Advanced Science

Gait variability is a critical functional indicator of dynamic balance and neurocognitive decline in health. Its translation into clinical practice is, however, challenged by a lack of age-related normative trajectories and reference values under real-world ecological settings. Furthermore, the conventional metrics used to estimate gait variability (Coefficient of Variation, CV; Standard Deviation, SD) have a fundamental methodological flaw: the inherent sensitivity of conventional metrics to the statistical outliers and environmental noise in real-world walking. In this study, we mitigate this factor by applying a robust statistical framework to quantify gait variability. Analysing a large-scale cohort of community-dwelling older adults (n=2,193), we first demonstrate that free-living gait data follows a heavy-tailed distribution, necessitating the use of robust estimators like the Robust Coefficient of Variation (RCV-MAD) and Median Absolute Deviation (MAD). Leveraging these metrics, we established the normative trajectory and reference values of real-world gait variability across the ageing lifespan, revealing a distinct, age-dependent increase in spatio-temporal fluctuations, indicating a decline in rhythmicity and steadiness with age. We further demonstrated the clinical utility of these robust metrics: RCV-MAD consistently yielded larger effect sizes than conventional CV in discriminating between fallers and non-fallers across all gait parameters. Furthermore, we illustrate the potential of long-term unsupervised monitoring to capture intrinsic variability during real-world walking. Validated for consistency and reliability, this robust framework provides the necessary ecological validity to transform gait variability into a standardised, rapid clinical metric for assessing functional decline at an early timepoint.

Background: Platform trials are an efficient trial design which enable testing of multiple interventions simultaneously. They could advance knowledge of treatments for intracerebral haemorrhage (ICH). We aimed to investigate the views of clinicians involved in stroke research on recruitment to a future platform trial for ICH. Methods: Between April and July 2025, we conducted a UK-wide online survey of clinicians actively involved in stroke research using convenience sampling through professional organisations. Participants considered factors related to the consent process and research environment and could provide optional free text responses about additional barriers or facilitators to recruitment. We used descriptive statistics for quantitative data and content analysis for qualitative data. Results: Among 73 respondents, 46 (63%) were female, 36 (50%) were stroke physicians, 24 (34%) nurses, 6 (8%) allied health professionals, and 7 (10%) were in other roles. 36 (49%) had >20 years of clinical experience, 45 (61%) reported spending <10% of their role in research. 66 (91%) thought that a platform trial would be a good option for testing interventions for patients with stroke due to ICH. Across 11 modifiable factors, clinicians most frequently rated perceived importance of the research question as a facilitator of recruitment (94%), while clinician preference for specific treatments was most frequently rated as a barrier (48%). Two themes emerged from free text responses: study design and infrastructure. Regarding study design respondents perceived consent procedures (n=9), study materials (n=8), study procedures (n=8), eligibility assessment (n=6), the research question (n=3) and randomization (n=3) as important for a future platform trial. Regarding infrastructure, emergent factors were staffing (n=17), local research culture and capacity (n=9), research governance and delivery (n=6), and training (n=6). Conclusion: The overwhelming majority of respondents from the UK clinical stroke community supported a platform trial for ICH, although the influence of survey responder bias is unknown.

