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Effect of genetically predicted sclerostin on cardiovascular biomarkers, risk factors, and disease outcomes.
Sclerostin inhibitors protect against osteoporotic fractures, but their cardiovascular safety remains unclear. We conducted a cis-Mendelian randomisation analysis to estimate the causal effect of sclerostin levels on cardiovascular risk factors. We meta-analysed three GWAS of sclerostin levels including 49,568 Europeans and selected 2 SNPs to be used as instruments. We included heel bone mineral density and hip fracture risk as positive control outcomes. Public GWAS and UK Biobank patient-level data were used for the study outcomes, which include cardiovascular events, risk factors, and biomarkers. Lower sclerostin levels were associated with higher bone mineral density and 85% reduction in hip fracture risk. However, genetically predicted lower sclerostin levels led to 25-85% excess coronary artery disease risk, 40% to 60% increased risk of type 2 diabetes, and worse cardiovascular biomarkers values, including higher triglycerides, and decreased HDL cholesterol levels. Results also suggest a potential (but borderline) association with increased risk of myocardial infarction. Our study provides genetic evidence of a causal relationship between reduced levels of sclerostin and improved bone health and fracture protection, but increased risk of cardiovascular events and risk factors.
Genome-wide association studies of COVID-19 vaccine seroconversion and breakthrough outcomes in UK Biobank.
Understanding the genetic basis of COVID-19 vaccine seroconversion is crucial to study the role of genetics on vaccine effectiveness. In our study, we used UK Biobank data to find the genetic determinants of COVID-19 vaccine-induced seropositivity and breakthrough infections. We conducted four genome-wide association studies among vaccinated participants for COVID-19 vaccine seroconversion and breakthrough susceptibility and severity. Our findings confirmed a link between the HLA region and seroconversion after the first and second doses. Additionally, we identified 10 genomic regions associated with breakthrough infection (SLC6A20, ST6GAL1, MUC16, FUT6, MXI1, MUC4, HMGN2P18-KRTCAP2, NFKBIZ and APOC1), and one with breakthrough severity (APOE). No significant evidence of genetic colocalisation was found between those traits. Our study highlights the roles of individual genetic make-up in the varied antibody responses to COVID-19 vaccines and provides insights into the potential mechanisms behind breakthrough infections occurred even after the vaccination.
Weaving and loom terminology in Japhug
We describe the yarn preparation and weaving tradition of the Japhug people of Sichuan Province, China, speakers of a Rgyalrongic language belonging to the Sino-Tibetan family. We discuss the practical aspects of the loom and its operation, as well as the terms used to describe it. Our analysis shows that the terms are mostly a mixture of autochthonous Rgyalrongic and borrowings from Tibetan. The loom is a version of the frameless body-tensioned loom, an ancient and widespread type in East Asia and Southeast Asia, used by many Sino-Tibetan language speakers. We provide a simple guide for researchers interested in recording weaving traditions in the field, and we briefly discuss the implications of our findings for the study of the languages and ethnography of the Sino-Tibetan peoples generally.
The role and uses of antibodies in COVID-19 infections: a living review.
Coronavirus disease 2019 has generated a rapidly evolving field of research, with the global scientific community striving for solutions to the current pandemic. Characterizing humoral responses towards SARS-CoV-2, as well as closely related strains, will help determine whether antibodies are central to infection control, and aid the design of therapeutics and vaccine candidates. This review outlines the major aspects of SARS-CoV-2-specific antibody research to date, with a focus on the various prophylactic and therapeutic uses of antibodies to alleviate disease in addition to the potential of cross-reactive therapies and the implications of long-term immunity.
Mucosal immune responses in COVID19 - a living review.
