middle-aged and older hypertensive patients. Methods: A questionnaire was administered to 1,015 middle-aged and older hypertensive adults from communities in six central provinces of China. The EQ-5D scale, Perceived Social Support (PSS) scale, Self-Efficacy Scale (SES), and Health Literacy Scale (HLS) were used to assess quality of life, social support, self-efficacy, and health literacy, respectively. Mplus 8.3 software was used to construct a structural equation model for path analysis. Results: The mean PSS, SES, HLS, EQ-5D, and EQ-VAS scores were 15.57{+/-}3.45, 10.61{+/-}2.41, 9.49{+/-}2.86, 0.88{+/-}0.18, and 71.06{+/-}17.49, respectively. Health literacy and quality of life scores significantly differed among middle-aged and older hypertensive patients, and both showed positive correlations with perceived social support and self-efficacy (both P<0.001). Perceived social support and self-efficacy exhibited a chain mediated effect on the relationship between health literacy and quality of life (EQ-5D utility index and EQ-VAS), accounting for 28.57% of the total effect of the EQ-5D utility index and 27.26% of that of the EQ-VAS. This study is the first to elucidate the mechanism by which health literacy influences quality of life in middle-aged and older hypertensive patients through the chain-mediated effect of perceived social support and self-efficacy. Conclusion : Health literacy is significantly correlated with quality of life in middle-aged and older hypertensive patients. This correlation can directly or indirectly explain the impact on quality of life through mediating pathways involving perceived social support and self-efficacy. Keywords: hypertensive patients, perceived social support, self-efficacy, health literacy, quality of life, mediating effect

Consanguinity is a reproductive union between individuals who share a recent common ancestor. These unions are common in many regions of the world and increase the burden of rare recessive disorders by elevating autozygosity in offspring. Current reproductive genetic screening focuses on a limited set of known pathogenic variants, leaving most recessive risk unaddressed. Here we argue that embryo-level autozygosity, quantified as the fraction of the genome in long runs of homozygosity (FROH), is a potentially actionable genomic biomarker that can be integrated into routine preimplantation genetic testing as a homozygosity-informed embryo-prioritization framework (PGT-H) that can be layered onto existing embryo biopsy workflows when couples are already undergoing IVF with PGT-A or PGT-M. Using forward simulations of first-cousin and double-first-cousin couples, we show that siblings conceived by the same couple span a wide range of FROH; selecting the lowest-FROH candidate from a cohort of five embryos reduces FROH by approximately 40% on average. Combining these reductions with empirical effect-size estimates, we estimate that for first-cousin couples this strategy could reduce risk of intellectual disability by roughly 35-45% (corresponding to an absolute risk reduction of about 1.8-2.2%) and potentially reduce excess recessive disease burden, while also modestly reducing risk of common diseases such as type 2 diabetes. We outline how existing PGT-A and PGT-M workflows could potentially be extended to report embryo-level FROH and discuss ethical and counseling considerations. Autozygosity-based embryo prioritization offers a principled way to address a component of recessive risk that current variant-centric approaches miss.