Abstract Background: Conventional MRI cannot reliably distinguish radiation necrosis (RN) from recurrent metastasis after cranial radiotherapy, as both can show similar enhancement despite different biology. We tested whether these entities are mechanically non-equivalent in vivo and separable by MRE-derived viscoelastic metrics and perilesional interface-instability features. Methods: In a prospective, histopathology-anchored cohort, 11 post-radiotherapy enhancing lesions were classified as RN (n=3) or recurrent/progressive tumor (n=8). MRE was acquired at 3.0 T with single-frequency 60-Hz excitation to derive storage modulus (G'), loss modulus (G''), and complex shear modulus magnitude (|G*|). Co-primary endpoints were median tumor G' and |G*|, each tested one-sided (RN > tumor) with Holm correction across the two co-primary tests. Median tumor G'' was tested two-sided. A prespecified secondary 6-endpoint family (absolute and tumor/NAWM-normalized G', G'', and |G*|) was analyzed with Benjamini-Hochberg FDR control. Exploratory instability mapping in a 0- 6 mm peritumoral shell generated interface-topology metrics, including convexity. Results: Absolute tumor-core medians were higher in RN than tumor for |G*| (1.79 vs 1.32 kPa; Cliff's {delta} = 0.67; q = 0.10), G' (1.62 vs 1.09 kPa; {delta} = 0.50; q = 0.14), and G'' (0.81 vs 0.46 kPa; {delta} = 0.75; q = 0.10). NAWM normalization improved separation: tumor/NAWM |G*| (2.26 vs 1.41; {delta} = 0.92; q = 0.04) and tumor/NAWM G'' (2.67 vs 0.87; {delta} = 1.00; q = 0.04) were FDR-significant. Convexity also differentiated RN from tumor (0.49 vs 0.36; {delta} = 1.00; MWU p = 0.01). Conclusions: Tumor/NAWM G'', tumor/NAWM |G*|, convexity, and tumor G'' emerged as the strongest candidate features, indicating that RN is mechanically harder and more dissipative than recurrent metastasis. Signal strength was high (Cliff's {delta} up to 1.00) but should be interpreted cautiously given sample size. Exploratory analyses further suggest that instability mapping captures biologically relevant interface behavior. These findings support a mechanics-based RN-versus-recurrence framework and justify prespecified, preregistered external validation.

A critical challenge in endocrine neurosurgery is intraoperative discrimination between normal pituitary tissue and pituitary neuroendocrine tumors (PitNETs). Suggesting the universal persistence of near-infrared autofluorescence (NIRAF) in endocrine organs and inspired by routine clinical use of NIRAF for parathyroid gland identification, we discovered that pituitary NIRAF can be employed for label-free transsphenoidal surgery guidance. Ex vivo confocal spectral imaging of 33 specimens identified secretory granules as the dominant long-wavelength fluorescence source and showed that normal pituitary had higher granule content than PitNETs. For the first time, we made use of the pituitary NIRAF during surgery and assessed its performance for pituitary/adenoma separation in vivo for 27 surgeries and showed near-perfect separability between pituitary and non-pituitary measurement sites with ROC-AUC of 0.98. The obtained results clearly demonstrate that the suggested method, based on the solid microscopic background, has the potential for clinical translation and paves the way for enhanced gland preservation during resection.

We propose a lesion-centric phenotype learning pipeline for interpretable breast ultrasound (BUS). Predicted lesion masks are used for mask-weighted pooling of segmentation-encoder latents, producing compact embeddings that suppress background influence; a lightweight calibration step improves cross-dataset consistency. We cluster embeddings to discover latent phenotypes and relate phenotype structure to morphology descriptors (compactness, boundary sharpness). On BUSI and BUS-UCLM with external testing on BUS-BRA, lesion-centric pooling and calibration improve separability and enable strong malignancy probing (AUC 0.982), outperforming radiomics and a standard CNN baseline. A simple rule-gated generator further improves BI-RADS-style descriptor consistency on difficult cases.

