medRxiv : the preprint server for health sciences最新文献

Detecting low variant allele fraction (VAF) mosaic variants without matching controls remains a major challenge in genomics, limited by technical noise, lack of benchmarks, and computational scalability. We present the DRAGEN mosaic caller, a hardware-accelerated approach identifying variants down to ∼1-2% VAF with low false-positive rates and hour-scale runtimes for mosaic SNV/indel detection from bulk sequencing. To support evaluation, we introduce a genome-wide low-VAF benchmark for variants between 1-10% VAF. Application to blood, sperm, and brain tissues revealed patterns, including mosaic hotspots and mutational signatures. The first analysis of HG002 blood showed that many "mosaic" variants defined from HG002 cell lines are likely culture-derived and not in vivo mutations. Importantly, DRAGEN also enables personalized assembly pangenome references to improve alignment and mosaic variant detection in complex regions. Together, this development makes routine low-VAF discovery feasible, opening new opportunities to study mosaic mutations in healthy and disease individuals.

Introduction: Mortality and smoking rates vary over time across the US. The Cancer Intervention and Surveillance Modeling Network-Lung Working Group (CISNET-LWG) has developed a smoking history generator to describe the effects smoking on health. This work further refines these parameters and quantifies effects on life expectancy.

Methods: Data from the National Health Interview Survey (NHIS) and the Tobacco Use Supplement to the Current Population Survey (TUS-CPS) were used to estimate smoking history parameters for each state. The age-period-cohort was used in most cases, but an age-cohort mode was used for cessation probabilities. Population mortality data were used to estimate mortality rates for all causes, lung cancer, and non-lung cancer. These were partitioned by smoking status.

Results: California and Kentucky are states with more or less aggressive tobacco control. The difference between population cohort life expectancy and life expectancy of never smoker was greater for males than for females, and it was greater in Kentucky than California because of higher smoking rates. These differences decreased with time. Similar result are shown for each state.

Conclusions: Variation in smoking parameters and mortality trends vary considerably among states. These show variation in exposure to tobacco smoking and their effects on life expectancy. The Southeast region tends to have greater differences from never smokers because of higher smoking rates. However, there are also other factors affecting mortality rates.

Purpose: To create a publicly available, interactive data platform to visualize various data measures reflecting Colorado and its residents to support research and outreach efforts, specifically focusing on cancer burden and disparities throughout the state. This platform, named ECCO (Exploring Cancer in Colorado), aims to integrate diverse public data sources into a unified, user-friendly interface, accessible to researchers, community members, and outreach programs alike.

Methods: A multi-disciplinary team developed ECCO, leveraging public data sources like Cancer InFocus, State Cancer Profiles, and the Colorado Department of Public Health and Environment. The platform's architecture employs a three-tiered web application model, utilizing a PostgreSQL database, a backend API built with FastAPI, and a Vue 3 frontend with an Open Layers map. Data is organized geographically at the county and/or census tract levels, categorized into measure categories (e.g., socio-demographics, cancer risk factors), and further filterable by demographic characteristics. An automated Extract-Transform-Load (ETL) data pipeline ensures regular updates of the data.

Results: The platform visualizes data such as socio-demographics, cancer risk factors, screening adherence, and cancer incidence and mortality rates. Additionally, ECCO incorporates location-specific data for cancer care facilities, health services, environmental exposures, and political boundaries. To date, ECCO has had 1.1K unique visitors and over 19K pageviews according to Google Analytics.

Conclusion: The ECCO platform provides a valuable tool for understanding and addressing cancer disparities in Colorado. By integrating diverse data sources and offering interactive visualization, ECCO enhances the ability of researchers, community members, and outreach programs to identify populations at risk, inform interventions, and support research priorities.

Availability: The application and code are available at https://coe-ecco.org/ and https://github.com/colorado-cancer-center/ecco .

Content summary: Key Objective: This work sought to develop ECCO (Exploring Cancer in Colorado), an interactive, easily-accessible data platform designed to visualize and understand diverse cancer-related data measures reflective of Colorado and its residents.Knowledge generated: ECCO integrates public data from sources like Cancer InFocus, State Cancer Profiles, and the Colorado Department of Public Health and Environment, visualizing measures such as socio-demographics, cancer risk factors, screening adherence, and cancer incidence and mortality rates at both county and census tract levels. The platform also incorporates location-specific data on cancer care facilities, health services, environmental exposures, and political boundaries.

Background: Evidence on how social and school-level contexts shape pediatric vision screening outcomes is limited, particularly in sub-Saharan Africa. We examined the association between individual and contextual factors and vision screening outcomes in a pilot enhanced school-based vision screening program (ESVSP) in Kumasi, Ghana.

Methods: We conducted a cross-sectional study using data from an ESVSP to detect eye disorders in school-children aged 4 to 22 years. Outcomes were the presence of eye disorders and referral adherence. Exposure variables were individual [(age, sex, socioeconomic status (SES)], and contextual [school type (public vs private)] factors. Logistic regression was used to estimate unadjusted (OR) and adjusted (aOR) odds ratios with 95% confidence intervals (CI).

Results: We analyzed data for 1,123 children screened and 299 referred. The average age was 10.2 (±2.6) years. Overall, 34% (n=382) had suspected eye disorders, and 32.8% (n=98) adhered to the referral. After adjusting for key variables, children attending public (32.2%) compared to private (67.8%) schools had 45% lower odds of identified eye disorders (aOR= 0.55; 95% CI 0.37, 0.83). Children with low (13.3%) compared to high (28.6%) SES had 70% lower odds of referral adherence (aOR= 0.30; 95% CI 0.12, 0.80).