Background: Chronic diseases and symptom syndromes often develop after prolonged biological changes that may precede formal diagnosis. RNA-based metatranscriptomics captures active microbial and human gene expression and may provide a functional layer for disease risk evaluation. To address this translational gap, we developed and validated a Disease Risk Score (DRS) framework that integrates metatranscriptome-derived pathway activity scores from stool, saliva, and blood samples, and evaluated its potential clinical utility as an adjunct risk-evaluation tool. Methods: DRS uses disease-specific sets of pathway activity scores derived from stool and saliva microbial functions, stool and saliva microbial taxa, and blood human gene expression. For each disease, 'not optimal' pathway scores are aggregated into a normalized cumulative odds ratio, or cOR, using score-level odds ratios, statistical significance, and literature-supported biological relevance derived from a Development Cohort of 22,369 individuals. A cOR [&ge;] 5 is defined as high risk. Performance is evaluated in an independent Validation Cohort of 15,908 individuals using self-reported diseases as the reference. Disease support requires both significant cOR separation between self-reported and not-reported (Cohen's d [&ge;] 0.2) and risk ratio enrichment of self-reported disease among individuals classified as high risk (95% CI of Risk Ratio > 1). Results: Of 20 initially evaluated diseases, 15 meet the prespecified validation criteria on the independent validation cohort: ADHD, anxiety, chronic fatigue syndrome, depression, GERD, hypertension, inflammatory bowel disease, IBS-C, IBS-D, insomnia, MASLD, obesity, obstructive sleep apnea, Sjogren's syndrome, and type 2 diabetes. Five selected clinical scenarios illustrate how DRS can support clinician-mediated decision making, including IBS subtype reclassification, improved diagnostic acceptance in IBS-D, personalized lifestyle counseling in MASLD and early type 2 diabetes, and diagnostic uncertainty in atypical GERD. Conclusions: DRS is a metatranscriptomics-based risk-stratification framework that aggregates active microbial and human pathway signals into interpretable disease-specific risk estimates across a wide range of disease conditions. Validation against self-reported disease labels in an independent cohort shows significant risk enrichment for each of 15 diseases. DRS is intended as an adjunct to clinical evaluation: a decision support tool in situations where routine care encounters uncertainty, delay, or low patient engagement. Future prospective studies using clinically adjudicated endpoints are needed to assess calibration and clinical outcomes.

Background Thalassemia is one of the most common monogenic disorders worldwide, current screening strategies combining hematological testing with molecular assays still carry a risk of missed diagnoses and undesirable efficiency, particularly for complex structural variants and rare mutations. Methods In this prospective double-blind, multicenter cohort study of 3,842 participants (3,362 pregnant women and 480 male partners), we conducted a head-to-head comparison to systematically evaluate the incremental clinical value and detection performance of single-molecule nanopore sequencing in thalassemia (SMITH) against conventional hematological testing and next-generation sequencing (NGS). Findings The overall concordance rate between NGS and SMITH was 98.6% (3789/3842). The discrepant cases (n=53) were directly attributed to the superior detection capabilities of SMITH, which successfully identified complex structural rearrangements-including 45 -globin gene triplications and four HK alleles-that were missed by NGS. Furthermore, SMITH accurately detected four rare variants (c.134_135insT/, c.-22(C>T)/, {beta}N/{beta}c.316-290delinsAGGGCAATAATTT and {beta}3.5 kb deletion/{beta}N ) and resolved ten trans and three cis configurations within the globin gene allele. Clinically, these technical advantages translated to a 9.3% (5/54) increase in the detection rate of high-risk prenatal couples, effectively preventing one birth affected by moderate-to-severe thalassemia. Additionally, SMITH corrected a diagnostic discrepancy in one case (HK vs. -3.7), sparing the couple from an unnecessary invasive procedure. Interpretation Our findings demonstrate that SMITH provides a powerful platform for resolving globin gene rearrangements, detecting rare variants, and enabling direct haplotype phasing. By effectively eliminating diagnostic blind spots, SMITH is expected to become an optimal method for thalassemia prevention programs. Funding This study was supported by Chinese National Natural Science Foundation Projects 81760037 and 82271894.

