Genomics

Currently, the genetic architecture of Middle Eastern populations is underrepresented in global genomic databases. This gap increases the rate of Variants of Uncertain Significance (VUSs) and clinical misinterpretations of genomic data especially in Middle Eastern populations. Whole exome sequencing was conducted on 90 healthy individuals from Jordan and the data were analysed using Principal Component Analysis (PCA) and multi-computational filtering. PCA revealed a double ancestry (EUR-AFR) admixture rather than a triple admixture (EUR-AFR-AMR). More than 3,500 populations-specific variants (PSVs) were identified, of which 72% were singletons. Additionally, 19 variants were significantly enriched compared to the maximum allele frequencies in public global databases (Fisher's exact test with Benjamini-Hochberg false discovery rate correction, p-value < 0.05). Consequently, the results suggest the reclassification of variants of Uncertain Significance (VUS) which reside in the ECE2 gene to likely benign and the variants of Conflicting Classification of Pathogenicity in the genes IL1RN and THPO to benign based on the significant allele frequency (AF=0.0389, p-value < 0.05). Furthermore, a pathogenic ClinVar variant was identified in a healthy individual, warranting careful interpretation. The findings underscore the importance of identifying PSVs in order to minimize or even prevent clinical misdiagnosis and highlight the unique genetic signature in Jordan. The study serves as a foundational resource for precision medicine in the region.

Introduction Obstructive lung diseases (OLDs) are responsible for high rates of illness and death worldwide. Inflammation, chronic airflow limitation, and bronchial remodeling occur in OLD and eventually result in the unique respiratory sounds. Despite its subjective and having low reproducibility, still traditional auscultation using a manual stethoscope is the main method used to identify the lung sounds. Nevertheless, the combination of recent advancements in digital stethoscopes and AI (Artificial Intelligence) has permitted the objective measurement of lung sounds. Nevertheless, there is a lack of standardized, region-specific databases for AI training and validation. Even though lung sound classification is an emerging aspect in research and telerehabilitation the lobar wise acoustic pattern is still novel due to lack of prevailing database to train AI models. Identifying this gap this study aims to develop an acoustic repository and analyze the data using segmental lung sounds from patients with OLDs and healthy controls through an electronic stethoscope. Methods and analysis This is a cross sectional observational study involving 120 participants (60 OLD patients and 60 healthy controls). Lobar wise acoustic signals will be captured using an electronic stethoscope in healthy and diseases population. The data will be analyzed using Audacity software for annotations and then it will be used for feature extraction and statistical analysis. The acoustic features extracted through Audacity, will include frequency, intensity, pitch, and root mean square (RMS) energy. Repeated measures ANOVA will be applied to compare mean sound intensities across lung segments while Pearson correlation will be used to assess associations with body composition parameters. The data will then be standardized for AI-based diagnostic applications. Ethics and dissemination The study is being reviewed from the Ethics Review Committee, Faculty of Medicine, University of Peradeniya (2025/EC/87) will be sought. Informed consent will be obtained in writing. The dissemination of results will take place through peer-reviewed publications and the creation of a public database containing lung sounds from the region.

Background: Diabetes mellitus (DM) worsens pulmonary tuberculosis (TB) and drives systemic hyper-inflammation, but the underlying mechanisms remain unknown. Neutrophils have key roles in TB immunopathology and lung cavitation. Here, we determine the role of neutrophils in DMTB patients and in driving TB immunopathology. Methods: Sputum and plasma from 30 TB and 30 DMTB patients were analysed for proteases and cytokines using Luminex bead array. Whole blood transcriptomics identified transcriptional differences. Single-cell RNA sequencing characterised neutrophil subsets and dysregulated pathways. Neutrophil function of poorly-controlled DM patients (HbA1c>8%) and healthy controls (HC) were examined following Mycobacterium tuberculosis stimulation, including reactive oxygen species (ROS), neutrophil extracellular traps (NETs), and phagocytosis. Pathways were interrogated using chemical inhibitors, protein array and western blot. Results: Compared to non-diabetic TB patients, poorly-controlled DMTB patients showed up-regulated sputum MMP-8 and MMP-9, associated with increased collagen-destruction and lung cavity formation. Circulating neutrophil count and neutrophil-derived plasma MMP-8 were up-regulated, alongside transcriptional enrichment of extracellular matrix degradation and inflammatory pathways including TNF and RAGE. Single-cell profiling identified reduced cycling neutrophil subset and myelocytes in DMTB, with overall reduced antibacterial and cell-killing signatures. Ex vivo mycobacterial stimulation of DM neutrophils increased ROS and MMP-9 with impaired NETs and delayed phagocytosis. TNFR1, TNFR2, and RAGE were up-regulated. RAGE inhibition with rosiglitazone mitigated Mtb-induced ROS and MMP-8 release. Conclusion: DM worsens neutrophil-driven tissue destruction and inflammation in TB via dysregulated TNF and RAGE-signalling, priming neutrophils towards immunopathology. Targeting RAGE alongside tight glycaemic control may dampen neutrophil hyper-inflammatory responses to limit tissue destruction.