COVID-19 was initially characterized as a disease primarily of the lungs, but it is becoming increasingly clear that the SARS-CoV2 virus is able to infect many organs and cause a broad pathological response. The primary infection site is likely to be a mucosal surface, mainly the lungs or the intestine, where epithelial cells can be infected with virus. Although it is clear that virus within the lungs can cause severe pathology, driven by an exaggerated immune response, infection within the intestine generally seems to cause minor or no symptoms. In this review, we compare the disease processes between the lungs and gastrointestinal tract, and what might drive these different responses. As the microbiome is a key part of mucosal barrier sites, we also consider the effect that microbial species may play on infection and the subsequent immune responses. Because of difficulties obtaining tissue samples, there are currently few studies focused on the local mucosal response rather than the systemic response, but understanding the local immune response will become increasingly important for understanding the mechanisms of disease in order to develop better treatments.
Association of COVID-19 With Risk of Posttransplant Diabetes Mellitus
Background. Posttransplant diabetes mellitus (PTDM) is an important complication for solid organ transplant recipients (SOTRs). COVID-19 has been associated with an increased risk of incident diabetes in the general population. However, the association between COVID-19 and new-onset PTDM has not been explored. Methods. Using the National COVID Cohort Collaborative Enclave, we conducted a cohort study of adults without diabetes receiving a solid organ transplant (heart, lung, kidney, or liver) in the United States between April 1, 2020, and March 31, 2023, with and without a first diagnosis of COVID-19 (COVID+ versus COVID-) within 180 d of SOT. We propensity score matched a single COVID+ SOTR with a COVID- SOTR who was diabetes free at the same point posttransplant. Within this matched cohort, we used multivariable Cox proportional hazards models to examine the adjusted risk of PTDM associated with COVID+. Results. Among 1342 COVID+ SOTRs matched to 1342 COVID- SOTRs, the crude rate of newly diagnosed PTDM in the 2 y post-COVID was 17% in those with versus 13% in those without COVID-19 (P = 0.007). COVID-19 was significantly associated with new PTDM (adjusted hazard ratio, 1.37; 95% confidence interval, 1.12-1.68 at 2 y). Conclusions. Similar to other viral infections, COVID-19 is associated with an increased risk of PTDM in SOTRs.
Navigating severe class imbalance in population cohort data
Class imbalance is a major challenge in predictive modelling for rare disease outcomes, particularly in large-scale population cohorts. Traditional machine learning models often struggle with imbalanced datasets, leading to biased performance metrics and poor generalisability. This study systematically evaluates multiple approaches to mitigate class imbalance in predicting Multiple myeloma using proteomic and clinical data from UK Biobank. We compare standard classification models (XGBoost and logistic regression) with synthetic resampling (SMOTE), anomaly detection techniques (isolation forests, local outlier factors, one-class SVM, and autoencoders), and a transformer-based foundation model (TabPFN), using standard classification performance metrics. Our results indicate that anomaly detection models generalise better than conventional classifiers (XGBoost and logistic regression), while SMOTE fails to improve, and may actively worsen, predictive performance. To address the precision-sensitivity trade-off, we introduce a sequential XGBoost ensemble classifier (SeqXGB) that prioritises high precision over sensitivity to minimise false positive predictions. Compared with a single XGBoost model, the SeqXGB approach successfully reduces false positives (420 vs 9), but significantly limits sensitivity (0.70 vs 0.15) in held-out test data. Our findings highlight that no single method is universally optimal for addressing class imbalance; rather, model selection should be guided by clinical application, balancing the risks of false positives and false negatives.
Panpipes: a pipeline for multiomic single-cell and spatial transcriptomic data analysis.
Single-cell multiomic analysis of the epigenome, transcriptome, and proteome allows for comprehensive characterization of the molecular circuitry that underpins cell identity and state. However, the holistic interpretation of such datasets presents a challenge given a paucity of approaches for systematic, joint evaluation of different modalities. Here, we present Panpipes, a set of computational workflows designed to automate multimodal single-cell and spatial transcriptomic analyses by incorporating widely-used Python-based tools to perform quality control, preprocessing, integration, clustering, and reference mapping at scale. Panpipes allows reliable and customizable analysis and evaluation of individual and integrated modalities, thereby empowering decision-making before downstream investigations.
Harnessing transcriptomic signals for amyotrophic lateral sclerosis to identify novel drugs and enhance risk prediction.
