medRxiv : the preprint server for health sciences最新文献

Background: In a cluster-randomized controlled trial, the "Migrants' Approached Self-Learning Intervention in HIV/AIDS for Tajiks" (MASLIHAT) reduced intervention participants' sexual risk behaviour including any condomless sex, condomless sex with female sex workers, and multiple sexual partners. This analysis investigates if observed changes in sexual risk behaviors translated into fewer reported STIs among participants over 12-month follow-up.

Methods: The MASLIHAT intervention was tested in a cluster-randomized controlled trial with sites assigned to either the MASLIHAT intervention or comparison health education training (TANSIHAT). Participants and network members (n=420) were interviewed at baseline and 3-month intervals for one year to assess HIV/STI sex and drug risk behaviour. We conducted mixed effects robust Poisson regression analyses to test for differences between conditions in self-reported STIs during 12 months of follow-up, and to test the contribution of sexual risk behaviours to STI acquisition. We then tested the mediating effects of sexual behaviours during the first six months following the intervention on STIs reported at the 9 and 12-month follow-up interviews.

Results: Participants in the MASLIHAT condition were significantly less likely to report an STI during follow-up (IRR=0.27, 95% CI 0.13-0.58). Condomless sex with a non-main (casual or commercial) partner was significantly associated with STI acquisition (IRR=2.30, 95% CI 1.26-4.21). Adjusting for condomless sex with a non-main partner, the effect of MASLIHAT intervention participation was reduced (IRR=0.36, 95% CI 0.16-0.80), signalling possible mediation. Causal mediation analysis indicated that the intervention's effect on reported STI was partially mediated by reductions among MASLIHAT participants in condomless sex with a non-main partner.

Conclusions: The MASLIHAT peer-education intervention reduced reported STIs among Tajik labour migrants partly through reduced condomless sex with casual and commercial partners.

Clinical trial registration: ClinicalTrials.gov , 2021-04-16, NCT04853394 .

The exponential growth of genomic datasets necessitates advanced analytical tools to effectively identify genetic loci from large-scale high throughput sequencing data. This study presents Deep-Block, a multi-stage deep learning framework that incorporates biological knowledge into its AI architecture to identify genetic regions as significantly associated with Alzheimer's disease (AD). The framework employs a three-stage approach: (1) genome segmentation based on linkage disequilibrium (LD) patterns, (2) selection of relevant LD blocks using sparse attention mechanisms, and (3) application of TabNet and Random Forest algorithms to quantify single nucleotide polymorphism (SNP) feature importance, thereby identifying genetic factors contributing to AD risk. The Deep-Block was applied to a large-scale whole genome sequencing (WGS) dataset from the Alzheimer's Disease Sequencing Project (ADSP), comprising 7,416 non-Hispanic white participants (3,150 cognitively normal older adults (CN), 4,266 AD). 30,218 LD blocks were identified and then ranked based on their relevance with Alzheimer's disease. Subsequently, the Deep-Block identified novel SNPs within the top 1,500 LD blocks and confirmed previously known variants, including APOE rs429358 and rs769449. Expression Quantitative Trait Loci (eQTL) analysis across 13 brain regions provided functional evidence for the identified variants. The results were cross-validated against established AD-associated loci from the European Alzheimer's and Dementia Biobank (EADB) and the GWAS catalog. The Deep-Block framework effectively processes large-scale high throughput sequencing data while preserving SNP interactions during dimensionality reduction, minimizing bias and information loss. The framework's findings are supported by tissue-specific eQTL evidence across brain regions, indicating the functional relevance of the identified variants. Additionally, the Deep-Block approach has identified both known and novel genetic variants, enhancing our understanding of the genetic architecture and demonstrating its potential for application in large-scale sequencing studies. Keywords: Alzheimer's disease, Whole-Genome Sequencing, Linkage Disequilibrium, Deep Learning, Genetic Loci, Imputation Methods.

Integrating clinical knowledge into AI remains challenging despite numerous medical guidelines and vocabularies. Medical codes, central to healthcare systems, often reflect operational patterns shaped by geographic factors, national policies, insurance frameworks, and physician practices rather than the precise representation of clinical knowledge. This disconnect hampers AI in representing clinical relationships, raising concerns about bias, transparency, and generalizability. Here, we developed a resource of 67,124 clinical vocabulary embeddings derived from a clinical knowledge graph tailored to electronic health record vocabularies, spanning over 1.3 million edges. Using graph transformer neural networks, we generated clinical vocabulary embeddings that provide a new representation of clinical knowledge by unifying seven medical vocabularies. These embeddings were validated through a phenotype risk score analysis involving 4.57 million patients from Clalit Healthcare Services, effectively stratifying individuals based on survival outcomes. Inter-institutional panels of clinicians evaluated the embeddings for alignment with clinical knowledge across 90 diseases and 3,000 clinical codes, confirming their robustness and transferability. This resource addresses gaps in integrating clinical vocabularies into AI models and training datasets, paving the way for knowledge-grounded population and patient-level models.