Background: Quantitative genome wide association studies (GWAS) primarily rely on additive linear models that compare average phenotypic differences between genotype groups. While effective for detecting common variants of moderate effect in large sample sizes, such approaches inherently reduce high resolution phenotypic data to summary statistics (group averages), potentially limiting the detection of subtle genotype phenotype relationships. Genomic Informational Field Theory (GIFT) is a recently developed methodology that preserves the fine-grained informational structure of quantitative traits by analysing ranked phenotypic configurations rather than relying solely on mean differences. Methods: We applied GIFT to genetic and neuropathological data from the Brains for Dementia Research cohort, a well characterised dataset of 563 individuals, and compared its performance with conventional GWAS. Principal component analysis (PCA) derived matrix was used to derive independent quantitative traits linked to from Alzheimer disease (AD) neuropathology measures (CERAD, Thal, Braak staging), with and without inclusion of age at death. Principal component analyses were performed using GWAS and GIFT frameworks on the same filtered genotype dataset. Results: Both GWAS and GIFT identified genome-wide significant associations (pvalue<0.000001) within the APOE locus (NECTIN2/TOMM40/APOE/APOC1), demonstrating concordance with established AD genetic variants. However, GIFT detected additional significant 19 SNPs beyond those identified by GWAS. Variants associated with AD pathology implicated genes involved in amyloid processing, neuronal apoptosis, synaptic function, neuroinflammation, and metabolic regulation. Notably, GIFT identified 29 loci associated with age at death related variation that were not detected by GWAS, highlighting genes linked to lipophagy, mitochondrial quality control, sphingolipid metabolism, frailty, and aging-related processes. Conclusions: GIFT recapitulates canonical GWAS findings while uncovering additional biologically relevant associations. By preserving the fine-grained structure of phenotypic data distributions and detecting non random genotype segregation across ranked trait values, GIFT enables the identification of associations that remained undetected by traditional average based GWAS approaches. These results demonstrate that rethinking analytical representation, rather than solely increasing sample size, can expand discovery potential of genetic association studies, offering a transparent and complementary framework for quantitative genomics in deeply phenotyped datasets.

Protocadherin-12 (PCDH12), a cell-adhesion protein belonging to the non-clustered protocadherin family, plays a crucial role in the establishment and regulation of neuronal connections and communication. Bi-allelic loss-of-function (LoF) variants in the PCDH12 gene have been associated with several neurodevelopmental disorders (NDDs) such as diencephalic-mesencephalic junction dysplasia (DMJD) syndrome, cerebral palsy, and cerebellar ataxia, often accompanied by ocular abnormalities. However, genotypes exhibit variable expressivity. Affected individuals sharing the same PCDH12 variant presenting differing phenotypic severities have posed major challenges towards identification of the underlying pathogenic mechanisms. Here, we report three affected individuals from two families, each harbouring non-truncating pathogenic missense variants in PCDH12. The patients are compound heterozygous, with each individual carrying one extracellular [c.1742T>G (p.Val581Gly) and c.1861_2del/insCA (p.Ile621His)] and one intracellular variant [c.3370C>T (p.Arg1124Cys) and c.3445G>A (p.Asp1149Asn] on each allele. The children present with a range of phenotypes similar to those associated with LoF variants. One child exhibited microcephaly and seizures, while the two siblings displayed developmental delays and severe behavioral disorders. All three children experienced some degree of visual impairment. The missense variants provided new insights into the neurodevelopmental consequences of compromised PCDH12 function by distinguishing the specific consequences associated with dysfunction in the extracellular versus intracellular domains of PCDH12. All identified missense variants are predicted to be deleterious and destabilizing. The expression of PCDH12 in HEK293T and HeLa cells demonstrated that PCDH12 is expressed effectively, regardless of the presence of missense variants. However, the extracellular variants p.Val581Gly and p.Ile621His compromised the stability of PCDH12's homophilic adhesion. Additionally, we found evidence of an interaction between PCDH12 and the extracellular domain of the epilepsy-associated PCDH19 protein. PCDH12 extracellular missense variants also negatively impact this interaction. Our study provides evidence that PCDH12 mediates both homophilic and heterophilic interactions. Our findings also highlight the importance of stable PCDH12-mediated adhesion, emphasizing the need to further study the functional consequences of PCDH12 missense variants on brain and visual system development.