INTRODUCTION: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease. This study integrates common genetic association results from the latest ALS genome-wide association study (GWAS) summary statistics with functional genomic annotations with the aim of providing mechanistic insights into ALS risk loci, inferring drug repurposing opportunities, and enhancing prediction of ALS risk and clinical characteristics. METHODS: Genes associated with ALS were identified using GWAS summary statistic methodology including SuSiE SNP-based fine-mapping, and transcriptome- and proteome-wide association study (TWAS/PWAS) analyses. Using several approaches, gene associations were integrated with the DrugTargetor drug-gene interaction database to identify drugs that could be repurposed for the treatment of ALS. Furthermore, ALS gene associations from TWAS were combined with observed blood expression in two external ALS case-control datasets to calculate polytranscriptomic scores and evaluate their utility for prediction of ALS risk and clinical characteristics, including site of onset, age at onset, and survival. RESULTS: SNP-based fine-mapping, TWAS and PWAS identified 118 genes associated with ALS, with TWAS and PWAS providing novel mechanistic insights. Drug repurposing analyses identified six drugs significantly enriched for interactions with ALS associated genes, though directionality could not be determined. Additionally, drug class enrichment analysis showed gene signatures linked to calcium channel blockers may reduce ALS risk, whereas antiepileptic drugs may increase ALS risk. Across the two observed expression target samples, ALS polytranscriptomic scores significantly predicted ALS risk (R 2 = 5.1 %; p-value = 3.2 × 10-27) and clinical characteristics. CONCLUSIONS: Functionally-informed analyses of ALS GWAS summary statistics identified novel mechanistic insights into ALS aetiology, highlighted several therapeutic research avenues, and enabled statistically significant prediction of ALS risk.
Fine needle aspiration of human lymph nodes reveals cell populations and soluble interactors pivotal to immunological priming.
Lymph node (LN) fine needle aspiration (LN FNA) represents a powerful technique for minimally invasive sampling of human LNs in vivo and has been used effectively to directly study aspects of the human germinal center response. However, systematic deep phenotyping of the cellular populations and cell-free proteins recovered by LN FNA has not been performed. Thus, we studied human cervical LN FNAs as a proof-of-concept and used single-cell RNA-sequencing and proteomic analysis to benchmark this compartment, define the purity of LN FNA material, and facilitate future studies in this immunologically pivotal environment. Our data provide evidence that LN FNAs contain bone-fide LN-resident innate immune populations, with minimal contamination of blood material. Examination of these populations reveals unique biology not predictable from equivalent blood-derived populations. LN FNA supernatants represent a specific source of lymph- and lymph node-derived proteins, and can, aided by transcriptomics, identify likely receptor-ligand interactions. This represents the first description of the types and abundance of immune cell populations and cell-free proteins that can be efficiently studied by LN FNA. These findings are of broad utility for understanding LN physiology in health and disease, including infectious or autoimmune perturbations, and in the case of cervical nodes, neuroscience.
Digital health interventions in primary care in low- and middle-income countries: a systematic scoping review protocol
Background The integration of digital health (eHealth) interventions into primary healthcare systems has gained recognition lately in Low-and Middle-Income Countries (LMICs) to enhance healthcare quality, accessibility, and efficiency. These interventions may offer effective strategies in mitigating the burden of chronic diseases by facilitating access to remote healthcare and optimising its processes. This scoping review aims to identify and assess eHealth interventions implemented in primary care settings in LMICs for further development and adaptation. Methods and analysis We will search two electronic databases, such as Scopus and Embase, to identify peer-reviewed studies reporting on eHealth interventions implemented in primary care settings within LMICs. This review will encompass evidence published in the English language without a time frame restriction. We will remove duplicates from the search, and two reviewers will independently assess all articles for eligibility by first screening the title and abstract, followed by a full-text review. Eligible articles will be extracted, and data will be charted according to types of intervention and settings using a standardised form. Ethics and dissemination There is no ethical review required for this scoping review. We plan to disseminate the findings by presentations at conferences and publishing in open-access journal.
