SuperGLUE facilitates an explainable training framework for multi-modal data analysis.

Single-cell multi-modal data integration has been an area of active research in recent years. However, it is difficult to unify the integration process of different omics in a pipeline and evaluate the contributions of data integration. In this article, we revisit the definition and contributions of multi-modal data integration and propose a strong and scalable method based on probabilistic deep learning with an explainable framework powered by statistical modeling to extract meaningful information after data integration.

Knockoff procedure improves susceptibility gene identifications in conditional transcriptome-wide association studies.

Transcriptome-wide association studies (TWASs) have been developed to identify candidate genes associated with complex traits by integrating genome-wide association studies (GWASs) with expression quantitative trait loci (eQTL) data. However, most existing TWAS methods assess the marginal association between a single gene and a trait of interest, ignoring the influence of other genes in the same genomic region. Furthermore, false-positive gene-trait associations may arise due to correlations between eQTLs and nearby causal genetic variants.

Fluid shear stress activates a targetable mechano-metastatic cascade to promote medulloblastoma metastasis.

Biofluid flow generates fluid shear stress (FSS), a mechanical force widely present in the tissue microenvironment. How brain tumour growth alters the conduit of biofluid and impacts FSS-regulated cancer progression is unknown. Dissemination of medulloblastoma (MB) cells into the cerebrospinal fluid initiates metastasis within the central nervous system.

Pre-training Genomic Language Model with Variants for Better Modeling Functional Genomics.

Sequence-to-function models can predict gene expression from sequence data and be used to link genetic information with transcriptomics data to understand regulatory processes and their effects on complex phenotypes. The genomic language models are pre-trained with large-scale DNA sequences and can generate robust representations of these sequences by learning the genomic context. However, few studies can estimate the predictability of gene expression levels and bridge these two classes of models together to explore individualized gene expression prediction.

scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis.

Various Foundation Models (FMs) have been built based on the pre-training and fine-tuning framework to analyze single-cell data with different degrees of success. In this manuscript, we propose a method named scELMo (Single-cell Embedding from Language Models), to analyze single-cell data that utilizes Large Language Models (LLMs) as a generator for both the description of metadata information and the embeddings for such descriptions. We combine the embeddings from LLMs with the raw data under the zero-shot learning framework to further extend its function by using the fine-tuning framework to handle different tasks.

Lipid metabolites as biomarkers and therapeutic targets in oral squamous cell carcinoma.

This study explores the association of lipid metabolism disruption and Oral Squamous Cell Carcinoma (OSCC). We aim to identify specific lipid biomarkers and therapeutic targets for OSCC. We included 78 OSCC patients and 80 healthy controls, and applied non-target lipidomics and transcriptomics for comprehensive analysis.

Effect of exercise training on the swimming behaviour and rheotaxis of .

is a key stocking species in the upper Yangtze River. To determine the appropriate intensity and duration of exercise training for enhancing their field survival rate, we examined the changes in swimming capacity and rheotaxis between the trained group (exercised at 60% critical swimming speed for 6 hours daily) and the control group (reared in static water) under different training intensities and durations. Results would help explore optimal pre-release exercise protocols for hatchery-reared fish.

Ferroptosis and gastric cancer: from molecular mechanisms to clinical implications.

Gastric cancer, one of the leading causes of cancer-related mortality globally, faces challenges in treatment due to limitations in surgery, chemotherapy resistance, and high recurrence rates. Ferroptosis, an iron-dependent form of cell death, induces cell membrane rupture through dysregulated iron metabolism, lipid peroxidation, and the accumulation of reactive oxygen species (ROS), offering a promising therapeutic avenue for gastric cancer treatment. This article systematically explores the core mechanisms of ferroptosis, including iron overload catalyzing lipid peroxidation via the Fenton reaction, dysregulation of antioxidant systems (such as GPX4 and FSP1), and their associations with gastric cancer cell proliferation, metastasis, and resistance.

Expert consensus on the diagnosis and treatment of cemental tear.