Wearable devices present transformative opportunities for personalized healthcare through continuous monitoring of digital biomarkers; however, individual variations in device wear time could mask or otherwise impact signal identification. Despite the widespread adoption of wearable devices in research, no comprehensive framework exists for understanding how wear time varies across populations or for addressing wear time-related biases in analysis. Using Fitbit data from 11,901 participants in the All of Us Research Program, we conducted the first large-scale systematic assessment of wearable device wear time across demographics, social determinants of health, lifestyle factors, mental health symptoms, and disease. Our findings revealed that wear time was higher among males and increased with age, income, and education, but decreased with depressive, anxiety, and anhedonia symptoms, with reductions more pronounced following clinical diagnoses compared to symptom-based classifications. Individuals with chronic conditions displayed differential levels of wear time compared to healthy controls. Critically, we demonstrate that the widely used [&ge;]10-hour daily compliance threshold, while appropriate for some research contexts, can disproportionately exclude days of data from disease populations: among individuals with major depressive disorder, 74.4% of data days were excluded compared to 20.9% for controls. We propose a flexible methodological framework including standard compliance thresholds, wear time covariate adjustment, metric normalization, propensity score matching, and adaptive thresholds that can be applied individually or in combination to optimize wearable data retention across diverse research contexts. These findings establish wear time as a critical methodological consideration for wearable device research and provide guidance for advancing equitable and rigorous digital health analytics.

Wastewater monitoring enables non-invasive, population-scale tracking of community infections independent of healthcare-seeking behavior and clinical diagnosis. Metagenomic sequencing extends this capability by enabling broad, pathogen-agnostic detection, genomic characterization, and identification of novel or unexpected threats. Here, we present data from CASPER (the Coalition for Agnostic Sequencing of Pathogens from Environmental Reservoirs), a U.S.-based wastewater metagenomic sequencing network designed for deep, untargeted pathogen monitoring at national scale. This release includes 1,206 samples collected between December 2023 and December 2025 from 27 sites across nine states, covering 13 million people. Deep sequencing (~1 billion read pairs per sample) generated 1.2 trillion read pairs (347 terabases), enabling detection of even rare taxa, with CASPER representing 66% of all untargeted wastewater sequencing data currently available on the NCBI Sequence Read Archive. Virus abundance trends correlate with nationwide wastewater PCR and clinical data for SARS-CoV-2, influenza A, and respiratory syncytial virus, while the pathogen-agnostic approach captures emerging threats, including avian influenza H5N1 during initial dairy cattle outbreaks, West Nile virus, and measles, among hundreds of viral taxa. As the largest publicly available untargeted wastewater sequencing dataset to date, CASPER provides a shared and growing resource for pathogen surveillance and microbial ecology.

Background Short-chain fatty acids (SCFAs) are widely used as functional readouts of gut microbial activity in vivo. The growing adoption of decentralised study designs and self-collection protocols has amplified the need for reliable room-temperature storage and shipment strategies. However, SCFAs volatility and the persistence of post-collection microbial metabolism raise concerns regarding pre-analytical stability and the interpretability of measured concentrations. Methods We assessed the temporal stability of fatty acids (FAs) across intestinal and systemic matrices under room-temperature storage. Untreated stool was compared with two nucleic acid stabilisation devices (eNAT and OMNIgene-GUT), while whole blood, plasma and dried blood spots (DBS) were evaluated as minimally invasive systemic sampling strategies. Profiles were quantified using complementary GC-MS and LC-MS/MS workflows. Results Untreated stool showed fermentation-driven increases in major SCFAs, whereas immediate freezing preserved baseline profiles. eNAT maintained faecal FA stability for up to 21 days, while OMNIgene-GUT exhibited baseline and time-dependent alterations. In systemic matrices, plasma and whole blood showed upward drift, whereas DBS declined initially before stabilising after approximately 14 days. Conclusions FA measurements are highly matrix- and device-dependent. Our findings provide practical guidance for the selection of sampling strategies in microbiome-associated FA studies and emphasise the need for controlled pre-analytical conditions in decentralised microbiome studies.