Global estimates and determinants of antituberculosis drug pharmacokinetics in children and adolescents: a systematic review and individual patient data meta-analysis.
BACKGROUND: Suboptimal exposure to antituberculosis (anti-TB) drugs has been associated with unfavourable treatment outcomes. We aimed to investigate estimates and determinants of first-line anti-TB drug pharmacokinetics in children and adolescents at a global level. METHODS: We systematically searched MEDLINE, Embase and Web of Science (1990-2021) for pharmacokinetic studies of first-line anti-TB drugs in children and adolescents. Individual patient data were obtained from authors of eligible studies. Summary estimates of total/extrapolated area under the plasma concentration-time curve from 0 to 24 h post-dose (AUC0-24) and peak plasma concentration (C max) were assessed with random-effects models, normalised with current World Health Organization-recommended paediatric doses. Determinants of AUC0-24 and C max were assessed with linear mixed-effects models. RESULTS: Of 55 eligible studies, individual patient data were available for 39 (71%), including 1628 participants from 12 countries. Geometric means of steady-state AUC0-24 were summarised for isoniazid (18.7 (95% CI 15.5-22.6) h·mg·L-1), rifampicin (34.4 (95% CI 29.4-40.3) h·mg·L-1), pyrazinamide (375.0 (95% CI 339.9-413.7) h·mg·L-1) and ethambutol (8.0 (95% CI 6.4-10.0) h·mg·L-1). Our multivariate models indicated that younger age (especially <2 years) and HIV-positive status were associated with lower AUC0-24 for all first-line anti-TB drugs, while severe malnutrition was associated with lower AUC0-24 for isoniazid and pyrazinamide. N-acetyltransferase 2 rapid acetylators had lower isoniazid AUC0-24 and slow acetylators had higher isoniazid AUC0-24 than intermediate acetylators. Determinants of C max were generally similar to those for AUC0-24. CONCLUSIONS: This study provides the most comprehensive estimates of plasma exposures to first-line anti-TB drugs in children and adolescents. Key determinants of drug exposures were identified. These may be relevant for population-specific dose adjustment or individualised therapeutic drug monitoring.
Harnessing Transcriptomic Signals for Amyotrophic Lateral Sclerosis to Identify Novel Drugs and Enhance Risk Prediction.
INTRODUCTION: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease. This study integrates the latest ALS genome-wide association study (GWAS) summary statistics with functional genomic annotations with the aim of providing mechanistic insights into ALS risk loci, inferring drug repurposing opportunities, and enhancing prediction of ALS risk and clinical characteristics. METHODS: Genes associated with ALS were identified using GWAS summary statistic methodology including SuSiE SNP-based fine-mapping, and transcriptome- and proteome-wide association study (TWAS/PWAS) analyses. Using several approaches, gene associations were integrated with the DrugTargetor drug-gene interaction database to identify drugs that could be repurposed for the treatment of ALS. Furthermore, ALS gene associations from TWAS were combined with observed blood expression in two external ALS case-control datasets to calculate polytranscriptomic scores and evaluate their utility for prediction of ALS risk and clinical characteristics, including site of onset, age at onset, and survival. RESULTS: SNP-based fine-mapping, TWAS and PWAS identified 117 genes associated with ALS, with TWAS and PWAS providing novel mechanistic insights. Drug repurposing analyses identified five drugs significantly enriched for interactions with ALS associated genes, with directional analyses highlighting α-glucosidase inhibitors may exacerbate ALS pathology. Additionally, drug class enrichment analysis showed calcium channel blockers may reduce ALS risk. Across the two observed expression target samples, ALS polytranscriptomic scores significantly predicted ALS risk (R2 = 4%; p-value = 2.1×10-21). CONCLUSIONS: Functionally-informed analyses of ALS GWAS summary statistics identified novel mechanistic insights into ALS aetiology, highlighted several therapeutic research avenues, and enabled statistically significant prediction of ALS risk.