Precision medicine aims to advance our ability from a "one-size-fits-all" approach to personalized and predictive healthcare across diverse populations. It promotes integration of multi-omics and phenotypic data to understand disease mechanisms and discover novel biomarkers and risk factors, which could be used to predict and prevent critical diseases in individual patients across diverse populations. The potential implications of precision medicine approach can accelerate our ability to classify patients at higher risk of developing critical diseases, improve diagnostic capabilities, develop deeper understanding of individual risk, investigate racial differences and demographic characteristics, and find relationships between genetic variants, expressions, and diseases. This study focuses on implementing an innovative and data driven framework of translational bioinformatics and Machine Learning (ML) techniques to analyze multi-omics, including RNA-seq and Whole-Genome Sequencing (WGS) data, generated using blood samples of randomly consented patients. First, we utilized bioinformatics pipelines to identify differentially expressed genes and their pathogenic and likely pathogenic variants for the downstream data analysis, annotation, and visualization. Then, applied a nexus of ML models for multi-omics biomarker discovery, disease prediction, density-based clustering, single-patient profiling, and pathogenicity classification. WGS data analysis supported the exploration of genetic variation and diversity among patients to identify known and novel biomarkers, whereas RNA-seq data analysis improved our understanding of functional and biological pathways that underlying disease states. We classified and clustered pathogenic variants and expressions across various genes and discovered numerous diseases leading risk factors. Our results include gene-disease associations and captured common pathways across the broader population, demonstrating a level of sensitivity and accuracy that has broad clinical implications. We validated our results through clinical records, and state of the science literature. This study delves into the strengths of multi-omics data integration and capabilities of ML application in genetically diverse and complex patient cohorts. Our approach has the potential to elucidate complex gene-disease interactions for genetically diverse populations, which can support earlier diagnoses for patients in many disease realms.

Background Bacterial fitness is shaped by interactions between genome variation and environmental context, yet how these interactions determine its predictability and heritability remains unclear. In the clinically important pathogens of Klebsiella pneumoniae, a leading cause of hospital-acquired infections, this question is particularly pressing. Despite extensive genomic characterization, we still lack a systematic understanding of how genome-wide variation translates into fitness across diverse environments in K. pneumoniae. Methods We filled this gap by profiling a systematic collection of 1,462 clinical K. pneumoniae isolates across 214 diverse environmental and pharmacological stress conditions using high-throughput chemical genomics. Fitness was quantified from colony growth and integrated with whole-genome sequencing data. Genome-wide association analyses identified genetic determinants of fitness, and machine learning models incorporating genomic features were used to predict fitness.Results Fitness exhibited a strongly environment-dependent genetic architecture, with modest but significant concordance between genetic background and phenotypic variation. Under antibiotic and stress-combination conditions, fitness was driven by discrete, high-effect determinants, including known resistance genes, resulting in stronger signals and improved predictability. In contrast, non-antibiotic environments showed more polygenic and distributed architectures with weaker associations. Genome-wide analyses identified both established and previously uncharacterized genes linked with fitness across conditions. Resistance and virulence determinants exhibited clear context-dependent trade-offs, conferring fitness advantages under selection but imposing costs in non-selective environments. Consistent with this, plasmid carriage showed environment- and genotype-dependent fitness effects, with benefits under antibiotic pressure and measurable costs otherwise. Genomic variant-based models for fitness prediction achieved moderate performance (Mean Spearman correlation ({rho}) = 0.36 (95% CI: 0.18-0.67) for predicted versus observed values in unseen data) across conditions, with improved accuracy under strong antibiotic selective pressures, and produced well-calibrated prediction intervals with high coverage. Despite strong population structure effect on predictions, models captured predictive gene and SNP biomarkers for fitness. Conclusion These findings highlight that bacterial fitness is an emergent property of genome-environment interactions rather than a fixed attribute of genotype. This work establishes a unified high-dimensional genotype-phenotype framework linking genomic variation to fitness across diverse conditions in a major pathogen, with broader implications for other pathogenic bacterial species.