Cemental tear is a rare and indetectable condition unless obvious clinical signs present with the involvement of surrounding periodontal and periapical tissues. Due to its clinical manifestations similar to common dental issues, such as vertical root fracture, primary endodontic diseases, and periodontal diseases, as well as the low awareness of cemental tear for clinicians, misdiagnosis often occurs. The critical principle for cemental tear treatment is to remove torn fragments, and overlooking fragments leads to futile therapy, which could deteriorate the conditions of the affected teeth.

Beneficial effects of dihydrocapsaicin on paraquat-induced acute kidney injury: role of Sox9/Sesn2 axis-mediated autophagy.

Autophagy inhibition induced by paraquat (PQ) poisoning is an important factor causing multi-organ damage, including renal tissue. Dihydrocapsaicin (DHC), the main active ingredient in chili peppers, was reported to induce autophagy, but its effect on PQ-induced AKI remains unclear. Rats were intraperitoneally injected with 25 mg/kg PQ to establish AKI rat model followed immediately by DHC administration (2.

Multi-Ancestry Transcriptome-wide Association Studies Uncover New Insights into Breast Cancer Genetics and Biology.

Genome-wide association studies (GWAS) have identified over 200 genetic risk loci for breast cancer, yet the target genes in these loci remain largely unknown. To address this knowledge gap, we conducted a series of multi-ancestry transcriptome-wide association studies (TWAS) to discover potential breast cancer susceptibility genes. We developed and validated ancestry-specific genetic models to predict levels of gene expression, alternative splicing, and 3' UTR alternative polyadenylation, using genomic and transcriptomic data from normal breast tissue samples of 652 females of African, Asian, or European ancestry.

Polygenic Resistance to Blood Pressure Treatment and Stroke Risk: Insights from the All of Us Research Program.

Objective: Our goal was to investigate the impact of polygenic susceptibility to hypertension on systolic blood pressure (BP), uncontrolled hypertension, and stroke among hypertensive patients with BP treatment prescription.

Methods: This genetic association study used data from the All of Us Research Program (2017-2023) and replicated findings using the United Kingdom Biobank (2006-2010). Participants prescribed BP medication ≥4 years, with diagnosed hypertension or ≥2 systolic BP measurements >130mmHg, and without a previous stroke were categorized as having low, intermediate, or high polygenic susceptibility to hypertension using percentiles (<20, 20-80, >80) of a polygenic risk score for systolic BP.

Bioengineered hybrid dual-targeting nanoparticles reprogram the tumour microenvironment for deep glioblastoma photodynamic therapy.

Glioblastoma (GBM) poses significant therapeutic challenges due to its hypoxic and immunosuppressive tumour microenvironment (TME), low immunogenicity and physical barriers. While combining photodynamic therapy (PDT) with immunotherapy holds promise, its efficacy is hampered by insufficient immune activation. In this study, we develop a multifunctional photodynamic-enhanced biomimetic intelligent nanoplatform (FBFO@HM@aOPN) responsive to the TME.

Plasticized electrohydraulic robot autopilots in the deep sea.

Soft robots, with their compliant bodies, minimal environmental disturbance, and ability to withstand ambient pressures, offer promising solutions for deep-sea exploration. However, a common challenge of stiffening in soft materials impairs their effective actuation in harsh conditions. In this work, we integrated a liquid dielectric plasticizer within an electrohydraulic soft robot, serving dual critical functions as a softening agent to maintain the softness of the polymer shell and an electrohydraulic fluid for efficient actuation.

The relationship between sleep patterns and the risk of edentulism: evidence from the CHARLS.

Background: Edentulism is a common disease among the elderly. The relationship between sleep patterns and edentulism has not been fully explored.

Methods: This study utilized baseline and follow-up data from the China Health and Retirement Longitudinal Study (CHARLS) conducted in 2011 and 2015-participants aged 45 or above.

Identification of multi-omic pleiotropy factors for peripheral artery disease.

Background: Peripheral artery disease (PAD) is prevalent and frequently co-occurs with type 2 diabetes (T2D) and coronary artery disease (CAD). Although shared genetic factors may contribute to these comorbidities, few studies have examined pleiotropy at the transcriptomic and proteomic levels.

Methods: We generated summary statistics for transcriptome-wide (TWAS) and proteome-wide (PWAS) association analyses across PAD, T2D, and CAD.

spVelo: RNA velocity inference for multi-batch spatial transcriptomics data.