Purpose: Human anatomy remains foundational to clinical practice, yet reduced instructional hours raise concerns about graduate competence and preparedness for patient care. Although trainees often report confidence, supervisors may perceive deficiencies, creating a gap between self-assessment and external evaluation. This study examined stakeholder perspectives on anatomical competence within physical therapy education to identify areas of discordance in perceived capability. Methods: A cross-sectional web-based survey collected responses from 165 stakeholders associated with an entry-level Doctor of Physical Therapy program featuring a 16-week dissection curriculum. Participants rated four domains of anatomical competence using a 5-point ordinal scale. Group differences were analyzed with the Kruskal-Wallis test appropriate for ordinal data. This methodology ensured robust assessment of stakeholder perceptions and comparative analysis. Results: Median ratings of preparedness and capability were 4 of 5 (quite prepared). Significant discordance emerged in three domains: recent graduates rated their foundational knowledge and ability to explain complex concepts to lay audiences higher than faculty or clinical instructors, whereas faculty expressed lower confidence in graduates' ability to explain patient symptoms using anatomical principles. No significant differences were observed in the ability to describe structures by location, suggesting shared perceptions of basic anatomical understanding despite variation in applied reasoning. Conclusions: Stakeholders generally viewed graduates as well prepared, yet disagreement persisted regarding clinical application of anatomical knowledge. Faculty skepticism about symptom explanation indicates that mastery of anatomy alone does not guarantee clinical reasoning. Curricular strategies emphasizing vertical integration and explicit connections between anatomical science and patient-centered reasoning may help bridge perception gaps and enhance professional competence.

Drug-induced liver injury (DILI) is an acute inflammatory liver disease caused not only by prescription and over-the-counter medications but also by health foods and dietary supplements. Typically, DILI patients recover once the causative substance is identified and discontinued. In contrast, autoimmune hepatitis (AIH) results from the immune-mediated destruction of hepatocytes due to a breakdown of self-tolerance mechanisms. Patients presenting with acute-onset AIH often lack characteristic clinical features, such as autoantibodies, and require prompt steroid treatment to prevent progression to liver failure. Liver biopsy currently remains the gold standard to differentiate acute DILI from AIH; however, general pathologists face significant diagnostic challenges due to overlapping histopathological features. This study integrates pathology expertise with deep learning-based artificial intelligence (AI) to differentiate DILI from AIH using histopathological images. Our AI model demonstrates promising classification accuracy (Accuracy 74%, AUC 0.81). This paper presents a detailed pathological analysis alongside AI methods, discusses the current model performance and limitations, and proposes directions for future improvements.

Background Artificial intelligence (AI) is increasingly being integrated into healthcare systems, with growing applications in clinical decision support, workflow optimization, and population health management. While substantial investments have been made in digital infrastructure, the successful adoption of AI in primary care depends critically on the readiness, awareness, and educational preparedness of healthcare professionals. Global health authorities emphasize the need for ethically grounded and workforce-focused approaches to AI integration; however, evidence on clinicians readiness for AI, particularly in primary care settings and in the Middle East region, remains limited. Objectives This study aims to assess the level of awareness, perceptions, attitudes, and educational needs related to AI among healthcare professionals working within Qatars Primary Health Care Corporation (PHCC). In addition, it seeks to examine organizational factors influencing the integration of AI-focused education in primary care and to develop an AI readiness framework that can inform targeted training strategies and policy planning. Methods This study will adopt a mixed-methods design guided by the Organizational Readiness for Change (ORC) framework, adapted for AI integration in primary care. The quantitative component will consist of an anonymous, census-style online survey distributed to all healthcare professionals across PHCC health centers and headquarters, assessing AI awareness, attitudes, training needs, and perceived infrastructure readiness. Composite AI awareness and attitude scores will be calculated, and regression analyses will be used to explore factors associated with AI readiness. The qualitative component will include semi-structured interviews and focus group discussions using maximum variation sampling to capture diverse professional perspectives. Qualitative data will be analyzed thematically, following COREQ and SRQR reporting standards. Quantitative and qualitative findings will be integrated to generate an AI readiness profile and an actionable education roadmap aligned with national digital health priorities. Discussion This study will provide the first comprehensive assessment of AI readiness among primary care healthcare professionals in Qatar. By identifying knowledge gaps, training priorities, and organizational enablers and barriers, the findings are expected to inform the development of evidence-based AI education strategies within continuing professional development frameworks. The proposed AI readiness framework may also offer a transferable model for other health systems seeking to align workforce development with responsible AI implementation in primary care.