Background: Citrin deficiency, caused by biallelic pathogenic variants in SLC25A13, must be identified early to prevent serious complications such as hyperammonemia and liver failure. However, clinical diagnosis is often delayed due to its nonspecific presentation and limited sensitivity of amino acid-based newborn screening methods. Although genome-based evaluations are being investigated to address these issues, concerns about their cost, turnaround time, variant interpretation ability, and data handling highlight the need for a more practical yet reliable alternative. We investigated the feasibility of applying proteomic approach on dried blood spots (DBS), which are routinely used in newborn screening. Methods: We performed untargeted liquid chromatography-tandem mass spectrometry to analyze the proteome of DBS using a previously developed "non-targeted analysis of non-specifically DBS-absorbed proteins" (NANDA) workflow. SLC25A13 protein abundance was quantified in individuals with biallelic loss-of-function mutations, compound loss-of-function/missense mutations, and heterozygous carriers; this was also evaluated in healthy and diseased controls representing relevant differential diagnoses. To leverage proteomic information, we derived a multivariate proteomic signature using feature selection and evaluated its performance with leave-one-out cross-validation. Biological relevance was assessed by enrichment analysis, and complementary transcriptomics was performed using RNA sequencing. Results: A total of 7,474 proteins, including SLC25A13, were consistently detected in DBS. SLC25A13 was undetectable in individuals with biallelic loss-of-function mutations. However, individuals with compound loss-of-function/missense genotypes showed reduced but measurable SLC25A13 levels, comparable to those observed in heterozygous carriers. In contrast, a compact 15-protein signature accurately identified individuals with compound loss-of-function/missense genotypes (AUC, 0.99; sensitivity, 1.00; specificity, 0.95). The signature was enriched for Ca2+-response, and transcriptomics showed downregulation of genes related to multimodal ion channels in affected individuals compared to controls. Conclusions: DBS-based proteomic profiling may assist in the diagnosis of citrin deficiency through SLC25A13-quantification and a biologically plausible multivariate signature. More broadly, this strategy offers a promising new diagnostic layer for protein disorders, providing a proteomic readout in a clinically practical DBS format with potential utility for future diagnostic and screening applications.

Close to 50% of all bird species are reservoirs of potentially pathogenic fungi, including those listed as priority by the World Health Organization. In Malawi, data on diversity, pathogenic potential, and ecological avian sources of medically important yeast are scarce. A cross-sectional study using a descriptive approach was conducted in Blantyre, Southern Malawi, to characterise medically important yeasts recovered from environments contaminated with excreta/guano from synanthropic pigeons. A total of 20 samples were collected from 4 peri-urban areas, which yielded 71 yeast isolates. To assess the pathogenic potential of the environmental isolates, we compared their phenotypic virulence traits with those of 21 clinical yeast isolates collected from referral hospital laboratories. Pichia kudriavzevii (39%) and Candida orthopsilosis (30%) were the commonly isolated species in the pigeon-guano-contaminated environments. Candida parapsilosis sensu stricto (29%) and Candida albicans (24%) constituted most of the clinical yeast isolates. Half of the species isolated in the pigeon-guano-contaminated environments were also identified among the clinical isolates. A majority of the environmental isolates showed virulence traits similar to or stronger than clinical isolates. The findings underscore the critical need for integrated surveillance under the One Health framework, especially in bird-inhabited spaces close to human settlements.

