Airborne microplastics (MPs) pose significant respiratory and systemic health risks upon inhalation; however, current assessment methods remain inadequate. This study integrates breathomics and transcriptomics to establish a non-invasive approach for...
Single-cell RNA-sequencing (scRNA-seq) coupled with robust computational analysis facilitates the characterization of phenotypic heterogeneity within tumors. Current scRNA-seq analysis pipelines are capable of identifying a myriad of malignant and no...
BACKGROUND: Fibrosis is a common complication in Crohn's disease (CD), often leading to intestinal strictures. This study aims to explore the transcriptomic signature of fibrostenotic ileal CD for a comprehensive characterization of biological and ce...
BACKGROUND: The endometrioid subtype of endometrial cancer is a significant health concern for women, making it crucial to study the factors influencing patient outcomes.
This study achieved cancer type and survival time prediction by transforming transcriptomic features into feature maps and employing deep learning models. Using transcriptomic data from 27 cancer types and survival data from 10 types in the TCGA data...
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Jun 1, 2025
Latest developments in spatial transcriptomics enable thoroughly profiling of gene expression while preserving tissue microenvironment. Connecting gene expression with spatial arrangement is key for precise spatial domain identification, enhancing th...
Ecotoxicology and environmental safety
Jun 1, 2025
Autism Spectrum Disorder (ASD) is a prevalent neurodevelopmental condition influenced by both genetic and environmental factors, including pesticide exposure. This study aims to investigate the pathogenic mechanisms of ASD and identify potential caus...
STUDY QUESTION: Does the transcriptome of preconceptional endometrium in the proliferative phase show a specific profile in women with recurrent pregnancy loss (RPL)?
Interdisciplinary sciences, computational life sciences
Jun 1, 2025
Existing single-cell clustering methods are based on gene expressions that are susceptible to dropout events in single-cell RNA sequencing (scRNA-seq) data. To overcome this limitation, we proposed a pathway-based clustering method for single cells (...
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