Transcriptome-wide association studies (TWAS) are commonly used to prioritize causal genes underlying associations found in genome-wide association studies (GWAS) and have been extended to identify causal genes through multivariable TWAS methods. However, recent studies have shown that widespread infinitesimal effects due to polygenicity can impair the performance of these methods. In this report, we introduce a multivariable TWAS method named Tissue-Gene pairs, direct causal Variants, and Infinitesimal effects selector (TGVIS) to identify tissue-specific causal genes and direct causal variants while accounting for infinitesimal effects. In simulations, TGVIS maintains an accurate prioritization of causal gene-tissue pairs and variants and demonstrates comparable or superior power to existing approaches, regardless of the presence of infinitesimal effects. In the real data analysis of GWAS summary data of 45 cardiometabolic traits and expression/splicing quantitative trait loci (eQTL/sQTL) from 31 tissues, TGVIS is able to improve causal gene prioritization and identifies novel genes that were missed by conventional TWAS.

Recent studies have reported increases in early-onset cancer cases (diagnosed under age 50) and call into question whether the increase is related to earlier diagnosis from other medical tests and reflected by decreasing tumor-size-at-diagnosis (apparent effects) or actual increases in underlying cancer risk (true effects), or both. The classic Multi-Stage Clonal Expansion (MSCE) model assumes cancer detection at the emergence of the first malignant cell, although later modifications have included lag-times or stochasticity in detection to more realistically represent tumor detection requiring a certain size threshold. Here, we introduce an approach to explicitly incorporate tumor-size-at-diagnosis in the MSCE framework and account for improvements in cancer detection over time to distinguish between apparent and true increases in early-onset cancer incidence. We demonstrate that our model is structurally identifiable and provides better parameter estimation than the classic model. Applying this model to colorectal, female breast, and thyroid cancers, we examine changes in cancer risk while accounting for detection improvements over time in three representative birth cohorts (1950-1954, 1965-1969, and 1980-1984). Our analyses suggest accelerated carcinogenic events and shorter mean sojourn times in more recent cohorts. We further use this model to examine the screening impact on the incidence of breast and colorectal cancers, both having established screening protocols. Our results align with well-documented differences in screening effects between these two cancers. These findings underscore the importance of accounting for tumor-size-at-diagnosis in cancer modeling and support true increases in early-onset cancer risk in recent years for breast, colorectal, and thyroid cancer.

Significance: This study models recent increases in early-onset cancers, accounting for both true factors contributing to cancer risk and those caused by improved detection. We show that while advancement in detection has led to earlier detection, our model estimates shorter sojourn times and more aggressive carcinogenic events for recent cohorts, suggesting faster tumor progression. Further, a counterfactual analysis using this model reveals the known statistically significant reduction in colorectal cancer incidence (supporting a robust modeling approach), likely due to screening and timely removal of precancerous polyps. Overall, we introduce an enhanced model to detect subtle trends in cancer risk and demonstrate its ability to provide valuable insights into cancer progression and highlight areas for future refinement and application.

Accurate grading of gliomas is critical to guide therapy and predict prognosis. The presence of microvascular proliferation is a hallmark feature of high grade gliomas which traditionally requires targeted surgical biopsy of representative tissue. Superb microvascular imaging (SMI) is a novel high resolution Doppler ultrasound technique which can uniquely define the microvascular architecture of whole tumours. We examined both qualitative and quantitative vascular features of gliomas captured with SMI, analysing flow signal density, vessel number, branching points, curvature, vessel angle deviation, fractal dimension, and entropy. Results indicate that high-grade gliomas exhibit significantly greater vascular complexity and disorganisation, with increased fractal dimension and entropy, correlating with known histopathological markers of aggressive angiogenesis. The integrated ROC model achieved high accuracy (AUC = 0.95), highlighting SMI's potential as a non-invasive diagnostic and prognostic tool. While further validation with larger datasets is required, this study opens avenues for SMI in glioma management, supporting intraoperative decision-making and informing future prognosis.