Background: Inherited Retinal Diseases (IRDs) are a group of genetically heterogeneous blinding conditions. Major global genomic reference databases are disproportionately enriched for individuals of European ancestry. This underrepresentation creates a significant bias that impedes the accuracy of genetic diagnosis in the Chinese population. This study aims to address this limitation by constructing a comprehensive genetic landscape of IRDs using large-scale deep-sequencing data from a large Chinese cohort. Methods: The study leveraged variant data primarily from 10,588 individuals in the China Metabolic Analytics Project (ChinaMAP) and cross-referenced findings against multiple national and international databases. We systematically curated variants within a targeted panel of 291 IRD-associated genes. Variant pathogenicity was assessed using a comprehensive pipeline integrating InterVar-automated classification based on 2015 American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) guidelines, ClinVar evidence (review status [&ge;] 1 star), and manual literature curation. We delineated the mutational spectrum, identified population-enriched pathogenic/likely pathogenic (P/LP) variants, and analyzed the distribution characteristics of IRD-associated highly-mutated genes. Furthermore, we calculated the carrier frequencies (CF) and genetic prevalence (GP) of autosomal recessive(AR)-IRD genes in the Chinese population. Results: The study revealed a highly concentrated genetic landscape for AR-IRDs in the Chinese population, with ABCA4 and USH2A emerging as the primary drivers of the genetic burden. This finding aligns with previous Chinese cohorts but contrasts with global databases, where genes such as the X-linked RPGR are more prevalent. In contrast, autosomal dominant (AD)-IRDs exhibited high locus heterogeneity, with pathogenic variants dispersed across numerous genes (e.g., COL2A1 and MFN2). We identified a series of P/LP variants that were either high-frequency or significantly enriched in the Chinese population, such as CNGB1 (p.P530R) and specific recurrent alleles in ABCA4 and CYP4V2. The estimated cumulative CF for AR-IRDs was 1 in 5.60, and the theoretical total GP was 1 in 2,624.67, based on the ChinaMAP data. Conclusion: By integrating the ChinaMAP dataset with diverse genomic resources, this study provides a genetic landscape of IRDs in the Chinese population. Our analysis shows a concentrated mutational spectrum in AR-IRDs, contrasting with the pronounced heterogeneity in AD-IRDs. These findings, including population-specific pathogenic variants and refined prevalence estimates, provide a resource for precision diagnostics, genetic counseling, expanded carrier screening (ECS), and public health policy development in China.

Abstract Schizophrenia (SCZ) and bipolar disorder (BPD) are highly heritable psychiatric disorders with complex polygenic architectures. Genome-wide association studies (GWASs) have identified numerous common variant associations, but rarer variants detectable through whole-genome sequencing (WGS) remain underexplored. We conducted rare variant association analysis using WGS data from the Portuguese Island Collection (PIC), including 28 families with SCZ (n = 53) and 41 families with BPD (n = 83) cases, and population controls (n = 62). Following ANNOVAR and CADD annotation, burden analysis of deleterious variants showed that both affected and unaffected family members from SCZ and BPD pedigrees had significantly higher burdens of rare deleterious variants compared to controls (p < 0.0001), with no significant differences observed between affected and unaffected relatives, consistent with shared familial genetic liability. Polygenic Risk Score (PRS) analysis confirmed significant genetic contributions to both disorders within PIC. Association analyses were subsequently performed using SAIGE-GENE+ identifying 483 and 583 nominally significant (suggestive associations) gene sets (p-value [&le;] 0.05; FDR > 0.05) for SCZ and BPD, respectively, including gene sets related to neurotransmission, synaptic function and structure, neurodevelopment, and neuroinflammation as well as major signaling pathways. Cross disorder overlaps also identified shared suggestive enrichment of GABA and glutamate signaling, synaptic signaling, and Wnt signaling gene sets in both SCZ and BPD. These findings support shared rare variant burden within multiplex psychiatric families and highlight the role of gene-set based rare variant analysis in identifying neurobiological pathways relevant to SCZ and BPD. Keywords: WGS, Rare Variants, Schizophrenia, Bipolar Disorder