Purpose: To evaluate the incidence and baseline predictors of intraocular pressure (IOP)-lowering treatment following detection of referable glaucoma by teleretinal screening. Design: Retrospective cohort study. Methods: Participants were derived from a safety-net teleretinal diabetic retinopathy screening program (2013-2024). Participants included individuals who screened positive for referable glaucoma (cup-to-disc ratio [CDR] [&ge;]0.6 or CDR asymmetry [&ge;]0.2) and completed in-office diagnostic evaluation. The primary outcome was initiation of IOP-lowering treatment (medication, laser, or surgery) and the secondary outcome was intervention with surgery. Cumulative incidence functions were estimated, accounting for loss to follow-up. Fine-Gray models were used to identify baseline screening predictors to risk stratify each outcome. Glaucoma diagnosis was approximated using diagnostic codes and chart review. Results: 2,367 participants were included. The cumulative incidence of treatment was 19.6% (95% CI: 18.0-21.2) at Year 1 and 45.1% (42.1-48.1) at Year 8. Early treatment occurred primarily in glaucoma cases, whereas treatment accumulated longitudinally in glaucoma suspects, reaching 36.5% (31.6-41.5) by Year 8. Surgery was less common (8-year incidence: 5.3%). Baseline screening data predicted treatment and surgery, enabling risk stratification. At Year 8, cumulative incidence differed substantially between high- and low-risk groups (treatment: 59.9% vs. 31.2%; surgery: 9.7% vs. 1.0%). Older age (sub-distribution hazard ratio [SHR] 1.03 per year, p<0.001), Black race (SHR 1.50, p<0.001), and personal history of glaucoma (SHR 1.90, p<0.001) were associated with treatment; Asian race was protective (0.71, p=0.03). Older age (SHR 1.06, p<0.001), worse visual acuity (SHR 5.11 per logMAR unit, p<0.001), and screening at a hospital-based site (SHR 2.46, p=0.003) were associated with surgical treatment. Conclusion: Nearly half of safety-net diabetic patients screening positive for referable glaucoma initiated IOP-lowering treatment over 8 years, while few received surgery. Baseline screening characteristics enabled risk stratification of treatment and surgery. These findings address an evidence gap about longitudinal consequences of screening and suggest that its impact extends beyond detection of prevalent glaucoma to include identification of high-risk glaucoma suspects who warrant ongoing surveillance.

Background: Neurological, neuropsychiatric, and neurodevelopmental disorders cluster in ALS families, sharing a common genetic architecture with ALS. Pathogenic variants in genes associated with other neurological, neurodevelopmental, or neuropsychiatric disorders may also co-occur in ALS and modify phenotype. We have sought to determine the prevalence and clinical pattern of likely-pathogenic/pathogenic (LP/P) non-ALS neurological, neurodevelopmental, and neuropsychiatric variants, alone and in combination with ALS-gene variants, in two large ALS cohorts. Methods: Whole-genome sequencing (WGS) of 469 Irish and 774 Answer ALS people with ALS (pwALS) was analysed for ClinVar LP/P variants associated with other neurological (n = 15541), neurodevelopmental (n = 9761), and neuropsychiatric (n = 321) phenotypes. Inheritance patterns for associated genes (autosomal recessive/autosomal dominant) along with the associated phenotype were validated using OMIM. Standardised clinical data included family history, site and age of onset, El Escorial category, survival, motor decline, and cognitive and behavioural assessments. Known ALS-gene variants and C9orf72 repeat expansion status were included for each cohort. Results: Non-ALS neurological variants were identified in 47/469 (10.0%) Irish and 69/774 (8.9%) Answer ALS participants, most frequently in hereditary spastic paraplegia-associated genes (3.2% Irish; 2.8% Answer ALS). Irish neurological variant carriers showed higher frequency of respiratory onset (10.6% vs 1.2%, Fisher's exact p = 0.002, {Phi} = 0.20) and fewer premorbid behavioural symptoms (0.92 +/- 0.56 vs 3.08 +/- 0.97, Cohen's d = -0.40). Neurodevelopmental variants occurred in 12/469 (2.6%) Irish and 20/774 (2.6%) Answer ALS participants. In the Irish cohort, neurodevelopmental variant carriers had significantly shorter survival in Cox proportional hazards model (log-rank p = 0.005), corresponding to a more than two-fold increased hazard of death (HR = 2.25, 95% CI 1.26-4.00), and had significantly increased familial burden of neuropsychiatric disorders among first- and second-degree relatives (negative binomial IRR for carriers = 2.41, 95% CI: 1.12-5.18, p = 0.025). Across combined cohorts, 18 individuals (Irish n = 8; Answer ALS n = 10) carried [&ge;]2 LP/P variants spanning ALS and non-ALS genes. Conclusion: Rare LP/P variants in genes associated with other neurological and neurodevelopmental disorders occur in up to 12% of pwALS across two independent cohorts. Carriers show distinct phenotypes, shorter survival, and characteristic family history patterns. These findings suggest that extended pleiotropic and oligogenic architectures may contribute to ALS heterogeneity.

