Intratumoral microorganisms within solid tumor TMEs significantly influence tumorigenesis and development by altering immune and metabolic patterns. Their diverse compositions and species contribute to the structural and functional heterogeneity of the TME, affecting tumor progression. Understanding the dual roles of these microbes in antitumor and protumor activities and their complex interactions with the TME enhances our knowledge of the mechanisms underlying tumorigenesis and development.
rcssci represents an innovative restricted cubic spline (RCS) curve optimization methodology within the R package ecosystem, designed to refine and elevate the visual elegance of conventional RCS curves. This advanced visualization method enhances the aesthetics of existing RCS curves, generating both statistically appropriate and artistically compelling density distributions. It adeptly uncovers meaningful correlations across RCS curves for diverse effect sizes while accentuating critical graphical elements such as reference thresholds for odds ratios (OR), risk ratios (RR), hazard ratios (HR), and morphologically optimized cut‐point values. Featuring straightforward installation and a user‐friendly interface, rcssci serves as an indispensable analytical toolkit to enhance the researcher’s comprehension of continuous exposure–outcome relationships through visualization prior to engaging with more complex machine learning‐driven correlation analyses. The rcssci package is available free of charge with an explicit reference to this publication. For comprehensive guidance on installation protocols, bug fixes, and accessing real‐time enhancements to the package, researchers are directed to consult the official announcements regarding the rcssci toolkit within the WeChat official account titled “实战医学统计” (in Chinese).
This review details a three‐stage paradigm shift for tumor‐reactive CD8+ T‐cell identification: decoding transcriptomic states, deciphering clonal functional efficacy, and molecular‐level therapeutic TCR design. Addressing translational hurdles and generative AI “scientific blind spots”—such as missing catch bonds—we present a visionary roadmap. By fusing explainable AI (XAI) with dynamic physical constraints via accelerated MD simulations, this framework enables force‐activated functional prediction to engineer programmable cytotoxic TCRs.
Integration of plasma proteomics and multi‐organ MRI in 666 UK Biobank participants, combined with a knockoff‐based causal inference and machine learning framework, reveals organ‐specific drivers of ectopic fat: PLA2G1B (pancreas), ERBB2/IGFBP2 (liver), CA14 (muscle), and NCAM2/TNFRSF10B (pericardium). These drivers engage distinct pathways (e.g., PI3K‐Akt in liver, BMP/TGFβ in muscle) and predict divergent disease outcomes—muscle fat broadly associates with cardiometabolic, neurological, and digestive diseases, whereas liver fat links to diabetes/hypertension and pancreatic fat to metabolic conditions. This causal atlas supports tissue‐targeted strategies.
This review systematically compares the size‐dependent toxicity of microplastics and nanoplastics in the intestine, liver, kidney, lung, and brain. Particle size determines bioavailability, barrier penetration, and injury mechanisms. Because of their small size and high surface reactivity, NPs readily cross the intestinal epithelium and blood–brain barrier, enter mitochondria and lysosomes, and trigger ferroptosis through iron overload and lipid peroxidation. They also disrupt mitochondrial membrane potential and activate inflammatory pathways. In contrast, larger MPs remain mainly extracellular, inducing YAP‐mediated metabolic reprogramming, chronic oxidative stress, and persistent immune activation via mechanical stress. These differences drive organ‐specific outcomes: NPs cause intestinal ferroptosis while MPs induce metabolic shifts; NPs damage liver mitochondria whereas MPs provoke chronic granulomas; and neurotoxicity occurs directly via NPs or indirectly through the gut–brain axis. The gut acts as a central hub, spreading local injury to distant organs via gut–liver, gut–brain, and gut–kidney axes. This size‐based framework supports precision toxicology and health risk asse…
Mendelian randomization (MR) leverages genetic variants to mitigate confounding biases in causal inference. This review systematically maps MR's methodological evolution, highlights its expanding applications in epidemiology and drug target validation, and outlines future directions for overcoming current biases through dynamic, multi‐omics, and cross‐ancestry integration.
A schematic diagram illustrating the KMT5C‐H4K20me3‐EWSR1‐ACADM signaling axis and its role in ccRCC progression. Key Outcomes: KMT5C/H4K20me3 are upregulated in ccRCC and predict poor prognosis. EWSR1 is a novel noncanonical H4K20me3 reader in ccRCC. KMT5C/EWSR1 co‐repress ACADM via transcription and m6A modification. A‐196 + sunitinib synergistically inhibits ccRCC growth in vivo.
ABSTRACT With the rapid advancement of genome‐wide association studies (GWAS), downstream analyses of GWAS data have become essential for elucidating the genetic mechanisms that underlie complex diseases. However, current post‐GWAS analyses face numerous challenges, including heterogeneous data formats, challenges in multi‐omics integration, and increasingly complex analytical workflows. As a comprehensive post‐GWAS analysis software platform, Omics GWAS provides researchers with an automated, end‐to‐end solution spanning data preparation through results’ visualization by integrating functional modules such as data standardization, Mendelian randomization, multi‐omics joint analysis, comorbidity mechanism exploration, and drug target discovery. The platform supports conversion of GWAS summary statistics across diverse data sources, integrates multidimensional omics data including expression quantitative trait loci (eQTLs), protein quantitative trait loci (pQTLs), and methylation quantitative trait loci (mQTLs) and systematically investigates causal relationships between genotypes and phenotypes using methods such as Mendelian randomization, colocalization analysis, and summary dat…
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