RNA velocity has emerged as a powerful tool to interpret transcriptional dynamics and infer trajectory from snapshot datasets. However, current methods fail to utilize the spatial information inherent in spatial transcriptomics and lack scalability in multi-batch datasets. Here, we introduce spVelo, a scalable framework for RNA velocity inference of multi-batch spatial transcriptomics data.

Glis3 is a modifier of cyst progression in autosomal dominant polycystic kidney disease.

Background: Autosomal dominant polycystic kidney disease (ADPKD) is caused by mutations affecting polycystin-1 (PC1) or -2 (PC2). The existence of a 'cilia-dependent cyst activation' (CDCA) pathway has been identified by demonstrating that structurally intact primary cilia are crucial for cyst growth following loss of polycystins. We previously used translating ribosome affinity purification (TRAP) RNA-Seq on pre-cystic mouse kidneys to determine the translatome that meet the criteria for CDCA and identified as an early effector of polycystin signaling.

Whole-transcriptome sequencing reveals the global molecular responses and ceRNA regulatory network involved in programmed cell death of rice cultivars zyk639 and zyk-lm.

Background: Programmed cell death (PCD) can be triggered by biotic stress or abiotic stress in rice, which is a crucial factor throughout the lifecycle. Long non-coding RNAs (lncRNAs) have important roles in a variety of biological processes. However, the regulatory relationship between lncRNAs and rice PCD occurrence is unclear.

Leveraging local ancestry and cross-ancestry genetic architecture to improve genetic prediction of complex traits in admixed populations.

The broader application of polygenic risk score (PRS) is hindered by the limited transferability of PRS developed in Europeans to non-European populations. While many statistical methods have been developed to improve the performance of PRS in non-European populations, most of them focused on discrete genetic ancestry clusters and did not consider admixed individuals. Admixed individuals pose a unique challenge for PRS calculation due to the complexity of local ancestry and cross-ancestry effect sizes.

spRefine Denoises and Imputes Spatial Transcriptomics with a Reference-Free Framework Powered by Genomic Language Model.

The analysis of spatial transcriptomics is hindered by high noise levels and missing gene measurements, challenges that are further compounded by the higher cost of spatial data compared to traditional single-cell data. To overcome this challenge, we introduce , a deep learning framework that leverages genomic language models to jointly denoise and impute spatial transcriptomic data. Our results demonstrate that spRefine yields more robust cell- and spot-level representations after denoising and imputation, substantially improving data integration.

Incorporating local ancestry information to predict genetically associated DNA methylation in admixed populations.

Methylome-wide association studies (MWASs) have identified many 5'-cytosine-phosphate-guanine-3' (CpG) sites associated with complex traits. Several methods have been developed to predict CpG methylation levels from genotypes when the direct measurements of methylation are unavailable. To date, the published methods have mostly used datasets from populations of European ancestry to train prediction models for methylations, which limits the generalizability of methylome-wide association study to non-European populations.

Almost Free Enhancement of Multi-Population PRS: From Data-Fission to Pseudo-GWAS Subsampling.

Many multi-population polygenic risk score (PRS) methods have been proposed to improve prediction accuracy in underrepresented populations; however, no single method outperforms other methods across all data scenarios. Although integrating PRS results across multiple methods and populations may lead to more accurate predictions, this approach may be limited by the availability of individual-level tuning data to calculate combination weights. In this manuscript, we introduce MIXPRS, a robust PRS integration framework based on data fission principles, to effectively combine multiple multi-population PRS methods using only genome-wide association study (GWAS) summary statistics from multiple populations.

Leveraging pleiotropy to improve genetic risk prediction across diseases.

Background: Polygenic scores (PGSs) have shown promise in predicting disease risk, but their predictive accuracy remains limited for many complex diseases. Leveraging the shared genetic architecture among correlated traits may improve prediction performance.

Methods: We developed a flexible framework for constructing multi-trait PGSs by integrating candidate PGSs (N=2,651) derived from publicly available GWAS summary statistics (N=51)-using single-trait, MTAG-all, and MTAG-pairwise approaches.