The relationship between mitochondrial DNA (mtDNA) heteroplasmy and nuclear DNA (nDNA) methylation (CpGs) remains to be studied. We conducted an epigenome-wide association analysis of heteroplasmy burden scores across 10,986 participants (mean age 77, 63% women, and 54% non-White races/ethnicities) from seven population-based observational cohorts. We identified 412 CpGs (FDR p < 0.05) associated with mtDNA heteroplasmy. Higher levels of heteroplasmy burden were associated with lower nDNA methylation levels at most significant CpGs. Functional inference analyses of genes annotated to heteroplasmy-associated CpGs emphasized mitochondrial functions and showed enrichment in cardiometabolic conditions and traits. We developed CpG-scores based on heteroplasmy-count associated CpGs (MHC-CpG scores) using elastic net Cox regression in a training cohort. A one-unit higher level of the standardized MHC-CpG scores were associated with 1.26-fold higher hazard of all-cause mortality (95% CI: 1.14, 1.39) and 1.09-fold higher hazard of CVD (95% CI: 1.01-1.17) in the meta-analysis of testing cohorts, adjusting for age, sex, and smoking. These findings shed light on the relationship between mtDNA heteroplasmy and DNA methylation, and the role of heteroplasmy-associated CpGs as biomarkers in predicting all-cause mortality and cardiovascular disease.

Cognitive impairment is a central characteristic of schizophrenia. Executive functioning (EF) impairments are often seen in mental disorders, particularly schizophrenia, where they relate to adverse outcomes. As a heterogeneous construct, how specifically each dimension of EF to characterize the diagnostic and prognostic aspects of schizophrenia remains opaque. We used classification models with a stacking approach on systematically measured EFs to discriminate 195 patients with schizophrenia from healthy individuals. Baseline EF measurements were moreover employed to predict symptomatically remitted or non-remitted prognostic subgroups. EF feature importance was determined at the group-level and the ensuing individual importance scores were associated with four symptom dimensions. EF assessments of inhibitory control (interference and response inhibitions), followed by working memory, evidently predicted schizophrenia diagnosis (area under the curve [AUC]=0.87) and remission status (AUC=0.81). The models highlighted the importance of interference inhibition or working memory updating in accurately identifying individuals with schizophrenia or those in remission. These identified patients had high-level negative symptoms at baseline and those who remitted showed milder cognitive symptoms at follow-up, without differences in baseline EF or symptom severity compared to non-remitted patients. Our work indicates that impairments in specific EF dimensions in schizophrenia are differentially linked to individual symptom-load and prognostic outcomes. Thus, assessments and models based on EF may be a promising tool that can aid in the clinical evaluation of this disorder.

Heterozygous pathogenic variants in AXIN2 are associated with oligodontia-colorectal cancer syndrome (ODCRCS), a disorder characterized by oligodontia, colorectal cancer, and in some cases, sparse hair and eyebrows. We have identified four individuals with one of two de novo , heterozygous variants (NM_004655.4:c.196G>A, p.(Glu66Lys) and c.199G>T, p.(Gly67Arg)) in AXIN2 whose presentations expand the phenotype of AXIN2-related disorders. In addition to ODCRCS features, these individuals have global developmental delay, microcephaly, and limb, ophthalmologic, and renal abnormalities. Structural modeling of these variants suggests that they disrupt AXIN2 binding to tankyrase, which regulates AXIN2 levels through PARsylation and subsequent proteasomal degradation. To test whether these variants produce a phenotype in vivo , we utilized an innovative prime editing N1 screen to phenotype heterozygous (p.E66K) mouse embryos, which were perinatal lethal with short palate and skeletal abnormalities, contrary to published viable Axin2 null mouse models. Modeling of the p.E66K variant in the Drosophila wing revealed gain-of-function activity compared to reference AXIN2. However, the variant showed loss-of-function activity in the fly eye compared to reference AXIN2, suggesting that the mechanism by which p.E66K affects AXIN2 function is cell context-dependent. Together, our studies in humans, mice, and flies demonstrate that specific variants in the tankyrase-binding domain of AXIN2 are pathogenic, leading to phenotypic expansion with context-dependent effects on AXIN2 function and WNT signaling. Moreover, the modeling strategies used to demonstrate variant pathogenicity may be beneficial for the resolution of other de novo heterozygous variants of uncertain significance associated with congenital anomalies in humans.