Diabetic peripheral neuropathy (DPN) is a common and disabling condition for which no disease-modifying therapies are available. Glycemic and metabolic drivers do not fully explain why only a subset of individuals with diabetes develop DPN, and genetic contributors remain poorly defined. We aimed to perform a multi-population genome-wide association study (GWAS) of DPN to highlight potential new etiological pathways and therapeutic targets. Methods We performed a multi-population GWAS of neuropathy in people with and without diabetes using the VA Million Veteran Program and UK Biobank, followed by replication in the All of Us Research Program (AoU), and gene-based and gene-set analyses to identify implicated pathways. Causal relationships between circulating serine levels and DPN were further tested using two sample Mendelian randomization. To further evaluate pathogenic potential, we analyzed rare, high impact variants in GWAS implicated genes among individuals with unresolved inherited neuropathies using the GENESIS platform. Findings Among individuals with type 2 diabetes, we identified seven genome wide significant loci (p<5x10-): PHGDH and PSPH (key serine synthesis genes), TEAD1, CYP4F11, LARGE1, FTO, and COBLL1. No loci were significant in individuals without diabetes or with type 1 diabetes. Four loci (PHGDH, TEAD1, FTO and CYP4F11) replicated in AoU (p <0.05). Mendelian randomization demonstrated that higher genetically predicted serine levels were associated with lower DPN risk, consistent with a causal role of serine metabolism in disease pathogenesis. Rare-variant burden analyses revealed associations of predicted deleterious variants with inherited neuropathy case status in PHGDH (odds ratio [OR] 12.7 [95% CI 7.9, 20.4]), PSPH (OR 8.5 [7.2, 10.2]), PHKG1 (OR 4.8 [3.7, 6.3]), and LARGE1 (OR 0.007 [0.0004, 0.1]). Interpretation Convergent genetic evidence across common and rare variation implicates serine synthesis as a key pathway in DPN. These findings link diabetic and inherited neuropathies through a shared metabolic mechanism, identifying serine metabolism as a potential therapeutic target.

Background: Artemisinin-based combination therapies remain the mainstay of malaria control strategies; nevertheless, the advent of genetic markers linked to partial artemisinin resistance in Plasmodium falciparum has elicited substantial concern across African settings. To assess the prevalence, geographic distribution, and clinical associations of these molecular markers, we undertook a systematic review and meta-analysis of observational cohort studies.Methods: We conducted a search of cohort studies published between January 2015 and June 2025, following PRISMA 2020 guidelines. We queried databases including PubMed/MEDLINE, Scopus, Web of Science, and CINAHL. Eligibility required prospective enrollment of patients, longitudinal monitoring (therapeutic efficacy studies), and pfkelch13 propeller domain genotyping.Results: A meta-analytical synthesis of 888 isolates from six core prospective cohorts revealed a pooled prevalence of 6% (95% CI: 2.1%-11.8%) for validated pfkelch13 mutations. A profound geographic dichotomy was identified: while West and Central African cohorts maintained a 0% prevalence, East African hotspots showed significant expansion, with prevalence reaching 12.8% in Rwanda and up to 25.5% in Northern Uganda; high statistical heterogeneity (, ) reflects this biological divergence. Conclusions: These findings highlight the established and expanding presence of artemisinin partial resistance in East Africa. Standardized surveillance is essential to adapt malaria control policies across the continent. Keywords: Africa; artemisinin resistance; clinical indicators; pfkelch13 gene; molecular markers; partial resistance; Plasmodium falciparum.