Objective: To identify Dickkopf-1 (DKK1) as a prognostically relevant candidate in head and neck squamous cell carcinoma and to evaluate whether DKK1 and cytoskeleton-associated protein 4 (CKAP4) expression is associated with cervical lymph node metastasis in tongue squamous cell carcinoma (TSCC). Methods: DKK1 was screened using the Human Protein Atlas Pathology Atlas. Immunohistochemical expression of DKK1 and CKAP4 was examined in 54 patients with primary TSCC (cT1-4N0) treated surgically between 2015 and 2020. Nine cases were excluded because of insufficient tissue blocks or inadequate staining quality, leaving 45 evaluable cases. Associations with delayed cervical lymph node metastasis were assessed together with conventional clinicopathological factors, including infiltrative growth pattern (INF) and pathological depth of invasion (pDOI). Results: In public database analysis, high DKK1 expression was associated with poorer overall survival in head and neck squamous cell carcinoma. In the TSCC cohort, pDOI [&ge;]5 mm and INF pattern c were significantly associated with cervical lymph node metastasis. Positive DKK1 and CKAP4 expression were also significantly associated with cervical lymph node metastasis. Furthermore, combined DKK1/CKAP4 positivity, when incorporated with INF and pDOI, provided additional risk stratification, and cases with all 3 factors showed a markedly increased likelihood of cervical lymph node metastasis. Conclusions: Expression of DKK1 and CKAP4 was associated with cervical lymph node metastasis in TSCC. Combined assessment of DKK1/CKAP4 expression with INF and pDOI may improve pathological risk stratification and may help identify patients who require closer neck evaluation and postoperative management.

Systematic Review (SR) is a prosperous study type in modern medicine and beyond. Many SR authors complement their primary database searches by supplementary techniques. Among these, citation-based techniques known as citation searching (CS) are widespread. Unranked Direct CS (UDCS) to identify directly cited and citing literature of seed references is currently most prevalent. Ranked (In)direct CS (RICS) additionally collects co-cited and co-citing literature combined with a ranking and cut-off procedure. However, RICS workflows remain non-standardized and tedious, and associated benefits unclear. This work aims to create a framework for the prospective international comparison of supplementary UDCS and RICS. To prime RICS research, we developed the open-source Co*Citation Network application and assessed parallel supplementary UDCS and RICS retrospectively in three completed SRs and prospectively in one case study. Automated RICS collected and ranked cited, citing, co-cited, and co-citing literature of seed references from OpenAlex database and applied an empirical rank cut-off to approximate the volume of UDCS results. In RICS compared to UDCS, we consistently noted higher overlap with primary database search results. Title/abstract screening in the case study showed a precision (number needed to read) of 1.8% (57) for UDCS and 2.1% (48) for RICS results. After full text screening, two additional articles were included for review, one of which was identified by UDCS and RICS, and one exclusively by UDCS. The present study indicates potential benefits of RICS for SR authors and will enable the formation of a research consortium to compare supplementary UDCS and RICS on larger scale.

Abnormalities in the gut microbiome, intestinal permeability, and the gut-immune-brain axis are increasingly linked to neuropsychiatric disorders, neurodegenerative disorders, inflammatory bowel disease (IBD), and other immunologic/autoimmune conditions. We investigated these phenomena in 128 youth with Pediatric Acute-Onset Neuropsychiatric Syndrome (PANS) and individuals with autism spectrum disorder (ASD) and other neurodevelopmental disorders (NDD) characterized by profound, unexplained deteriorations/regressions in developmental, neuropsychiatric, and behavioral functioning. Previous studies we have carried out showed that immune dysregulation and DNA damage response (DDR) gene mutations are implicated in a subset of these patients. The current study examines the role of genetic variants affecting intestinal homeostasis. We report a series of patients exhibiting both neuropsychiatric deterioration and gastrointestinal symptoms. Genetic analysis identified ultrarare (minor allele frequency < 0.001) pathogenic or likely pathogenic variants in eight genes primarily expressed in the intestines and associated with IBD, dysbiosis, or intestinal permeability. Across thirteen patients, mutations were identified in DUOX2 (n=4), SLC10A2 (n=2), UNC45A, TTC7A, LGALS4, SI, CCR9, MEP1B, and BACH2. While these findings suggest a potential role for genetic variants governing intestinal homeostasis in these cases of neuropsychiatric decline, their presence in only a small subgroup necessitates larger, prospective cohorts to determine whether these variants are statistically significant and play a definitive role in the pathogenesis of these disorders.