Background Older adults' walking has so far been evaluated using standardised assessments of walking capacity within a clinical setting. By taking the evaluation out of the laboratory into the real world, this study provides first evidence of the ability of Digital Mobility Outcomes (DMOs) to detect changes over time and the Minimal Important Difference (MID) in patients after proximal femoral fracture (PFF). This will guide the implementation of DMOs in research and clinical care. Methods For this multicenter prospective cohort study, 381 community-dwelling older adults were included within one year after sustaining a PFF and assessed at two time points, separated by six months. Walking activity and gait DMOs were measured using a single wearable device worn on the lower back for up to seven days. A global impression of change question and three mobility-related outcome measures (Late-Life Function and Disability Instrument; Short Physical Performance Battery; 4m gait speed) were used as anchor variables. To assess each DMOs ability to detect changes, we calculated the standardized mean change as effect size. For estimating MIDs, both distribution-based and anchor-based methods were applied, followed by triangulation by experts if at least three anchor-based estimates were available per DMO, resulting in single-point estimates. Results All three anchor variables demonstrated substantial changes. Overall, 10 out of 24 available DMOs showed large and 7 DMOs moderate positive effects in the expected direction of the respective anchors. Seven DMOs showed no or only small effects. For 12 DMOs, at least three anchor-based estimates were available, enabling MID triangulation. MIDs for walking activity DMOs per day were: a walking duration of 10 minutes, a step count of 1,000 steps, 50 walking bouts (WB), and 15 WBs in WBs over 10 seconds. For gait DMOs, depending on the walking bout length, MIDs for walking speed were between 0.04 m/s and 0.08 m/s, and MIDs for cadence between 4 and 6 steps/minute. Almost all DMOs showed a strong ability to detect improvement in mobility, but rarely in detecting decline. Conclusions For the first time, MIDs are presented for real-world DMOs in PFF patients. These MIDs inform sample size requirements and interpretation of intervention effects for clinical trials, thereby providing guidance and reassurance for clinicians and regulatory bodies.

Wastewater-based epidemiology (WBE) is an effective surveillance approach for monitoring viruses of public health relevance at the community level, complementing clinical surveillance systems. Molecular methods such as PCR/qPCR are widely used for targeted detection, while next-generation sequencing (NGS) with targeted enrichment panels has emerged as a complementary strategy for broader viral detection and genomic characterization. This study comparatively evaluated conventional PCR/qPCR and a targeted enrichment whole-genome sequencing Viral Surveillance Panel (VSP, Illumina) for virus detection in wastewater. Fifty-six wastewater samples collected between 2017 and 2023 from a wastewater treatment plant in Cordoba, Argentina, were concentrated by polyethylene glycol precipitation and pooled by season and year, reaching a total of 14 pools. Each pool was analyzed in parallel by PCR/qPCR for eight human viruses of public health importance and by the VSP, targeting 66 viral species, sequenced on a NovaSeq 6000 platform, and analyzed with the DRAGEN pipeline. Detection frequencies for each virus using PCR/qPCR and VSP were: RoV A 100%/14.3%; NoV 100%/14.3%; AiV 50%/42.9%; SARS-CoV-2 14.3%/0%; HAV 42.9%/0%; HEV 14.3%/0%; JCPyV 35.7%/85.7%; BKPyV 28.6%/71.4%, respectively. In addition, VSP detected the genomes of Astrovirus (71.4%), Salivirus (21.4%), Coxsackie A (14.3%), Rotavirus C (14.3%), and Merkel Cell virus (7.1%), and enable the recovery of 16 near complete genomes (coverage > 92.5%) of AiV, JCPyV, BKPyV, Salivirus and Astrovirus. PCR/qPCR and targeted enrichment NGS provide complementary information wastewater viral surveillance. Their combined application improves virus detection and genomic characterization, reinforcing the value of integrated approaches in environmental virology and public health monitoring.