Background. Blood-based biomarkers are increasingly proposed for identifying high-risk individuals before clinical disease and for making prevention-oriented trials more efficient. Prognostic enrichment can increase event rates, but trial efficiency also depends on whether the intervention effect is preserved in the enriched population. Methods. Using the UK Biobank Pharma Proteomics Project, we trained disease-specific proteomic risk scores (ProRS) from 2,916 plasma proteins with elastic-net Cox models. We compared ProRS, polygenic risk scores (PRS), and combined PRS--ProRS scores across ten incident diseases. We estimated cumulative incidence and theoretical two-arm time-to-event trial sample sizes across risk strata. To evaluate effect preservation, we examined six intervention-analogue exposure--outcome pairs spanning genetic (PCSK9/coronary artery disease, APOE/Alzheimer's disease, PPARG/type 2 diabetes, IL23R/Crohn's disease), behavioural (physical activity/all-cause mortality), and pharmacological (RAAS inhibitors versus calcium channel blockers/coronary artery disease) examples. Results. ProRS outperformed PRS for 9 of 10 diseases (median C-index 0.75 versus 0.61). ProRS and PRS were weakly correlated (median Pearson |r| = 0.04), and joint PRS--ProRS stratification identified groups with higher observed incidence than either score alone for several endpoints. In the top risk quartile, combined-score enrichment reduced theoretical required sample sizes by 32--74\% under a fixed 20\% relative hazard reduction. These gains were not always preserved when stratum-specific intervention-analogue effects were used. Effects were broadly preserved for APOE/Alzheimer's disease and physical activity/mortality. The PPARG/type 2 diabetes effect attenuated toward the null under all three score types, showing that event-rate enrichment does not guarantee effect preservation. For IL23R/Crohn's disease and the antihypertensive comparison, point estimates differed across score types -- preserved under polygenic but attenuated under proteomic enrichment -- but confidence intervals were wide and overlapping. Conclusions. Proteomic risk scores can identify high-event-rate populations for prevention-oriented trials, but event-rate enrichment alone is insufficient for trial design. Biomarker-guided enrichment should evaluate mechanism-specific effect preservation and may be preferable as a stratification or adaptive-design variable rather than as a restrictive eligibility criterion.

Introduction: Malaria, primarily transmitted by Anopheles mosquitoes, remains a major public health concern in Maiduguri, Borno State, Nigeria. While conventional control methods (e.g., ITNs) face challenges due to insecticide resistance and accessibility constraints, many communities rely on locally sourced natural products. This study aimed to assess the prevalence, usage patterns, and associated factors of these natural alternatives. Methods: A cross-sectional survey was conducted across three purposefully selected communities in Maiduguri (Mairi, Furi, Lagos Street). A total of 450 household heads were interviewed using a structured questionnaire, collecting data on socio-demographics, specific natural products used, method of application, frequency, and perceived efficacy. Data were analyzed using descriptive statistics and binary logistic regression. Results: Overall usage prevalence of natural products was high at 68.4%. The most common products identified were Neem (Azadirachta indica) extract (45.9%) and burnt Lemon Grass (Cymbopogon citratus) (31.2%). Usage pattern was predominantly indoor fumigation (burning), and over 70% of users prepared the products crudely at home. Logistic regression revealed that rural residence (Odds Ratio (OR): 2.1; p<0.01) and low education level (OR: 1.8; p<0.05) were significant independent predictors of higher natural product reliance. Conclusion: Natural products constitute a widely adopted, community-driven vector control method in Borno State. The high prevalence and association with vulnerable populations suggest an urgent need to standardize the preparation and application of these products for potential integration into regional malaria control programs. Keywords: Anopheles, Adulticides, Borno State, Malaria, Natural Repellents, Vector Control, Usage Pattern.

Polygenic risk scores (PRSs) are emerging as powerful tools for quantifying inherited risk for common diseases and, in some cases, are approaching clinical implementation. A major concern for PRS implementation is their limited accuracy in non-European populations, particularly in those of African ancestry. However, past evaluations have focused on metrics such as relative risk or AUC, which do not capture background risk arising from contextual factors. We introduce a novel measure of variable importance, the conditional average derivative estimator (CADE), to evaluate PRS utility across diverse contexts and populations within absolute risk models that integrate PRSs with other relevant risk factors. We illustrate this framework by integrating PRSs for breast and prostate cancer within age-specific absolute risk models for incidence and mortality fit using individual-level data from the All of Us Research Program with inputs from the National Cancer Institute SEER cancer registry. Our projections show that although the PRSs are known to have the lowest discriminatory accuracy in African Americans (AA), there are contexts in which they provide greater utility, such as for the stratification of prostate cancer risk and mortality, where the CADE values for AA were 2- and 7-fold higher than for European Americans. These findings suggest that conclusions about the limited clinical utility of PRS in non-European populations may be premature and underscore the need to quantify PRS risk-stratification utility at the absolute-risk level, while accounting for disease onset, survival, and broader health and economic factors.