Background: Polygenic risk scores (PRS) for coronary artery disease (CAD) are associated with cardiovascular events, but the relationship between inherited risk and routinely reported coronary computed tomography angiography (CTA) findings has not been studied. Objectives: To evaluate associations between a genome-wide PRS for angiographic coronary disease burden and coronary CTA-derived measures of atherosclerotic severity in a real-world clinical cohort. Methods: We studied Penn Medicine BioBank participants with available genotypes and clinically obtained coronary CTA reports. A previously published PRS for angiographic CAD burden was calculated using pgsc_calc. CAD-RADS scores and coronary artery calcium (CAC) values were extracted from radiology reports using the large language model Llama 3.1 8B. Associations between PRS and CAD-RADS severity were evaluated using Bayesian cumulative ordinal logit regression, while associations with log-transformed CAC burden were assessed using Bayesian linear regression. Results: Among 630 participants, median age was 59 years (IQR 49 - 68), 53% were female, 62% were genetically similar to a European reference population, and 34% to an African reference population. LLM-extracted CAD-RADS and CAC values demonstrated near-perfect agreement with manual abstraction. Higher PRS was associated with greater coronary atherosclerotic burden on CTA. Each 1-standard deviation (SD) increase in PRS was associated with a 20% higher odds of belonging to a more severe CAD-RADS category (cumulative OR 1.20, 95% credible interval 1.06-1.44). Higher PRS was also associated with greater CAC burden ({beta} 0.38, 95% credible interval 0.15 - 0.61). Conclusions: Polygenic risk for angiographic coronary disease burden is reflected in clinically reported coronary CTA severity measures, including CAD-RADS and CAC. These findings demonstrate that inherited susceptibility to CAD manifests as greater anatomic atherosclerotic burden at the time of clinical presentation and support further investigation of genetic risk integration into imaging-based cardiovascular risk assessment.

In the United States, sickle cell disease (SCD) is a rare inherited hemoglobinopathy affecting about 100,000 individuals, mostly with African ancestry. SCD causes damage to multiple organ systems and SCD nephropathy (SCDN) is a common complication associated with early mortality. We previously performed a genome-wide association study (GWAS) for SCDN and identified a modest number of genome-wide significant loci. Here, we leveraged the ancestral composition of participants from two well-characterized adult SCD cohorts to boost statistical power and perform a local ancestry-aware GWAS for estimated glomerular filtration rate (eGFR), resulting in the identification of novel genome-wide significant loci within the African (AFR) and European (EUR) ancestral components of participants. Meta-analysis identified 12 significant genomic regions in the AFR tract, including PPIL6, ARHGAP24, RAB11A, and STEAP3, and 38 regions in the EUR tract, including UBLCP1, ADAMTS6, JAZF1, MYO7B, MYO1C, PDGFA, GPC5, LRP1B, KANK1, and TRPV5. The identified regions encompass genes affecting inflammation, extracellular matrix (ECM) integrity, iron metabolism, magnesium ion homeostasis, B cell apoptosis, tumor necrosis factor (TNF) production, and estrogen signaling. Many of these genes and pathways are important not only for renal function, but also for SCD biology, providing additional support for the hypothesis that SCDN pathophysiology is unique from other forms of kidney disease. This study represents the largest local ancestry-aware analysis of SCDN to date, furthers our understanding of the genetic risk factors underlying SCDN, and proposes new targets that could be useful for the early identification and treatment of kidney dysfunction in SCD patients.

Quantifying uncertainty in polygenic score (PGS)-based phenotype prediction is crucial for the integration of genomic data into precision medicine. While the PGS provides a fundamental pivot for point estimation, clinical decision-making necessitates the construction of well-calibrated prediction intervals that reliably encompass the true phenotypic values. However, phenotypic residuals are frequently characterized by complex heteroscedasticity and stratified variance structures across diverse demographic contexts. Existing approaches often rely on global calibration mechanisms, which fail to account for such localized variance structures and lead to systematic miscalibration within specific subpopulations. To bridge this gap, we propose Clustering-based Split Conformal Prediction with Normalized Residuals (C-SCNR), a versatile framework based on Split Conformal Prediction. By adopting residual normalization and incorporating a repetitive `split-and-cluster` mechanism, C-SCNR dynamically identifies latent error strata and applies fine-grained adjustments to the resulting intervals. Our framework requires no distributional assumptions regarding the phenotype, is compatible with any PGS method, and flexibly accommodates biologically-informed grouping. Simulation studies demonstrate that our framework consistently outperforms existing methods across diverse error distributions. In real-data applications analyzing Body mass index (BMI), Low-density lipoprotein (LDL) cholesterol, and High-density lipoprotein (HDL) cholesterol in the UK Biobank, C-SCNR effectively resolves the coverage deficiencies of existing methods in specific subgroups and consistently yields superior localized calibration. Overall, C-SCNR represents a flexible and powerful framework for constructing high-resolution context-specific prediction intervals, thereby facilitating more reliable clinical interpretations of polygenic risk.