Introduction Heat waves are increasingly frequent and linked to higher mortality risks in Hong Kong. However, estimates of total excess mortality associated with heat waves remain unavailable. This study quantifies excess deaths associated with heat waves in Hong Kong from 2014 to 2023. Methods Daily age- and sex-specific mortality rates and population data were obtained from the Hong Kong Life Tables and Census and Statistics Department. Temperature data came from the Hong Kong Observatory, and relative risks were derived from local research. A Monte Carlo simulation was used to estimate heat-attributable deaths under different heat wave definitions, calculating total excess deaths and annualized death rates per 100,000 population. Results Between 2014 and 2023, heat exposure resulted in an estimated 1,455 (95% CI: 1,098-1,812) to 3,238 (95% CI: 3,234-3,242) excess deaths. In 2023, annualized excess death rates ranged from 2.95 (95% CI: 2.41-3.50) to 5.09 (95% CI: 5.07-5.12) per 100,000 people. Males and individuals aged 65 or older were disproportionately affected. Conclusion Over the 10-year study period, 1,455 to 3,238 excess deaths in Hong Kong were attributed to extreme heat. Heat waves now rank among the top ten causes of death in Hong Kong, with mortality rates comparable to diabetes. These findings underscore the need for urgent public health interventions to mitigate the impact of extreme heat.

Study question: How is glycodelin, a glycoprotein secreted by reproductive tissues, causally related to reproductive diseases and traits? Summary answer: We present evidence for a causal role of sex hormones in determining glycodelin levels, but limited evidence that glycodelin subsequently causally impacts reproductive traits. What is known already: Glycodelin is expressed in female and male reproductive tissues and has four glycoforms (-A, -C, -F and -S), with the glycosylation pattern determining its function. Differences in the levels of glycodelin are associated with reproductive traits, including fertility, endometriosis, preeclampsia, and female-specific malignancies. Study design, size, duration: We used cross-sectional data from the UK Biobank to investigate relationships between glycodelin and reproductive-related traits in men and women by performing genome-wide association studies (GWAS) and Mendelian randomization (MR) analyses. Participants/materials, setting, methods: We included individuals of European genetic ancestry aged 40-69 in 2006-2010, with genetic data in the UK Biobank v3 release. We performed GWAS of glycodelin levels in 46,468 people, stratified by sex (21,368 men and 25,100 women) and menopause status (6,409 pre- and 18,691 post-menopausal women). We tested bidirectional casual associations between glycodelin levels and 19 reproductive-related traits using one- and two-sample MR analyses. Main results and the role of chance: Nine genetic signals reached genome-wide significance (P<5x10-8) across the glycodelin phenotypes. A known genetic signal (rs9409964) near the PAEP gene, which encodes glycodelin, was most strongly associated (P<3x10-80 across all phenotypes), and had heterogeneous effects (effect (SD) per A allele of 1.31 in men vs 0.60 in women, and 0.4 in pre- vs 0.9 in post-menopausal women). Higher serum concentrations of bioavailable testosterone raised glycodelin in men (effect = 0.14 SD, IVW P=4.1x10-13), while effects in women depended on menopause status (pre-menopausal effect = -0.16 SD, IVW P=3.6x10-3; post-menopausal effect = 0.10 SD, IVW P=5.9x10-4). There was no strong evidence that differences in glycodelin levels were caused by, or were the cause of, other reproductive-related traits. Limitations, reasons for caution: Proteomic measurements of glycodelin did not differentiate between glycoforms and were derived from blood and might not reflect levels in reproductive tissues. The sample size for the pre-menopausal GWAS was modest, reducing our power to detect relationships with reproductive conditions. Genetic instruments are assumed to be proxies for average lifelong exposure, which does not reflect variation in hormones and biomarkers over lifetime. Wider implications of the findings: We suggest that reported associations of glycodelin with reproductive conditions are likely to result from the effects of sex hormones rather than being directly causal. These findings may help reconcile previously conflicting associations between glycodelin and reproductive traits.