Conclusion: In this pilot school-based program, school context and socioeconomic status were associated with suspected pediatric vision and eye disorders, and referral adherence, respectively. These findings highlight equity-relevant gaps in referral adherence and underscore the need for context-specific strategies to strengthen referral pathways in low-resource settings.

Older individuals frequently harbor multiple brain pathologies, including Alzheimer's disease (AD) related amyloid-β (Aβ) and tau alongside α-synucleinopathy and vascular pathology. Proteomic profiling offers a strategy to better understand common as well as unique features of these different brain pathologies. We analyzed cerebrospinal fluid (CSF) (n=1,658) and plasma (n=749) samples from participants in the BioFINDER cohorts using the automated NULISAseq CNS Disease panel of 125 proteins. Differentially abundant proteins (DAPs) related to AD pathology (based on Aβ- and tau-PET positivity), α-synuclein (based on synuclein amplification assay [SAA] positivity) and vascular pathology (based on white matter lesion [WML] load) were identified with linear models simultaneously including a binary measure for the three pathologies. In the BioFINDER-2 subcohorts, DAPs were further evaluated for associations with continuous baseline (n=1,137) and longitudinal (n=656) Aβ-PET, tau-PET, and WML measures in models accounting for all pathologies. Associations with AD-signature cortical atrophy (n=915) and cognitive decline by the MMSE (n=1054) were also examined. We identified 84 CSF DAPs, with largely distinct protein signatures for each pathology (AD, n=66 DAPs; vascular pathology, n=55; α-synuclein pathology, n=16). 10 DAPs (e.g., FABP3, UCHL1, NPTXR, NPTX2) were altered across all three pathologies, reflecting general neurodegeneration. AD-associated DAPs included glial/inflammatory markers (CHIT1, CX3CL1, CD63) linked to Aβ pathology, and synaptic/neuronal injury markers (VSNL1, NRGN, NEFL) and metabolic enzymes (FABP3, MDH1) linked to tau pathology. Aβ-associated proteomic differences were most evident in CU individuals, while tau-associated differences predominated in MCI. More proteins, particularly neurodegeneration and synaptic markers, were associated with tau change than with Aβ change. Vascular pathology exhibited a distinct profile, enriched for inflammatory, angiogenic and extracellular matrix proteins (PGF, POSTN, TREM1, VCAM1). DDC was the main protein associated with α-synucleinopathy. Only a few proteins, including UCHL1, NPTX2, and NEFL, predicted cognitive decline and cortical atrophy after accounting for all brain pathologies. In plasma, although fewer DAPs were identified (n=20), findings included established AD biomarkers. Only plasma VCAM1 and NEFL were associated with α-synuclein and vascular pathology. NULISA identified stage-dependent, disease-specific CSF biomarker signatures with limited overlap, alongside shared neurodegenerative markers, supporting improved biological interpretation and more refined classification of neurodegenerative pathology.

Background: Blood-based biomarkers offer a scalable alternative to cerebrospinal fluid and PET imaging for Alzheimer's disease (AD) detection, yet traditional venipuncture limits participation among rural and socioeconomically disadvantaged populations. Self-collection using the Tasso+ capillary device could reduce access barriers, but its feasibility and validity for AD plasma biomarkers remain uncertain, particularly with real-world delays prior to processing.

Methods: Adults aged 45-90 years from the Wisconsin SHOW cohort who were underrepresented in AD research (Black or Hispanic race/ethnicity, rural residence, or

Results: Tasso+ collection was successful for 96% of participants; 64% rated it very easy and 86% reported comfort/no pain, yet 57% preferred future venipuncture-particularly Black, lower-income, and lower-education participants. Agreement varied markedly by biomarker. GFAP and NfL demonstrated excellent concordance (CCC 0.97-0.98) with minimal bias (-6% to -8%). Aβ40 and Aβ42 showed modest correlations (r=0.40-0.47) and substantial underestimation (-60% to -70%). Aβ42/40 and pTau217 exhibited poor correlation and extreme positive bias for pTau217 (∼+2600%). Hemolysis was more frequent in Tasso+ samples and contributed to disagreement for several markers; processing lag and sample volume were not strong predictors.

Conclusions: Remote capillary self-collection with a 24-hour delay is suitable for measuring GFAP and NfL but not currently reliable for Aβ or pTau217 without improved handling (e.g., temperature control, hemolysis reduction). Although user experience was favorable, trust and logistical concerns limited preference among underrepresented groups. Community-informed strategies and optimized pre-analytics are essential before deploying Tasso+ in large AD studies.

As cognitive decline progresses, older adults may self-regulate their driving. Avoidance of left turns across traffic is observable in naturalistic driving data but rarely self-reported. We studied 106 older adults using baseline and one-year follow-up neuropsychological assessments. In-vehicle sensors passively recorded driving behavior over 12 weeks. We identified 295,112 turns from vehicle heading changes. We used mixed-effects logistic regression to model the odds of turning left, with cognitive status category change from baseline to one-year follow-up as the predictor. Greater cognitive impairment, represented by movement to a more severe cognitive status category at one-year follow-up, was associated with reduced odds of turning left (odds ratio = 0.984, 95% confidence interval = 0.969-0.999; P value = .037). Left-turn avoidance may be a behavioral marker of early cognitive decline. Passive driving data could help detect functional changes, enabling intervention to preserve mobility and independence. Further research is needed to establish a clinical threshold of concern for decreasing trends in left turn frequency in older drivers.