Background: Thalamic low-intensity transcranial focused ultrasound (tFUS) has shown promise for increasing behavioral responsiveness in disorders of consciousness (DOC), but no study has examined whether it can causally modulate the well-validated behavioral, electrophysiological, and metabolic biomarkers of DOC impairment. Methods: Sixteen adult patients (44% Female; Age, M=37.81, SD=15.97) with a chronic DOC (Time Since Injury, M=3.39, SD=1.94 years) secondary to severe brain injury (TBI 44%, non-TBI 56%) underwent a 10-day inpatient, longitudinal, single-arm, open-label protocol. tFUS was delivered in a single session targeting the left central thalamus. Well-known behavioral (CRS-R), electrophysiological (EEG {delta}/{beta} ratio), metabolic (18F-FDG PET), and polysomnographic outcomes were assessed at baseline and after sonication. Results: The maximum CRS-R total score increased significantly following tFUS compared to baseline (M=13.27 vs. M=10.33; t(14)=7.407, p<0.001, d=1.913), as did the global EEG {delta}/{beta} ratio (N=14; W=17, p=0.025, r=0.68), with the degree of frontal slowing positively predicting behavioral gains ({tau}b=0.51, p=0.016). Glucose metabolism decreased bilaterally in thalamus and frontal, temporal, and parietal cortices at both post-tFUS timepoints compared to baseline. Finally, N2 sleep increased by 33% following tFUS (N=11; t(10)=2.386, p=0.038, d=0.72), though this did not survive correction. No severe adverse events were observed. Conclusion: Thalamic tFUS can causally modulate well-validated behavioral, electrophysiological, and metabolic biomarkers of DOC. The convergent inhibitory signature across these measures suggests a thalamocortical reset mechanism, complementing existing excitatory neuromodulation approaches and providing the mechanistic foundation for a large, randomized sham-controlled trial.

Biological aging is heterogeneous across organ systems, yet whether CT-derived abdominal aging provides prognostic value beyond routine clinical data and whether organ decomposition adds beyond a unified estimate remains untested. We developed and evaluated organ-specific and ensemble biological age models from radiomic features across five abdominal organs in 68,675 CT scans from 32,883 subjects, evaluated on alignment with chronological age of healthy subjects (nested cross validation: MAE=3.68 years, R^2=0.90). In sequential analyses restricted to adults aged 20-60 years which is the stratum of strongest BAG-disease association, ensemble biological age gaps provided incremental prognostic value beyond demographic covariates for all-cause disease and mortality (Delta C-index=0.141, 0.051) and beyond routine blood biomarkers (Delta C-index=0.048), confirming CT-derived aging captures structural information beyond laboratory markers. Organ-specific biological age added incremental prognostic value beyond ensemble selectively for focal diseases: cardiovascular (aorta, Delta C-index=0.091) and hepato-pancreatic (pancreas, Delta C-index=0.096). These findings establish a hierarchical organization of CT-derived biological aging, positioning routine CT as a source that adds prognostic value to existing clinical biomarkers.

Background: Frontotemporal dementia (FTD) lacks widely accessible disease-specific biomarkers. Optical coherence tomography (OCT) and OCT angiography (OCTA) may provide non-invasive measures of retinal changes associated with neurodegeneration. We conducted a systematic review and meta-analysis evaluating retinal biomarkers in FTD compared with Alzheimer disease (AD) and controls. Methods: A systematic search of PubMed and Embase was conducted through April 25, 2026 according to PRISMA guidelines. Studies evaluating OCT/OCTA biomarkers in FTD with comparator groups were included. Inverse weighted random-effects models, publication bias assessments, and meta-regressions were performed. Results: Ten studies involving 139 individuals with FTD, 87 with AD, 29 with mild cognitive impairment, 14 with TDP-43 proteinopathy, 5 with tauopathy, and 255 controls were included in the systematic review; five studies were eligible for meta-analysis. Compared with AD, individuals with FTD demonstrated significantly thinner retinal nerve fiber layer (RNFL) thickness (SMD = -0.61, 95% CI -0.98, -0.24). Compared with controls, individuals with FTD exhibited significantly thinner ganglion cell layer-inner plexiform layer (GCL-IPL) thickness (SMD = -0.55, 95% CI -1.02, -0.08), whereas pooled analyses across multiple retinal biomarkers were non-significant (SMD = -0.19, 95% CI -0.52, 0.14). RNFL thickness correlated negatively with female % in FTD and positively with age in both AD and controls. Conclusions: Individuals with FTD exhibit lower RNFL thickness than AD and lower GCL-IPL thickness than controls, suggesting retinal alterations may reflect neurodegeneration. However, larger longitudinal studies with standardized OCT/OCTA protocols are needed to determine the diagnostic and prognostic utility of retinal biomarkers in FTD

