Short long bones (SLBs) detected prenatally are associated with chromosomal abnormalities; however, risk stratification according to phenotypic severity and associated anomalies remains incompletely defined. In this 10-year retrospective cohort study (2015-2024), 790 fetuses with SLBs (≥ 2 SD below the gestational mean) underwent conventional karyotyping (CS) and/or chromosomal microarray analysis (CMA). SLBs were categorized by severity (mild, moderate, severe) and presentation (isolated vs. non-isolated). Detection rates were compared across subgroups. Chromosomal abnormalities were identified in 38/790 fetuses (4.8%), including 2.2% numerical and 2.7% structural aberrations. The detection rate increased with limb shortening severity (3.1% in mild, 8.2% in moderate, and 8.8% in severe cases; P = 0.005). Non-isolated SLBs were associated with a significantly higher abnormality rate than isolated cases (7.7% vs. 2.2%, P < 0.001). CMA detected clinically relevant submicroscopic copy number variants missed by karyotyping, whereas karyotyping identified balanced rearrangements not detectable by CMA. Chromosomal abnormalities represent a measurable but limited proportion of fetuses with SLBs, with risk significantly influenced by phenotypic severity and coexisting anomalies. These findings support a risk-stratified framework for prenatal genetic evaluation and may inform clinical counseling and management strategies.

This study was conducted to delineate layer-specific transcriptomic alterations in the keratoconus (KC) epithelium and stroma and to identify potential biomarkers using machine learning (ML)-based analysis. Two publicly available microarray datasets were integrated for epithelium analysis. For stroma analysis, RNA sequencing data from 31 KC patients and four normal donors were combined with a public microarray dataset. Differentially expressed genes (DEGs) were identified separately, after which functional enrichment, gene set enrichment, and gene set variation analyses were performed. Four ML algorithms were then applied, and feature importance was ranked using SHapley Additive exPlanations. The top 50% of genes across all the models were intersected, and hub genes were identified on the basis of protein-protein interaction (PPI) networks. The key findings were validated in two independent public datasets and by quantitative real-time polymerase chain reaction. A total of 272 and 5,124 DEGs were identified in the epithelium and stroma, respectively, with 53 DEGs downregulated in both. Enrichment analysis of these shared genes revealed dysregulation of retinoid, steroid hormone, and aldehyde metabolism. Layer-specific analyses revealed the downregulation of immune and inflammatory signalling, stress and redox responses, cellular proliferation, and tissue repair. ML and PPI network analysis were used to identify the epithelial hub genes CXCL10 and SLC1A1, which were both downregulated. In the stroma, 10 immune-related hub genes were identified, with the expression of IL6, IL1B, CXCL8, CXCL10, and ICAM1 being downregulated. In KC, inflammation, stress responses, and tissue repair occur in the epithelium and stroma, and metabolic and neural pathway dysregulation occurs, with more extensive stromal alterations than epithelial changes.

Acute myocardial infarction (AMI) is a leading cause of death worldwide, with oxidative stress (OS) playing a central role in its pathogenesis. However, the dynamic expression profiles and functional implications of OS-related genes at the single-cell level remain poorly understood. OS activity first assessed in single-cell RNA sequencing (scRNA-seq) data from myocardial tissue samples. Then, we integrated scRNA-seq data from AMI patients with multi-cohort bulk RNA-seq datasets to comprehensively assess OS activity across peripheral immune cells. A curated list of 807 OS-related genes was used to construct OS activity scores at the single-cell level. Differential analysis, correlation filtering, and univariate logistic regression were applied to identify high-risk OS-related genes. Six machine learning algorithms-including LASSO, Random Forest, Boruta, Bayesian modeling, LVQ, and Treebag were used in consensus to prioritize diagnostic biomarkers. To further characterize the functional implications of TKT, we performed downstream analyses including Western Blot analysis, single-cell expression profiling, pseudotime trajectory inference, and cell-cell communication network construction. TKT emerged as a robust diagnostic marker, consistently selected across all machine learning models and validated in both training and independent cohorts (AUC > 0.73). TKT was predominantly expressed in monocytes and neutrophils theoretically, where it correlated with high OS activity and pro-inflammatory gene signatures. Pseudotime analysis revealed distinct expression dynamics in monocyte and neutrophil lineages. CellChat analysis further suggested that TKT⁺ immune cells act as central hubs within the AMI immune microenvironment, mediating enhanced inflammatory communication. This study identifies TKT as a high-confidence, oxidative stress-associated biomarker for AMI through an integrated multi-omics and machine learning approach. TKT may serve as a bridge between metabolic reprogramming and immune activation, offering insights into potential therapeutic targets in post-infarction inflammation.