Introduction: Variants in the polymerase gamma (POLG) gene are associated with a wide range of mitochondrial disorders. Emerging evidence suggests a potential link between POLG variants and Parkinson's disease (PD); yet, results remain inconclusive. Objectives: To investigate the genetic spectrum and prevalence of POLG variants in PD across diverse ancestries. Methods: We leveraged multi-ancestry genetic data from the Global Parkinson's Genetics Program (GP2), including genotyping data from 98,589 and short-read sequencing data from 36,022 individuals. We performed a POLG rare variant screen, case-control association, and gene-level burden analyses. Results: Five PD cases carried potentially biallelic rare pathogenic/likely pathogenic POLG variants. Additionally, 228 individuals (<1%; 161 PD cases, 28 individuals with other neurological disorders, and 39 controls) carried 34 distinct rare pathogenic/likely pathogenic heterozygous variants, with no significant frequency differences between cases and controls, except for the p.Ala467Thr variant in the European population. The co-inherited pathogenic variants p.Thr251Ile and p.Pro587Leu were present in <1% of both cases and controls, with no significant group differences. Burden and variant-level association analyses showed no association between rare POLG variant burden or common POLG variant enrichment and PD. Conclusions: POLG variants are overall rare in PD. The identification of rare pathogenic variants among PD cases suggests that POLG-related mitochondrial dysfunction may contribute to PD in isolated instances, particularly under recessive inheritance. Our findings support a role for POLG variants in select cases and underscore the need for larger-scale sequencing and functional studies.

Background Coeliac disease (CD) diagnosis on duodenal biopsies is limited by interobserver variability. We have previously demonstrated pathologist-level performance with our artificial intelligence (AI) model for the histopathological diagnosis of adult CD, but not in paediatric practice. As paediatric CD screening programmes expand internationally, accurate and scalable diagnostic tools are needed. We investigated whether an AI model trained exclusively on adult whole-slide images (WSIs) can generalise to paediatric CD diagnosis across independent centres. Methods A training and validation dataset of 9,958 WSIs from 8,421 adult patients (961 CD) from five centres was used to develop an ensemble of multiple-instance learning models using features from a foundation model. Testing was performed on 708 consecutive paediatric patients (86 CD) from two centres (Edinburgh and Southampton) not included in training. Model calibration was assessed, and probability outputs were grouped into clinically interpretable categories. Findings In adult cross-validation, the AI model achieved an area under the receiver operating characteristic curve (AUC) of 98.7%, sensitivity of 84.9%, specificity of 99.0%, and negative predictive value (NPV) of 98.1%. On testing (paediatric) datasets, performance remained high (AUC 98.8%, sensitivity 80.2%, specificity 98.4%, NPV 97.3%). Restricting analysis to predictions outside the intermediate-probability range (predicted CD probability <10% or [&ge;]65%; 85.3% of cases) improved sensitivity to 100% and specificity to 98.7%. No misclassifications were observed among high-confidence predictions (<2% or [&ge;]85%; 66.0% of cases). The expected calibration error was 0.03. Performance improved significantly when biopsies from both duodenal sites (bulb [D1] and descending [D2/3]) were considered. Interpretation Our AI model, trained on adult biopsies, generalises to paediatric CD diagnosis across centres and scanner platforms. Well-calibrated probability outputs provide clinically interpretable measures of diagnostic confidence and could support safe identification of CD-negative biopsies within defined thresholds. These findings demonstrate the feasibility of applying adult-derived AI models in paediatric populations and reinforce the importance of multi-site (D1 & D2) biopsy sampling.