Hybrid mechanistic-statistical models offer interpretability and adaptability for short-term seasonal epidemic forecasting, but it remains unclear whether their accuracy depends more on increased biological complexity or on the assimilation of richer data. Using eight retrospective influenza seasons in North Carolina, we evaluate whether training on historical data and assimilating auxiliary emergency department (ED) visit data improves four-week-ahead hospital admission forecasts more than adding biological complexity (multi-subtype structure and cross-season immunity). Hierarchical Bayesian training on historical data improves accuracy by 22.4 % (95 % CI: 16.4-28.1 %), and inclusion of ED visit data yields a further 5.3 % (95 % CI: 3.0-7.6 %) improvement, whereas added biological complexity produces diminishing or null gains. We further observe a substitution effect in which ED visit data partially compensates for omitted biological structure. We deployed a simplified model variant in the 2025-2026 CDC FluSight Challenge and ranked among the top ensemble performers, supporting the robustness of Bayesian hierarchical training in real time. Together, these findings indicate that short-term forecast accuracy is driven more by historical learning and assimilating auxiliary signals than by biological fidelity, with implications for how forecasting systems should balance mechanistic complexity.

Background Artificial intelligence-enhanced electrocardiography (AI-ECG) enables scalable, low-cost cardiac dysfunction screening, but existing models are annotation-intensive and predominantly adult-derived, leaving paediatric generalizability uncertain. Paediatric cohorts exhibit highly variable cardiac morphology and function compared to adults, which may be useful for learning generalizable AI-ECG models. Methods We pretrained ECG-Fyler on a predominantly paediatric, all-age cohort at Boston Children's Hospital (1992-2023), annotated with a cardiology-specific coding system (Fyler codes), and evaluated it on assessments from echocardiography (echo) and cardiac magnetic resonance (CMR) studies. We validated on an external adult cohort from Columbia University Irving Medical Center. Performance was benchmarked against several AI-ECG foundation models by AUROC across age groups, lesion types, and limited-data scenarios. Findings The pretraining cohort comprised 782,138 ECGs from 255,271 patients (median age: 10.9 years, IQR: [2.8-16.8]). Internal evaluation included 178,495 ECG-echo pairs (median age: 10.9 [3.7-17.0]) and 8,584 ECG-CMR pairs (median age: 20.7 [15.6-29.6]). External validation included 82,543 ECG-echo pairs from adults (median age: 64.0 [52.0-74.0]). ECG-Fyler improved AUROC across biventricular dysfunction and dilation tasks, with the largest gains in low-data settings. In internal validation, ECG-Fyler detected low left ventricular ejection fraction (LVEF [&le;] 40%) from only 100 fine-tuning samples (AUROC: 0.80, 95% CI: [0.78-0.80]), outperforming other models (AUROC < 0.65) and improving with additional fine-tuning (AUROC: 0.94 [0.93-0.94]). Similar improvements were observed for CMR-derived LVEF, RVEF, and ventricular dilation. In external validation on adults, ECG-Fyler exhibited an AUROC of 0.83 (CI: [0.82-0.85]) for LVEF [&le;] 40%. After fine-tuning on less than 10% of external data, LVEF [&le;] 45% performance (AUROC: 0.87 [0.86-0.88]) outperformed a fully trained, site-specific prior model (AUROC: 0.85 [0.84-0.87]). Interpretation Pretraining on richly annotated, paediatric-dominant ECGs yields models that transfer efficiently across institutions and ages, supporting AI-ECG screening and triage when labels or imaging access are limited. Funding National Institutes of Health (R01LM012973); Kostin Innovation Fund, Boston Children's Hospital