Background Whole-genome sequencing (WGS) has improved the diagnosis of rare genetic disorders, yet interpretation of non-coding variants that affect splicing remains challenging. In silico predictions alone are insufficient, and short-read RNA sequencing may fail to capture complex or low-abundance splicing events. Targeted amplicon-based long-read RNA sequencing (Amp-LRS) offers a cost-effective approach for functional validation of candidate splice-altering variants. Methods We applied Amp-LRS to five patients with neurological disorders (central nervous system, peripheral nervous system, or muscle) harbouring candidate non-coding variants predicted to alter splicing. RNA was extracted from fibroblasts or peripheral blood, and full-length transcript amplicons were sequenced using Oxford Nanopore Technologies. Nonsense-mediated decay (NMD) inhibition was performed on fibroblast cultures using cycloheximide. Results Amp-LRS validated all five candidate variants, including intronic and UTR variants in POLR3A, OPA1, PYROXD1, GDAP1, and SPG11. Aberrant splicing events included exon skipping, intron retention, cryptic splice site activation, and pseudoexon inclusion, often resulting in frameshifts and premature termination codons. For POLR3A and OPA1, multiple abnormal isoforms arose from single variants, highlighting the complexity of splicing disruption. Some pathogenic effects were detectable only in a minority of reads and variably enriched by NMD inhibition, consistent with being hypomorphic. The approach was successfully applied using accessible tissues and enabled multiplexed sequencing at low per-sample cost. Conclusions Amp-LRS is a sensitive, versatile, and cost-effective method for functional assessment of non-coding splice-altering variants identified by WGS. By enabling full-length transcript analysis from accessible tissues, this approach improves interpretation of variants of uncertain significance and could enhance molecular diagnosis in rare neurological diseases.

Acute kidney injury (AKI) and chronic kidney disease (CKD) are two interconnected clinical conditions, both defined by degree of functional impairment, but with heterogeneous clinical trajectories. Using new transcriptomic technologies, recent studies have described the cellular diversity in the healthy and injured kidney at the single cell level. Here, we used single nucleus transcriptomics to investigate the molecular diversity and commonalities in kidney biopsies from over 150 participants with AKI and CKD enrolled within the Kidney Precision Medicine Project (KPMP), and did so at the patient participant level. Using an unsupervised approach, we identified two multi-cellular programs associated with clinical and histopathological features of acute injury and chronic damage, respectively. We found that these programs are expressed across patients with AKI and CKD, supporting shared, rather than distinct, underlying molecular mechanisms. These programs capture tissue-level compositional changes towards adaptive and failed-repair states in tubular epithelial cells, as well as intra-cellular molecular changes characteristic of stress in all cell types. We identified subunits of the NFkB and AP-1 complexes, as well as members of the STAT family, as putative upstream regulators of the acute and chronic programs. We were able to link these continuous molecular measures of acute injury and chronic damage with urine and plasma protein profiles obtained at time of biopsy. These non-invasive protein signatures were predictive of renal outcomes in an independent cohort of 44 thousand participants from the UK biobank. In summary, unbiased identification of cellular programs in kidney disease biopsies defined molecular programs of injury cutting across conventional disease categorisation and established a non-invasive molecular link to long term patient outcomes.

Resting-state electroencephalography (EEG) has been proposed as a scalable source of biomarkers for chronic pain, but its clinical potential remains uncertain. To systematically evaluate this potential, we benchmarked nine modeling strategies, spanning conventional machine learning with handcrafted features to state-of-the-art deep learning. Across 72 configurations of signal representations and model architectures, we trained models to predict self-reported pain intensity, using chronological age decoding as a positive control. Pain prediction performance was limited (R=0.15), with the best results achieved by conventional connectivity-based models. In contrast, age was robustly decoded from the same dataset (R=0.53), confirming technical efficacy. These findings indicate that resting-state EEG contains limited information about inter-individual differences in chronic pain intensity, making it unlikely to yield clinically actionable biomarkers in cross-sectional settings. Instead, its potential may lie in intra-individual modeling of pain dynamics, which could advance individualized mechanistic insights and more personalized treatment of chronic pain.