Flow cytometry can establish T cell clonality by detecting a restricted expression pattern of the T cell receptor (TCR) {beta} constant region (TRBC), expressed in association with CD3. However, T cell neoplasms frequently lose surface expression of the CD3/TCR complex, posing a challenge to demonstrating T cell lineage and clonality. To address this challenge, here we present a 12-color flow cytometry panel, called cytoTCR, to characterize cytoplasmic expression of CD3/TCR complex components. We apply cytoTCR to 38 patient specimens with immunophenotypically abnormal T cell populations, demonstrating this approach can efficiently establish T cell lineage and clonality in challenging T cell neoplasms that have lost surface CD3 expression. While we show that natural killer (NK)-lineage neoplasms can express cytoplasmic CD3 at similar levels to T cells, we show that absent expression of cytoplasmic TCR components by mature lymphocytes can help confirm NK cell lineage. We demonstrate that cytoTCR can detect cytoplasmic TRBC-restriction in challenging cases of null-phenotype anaplastic large cell lymphoma, which lack surface expression of pan-T cell antigens. In cases of T-lymphoblastic leukemia, cytoTCR shows that cytoplasmic TRBC expression matches the expected developmental stage of the leukemia. Finally, we use cytoTCR to characterize atypical cCD3-CD7- T cells in a patient with a history of T-lymphoblastic leukemia as well as recent CAR-T therapy, showing that this atypical population is polytypic and represents CAR-T product rather than residual disease. Our study presents a broadly applicable flow cytometric approach to simultaneously assess T cell lineage and clonality in suspected T lineage populations with absent surface CD3 expression.

In spite of well-established global immune landscape, SARS-CoV-2 is still able to further spread and continue causing infection waves. The current understanding about the reason behind is limited, and it is still difficult to predict the evolution or spreading tread of SARS-CoV-2. Therefore, it is necessary to investigate whether the establishment of population immunity has changed the virus evolution or spreading pattern. In this investigation, one overall analysis of the SARS-CoV-2 spreading in the past several years have been carried out through one thorough genomic epidemiology study, with Germany being chosen as one representative location in view of the systemic efforts for genomic surveillance. The growth advantage of a few predominant variants in its early spreading period has been evaluated through a logistic regression model. The results have revealed that the major circulating SARS-CoV-2 variants since 2023 are mainly derived from the Omicron BA.2 family. Since middle of 2024, most predominant variants were produced primarily through recombination, indicating that the evolution derived from recombination might be the major driving force for the continuous spread of SARS-CoV-2 despite the existence of population immunity. Furthermore, the lower growth advantage of recently emerged variants might possibly lead to a tread of reduction in the frequency of infection wave. The information revealed from this investigation suggests that although short-term spreading tread can be affected by specific virus feature as well as local immunity landscape, the long-term spreading tread is mainly decided by the genomic diversity of the viruses, and can be predicted through phylogenetic and genomic epidemiology investigation. The results have emphasized the importance of maintaining the efforts for genomic surveillance of SARS-CoV-2, which is essential from both medical and research perspectives.

Malaria molecular surveillance (MMS) is becoming increasingly common in endemic settings and has been proposed as a tool for monitoring parasite transmission to inform programmatic decision-making. However, the conditions under which parasite genetic metrics provide interpretable signals for broader use cases, such as assessing intervention impacts and detecting importation, remain under-characterized. We present EMOD with Full Parasite Genetics (FPG), a simulation framework designed to explore how parasite genetic metrics arise from transmission, intervention, importation, and sampling processes at programmatically relevant timescales. Using seasonal scenarios across a range of transmission intensities, we demonstrate three principal findings. First, genetic metrics can detect insecticide-treated net intervention impacts at seasonal and yearly timescales, but the strength, timing, and form of the relationship between genetic and epidemiological measures vary by metric and sampling timing. Second, importation can break the expected relationship between parasite genetic diversity from local transmission intensity at very low incidence, allowing low-transmission settings with substantial importation to maintain elevated diversity metrics. Third, convenience sampling practices, including sample size, collection timing, and the clinical composition of sampled populations, introduce non-random biases in genetic metric estimation in a way that obscures the true transmission signal. Together, these findings show that parasite genetic metrics can support operational surveillance, but that their interpretation depends on transmission context, importation, metric choice, and sampling design. EMOD FPG provides a framework for evaluating these dependencies in future setting-specific analyses and for guiding the interpretation of parasite genetic data across sites and over time.