Multi-omics in combination with advanced computational methodologies synthesizes diverse omics data to provide deeper insights into molecular interactions and offers transformative potential for unravelling phenomenon behind disease complexities, improving diagnostics, disease prevention, and personalized treatments. This integrative strategy enables our understanding of gene-environment relationships, chronic disease progression, and the intricate molecular pathways involved in health. Effective multi-omics analyses require robust data sharing, accessibility, interoperability, and governance, which are critical for linking genomic elements to phenotypic traits. The Global Alliance for Genomics and Health advocates for responsible data-sharing practices, by promoting key principles such as transparency and equity. By emphasizing a collaborative approach to data utilization, our proposed framework seeks to advance improved disease prevention and treatment strategies. Multi-disciplinary collaboration, encompassing researchers, clinicians, policy makers, and patient representatives, is pivotal for driving innovation and addressing rare disease diagnostics. The success of multi-omics applications hinges on the establishment of comprehensive datasets, understanding the functional implications of multi-omic variation, adherence to findable, accessible, interoperable, reusable (FAIR) and Collective Benefit, Authority to Control, Responsibility, and Ethics (CARE) principles, and the strengthening of global genomic commons, benefiting scientific research, drug development, and broader health initiatives. Our review highlights essential components of multi-omics integration, underscoring its potential to transform the landscape of precision medicine and improved patient outcomes worldwide.

Aberrant microRNA expression has been implicated in cervical carcinogenesis, making miRNAs promising biomarkers for early detection and risk stratification of cervical precancer and cancer. This study aimed to identify and validate miRNA expression profiles and signatures that distinguish benign, precancer, and cancer cervical tissues, providing insights into their potential utility in cervical cancer screening and diagnosis. We analyzed miRNA expression from archival FFPE cervical tissues of 272 women (100 benign, 89 precancer, 83 invasive cancer) using next-generation sequencing. Differential expression was assessed using DESeq2 with significant miRNAs defined by an absolute log2 fold change ≥ 1 and an adjusted p-value < 0.05. Functional enrichment of miRNA targets was performed using multiMiR and DAVID, and protein-protein interaction (PPI) networks were constructed using STRING and Cytoscape. Machine learning classifiers were developed with recursive feature elimination (RFE) and XGBoost and evaluated in internal and external cohorts. miRNA signatures were further assessed for association with overall survival in the TCGA cervical cancer cohort using multivariable Cox regression models. Notably, hsa-miR-1246 was the most significant miRNA in cancer vs. benign (log₂FC = 5.58, p = 3.20 × 10⁻41) and cancer vs. precancer (log₂FC = 4.89, p = 5.23 × 10⁻33). hsa-miR-149-5p was most significant in benign vs. precancer (log₂FC = −1.48, p = 1.53 × 10⁻¹⁴). Enrichment analyses highlighted cancer-related pathways with hub genes including TP53, BCL2, STAT3, ESR1, and MYC. RFE with XGBoost identified a 26-miRNA panel for multiclass classification and a 4-miRNA panel for binary cancer vs. benign classification. The binary model achieved strong performance with an AUC of 0.97 and 0.91 in internal and external validation sets respectively. Both the 26-miRNA (p < 0.001) and 4-miRNA (p = 0.01) signatures were associated with poorer overall survival. Our findings identify distinct miRNAs and miRNA-based signatures that differentiate benign, precancer, and cancer cervical tissues, highlighting their potential clinical utility as diagnostic and prognostic biomarkers. Furthermore, the development of compact, biologically interpretable miRNA signatures demonstrates strong promise as robust classifiers for early cancer detection and tissue characterization, with possible therapeutic relevance.

The Human Exposome Project (HEP) aims to chart lifelong environmental exposures and their biological consequences, furnishing the environmental counterpart to the genomic revolution. Yet the fine‑grained, multimodal data streams that fuel exposomics-biospecimens, geolocation traces, wearable‑sensor feeds, and socio‑environmental metadata-raise privacy, justice, and governance questions that may exceed the reach of conventional bioethics. Building on lessons from genomics, biobanking, digital health, and environmental‑justice research, we identify five foundational ethical domains for exposome science: (1) privacy and data sovereignty, (2) informed consent and sustained participant engagement, (3) environmental justice, (4) governance and oversight, and (5) actionability and the responsible return of results,as well as (6)the adherence to research program goals. Similar to the "values in design" construct widely used in the socio-technical field and the "ethics by design" in the artificial intelligence (AI) field, we translate these domains into operational pillars for ethics‑by‑design research practice: dynamic or tiered consent architectures; participatory governance mechanisms such as community advisory boards; embedded ethics research programs; algorithmic‑fairness protocols for artificial‑intelligence analytics; and dedicated review bodies equipped to evaluate longitudinal, sensor‑based, multi‑omics studies. Concrete recommendations include federated data stewardship to minimize re‑identification risk, Evidence‑to‑Decision frameworks that couple exposomic evidence with societal values, and transparent pathways for communicating context‑dependent findings to individuals, communities, and policymakers. Ethical preparedness and action are a prerequisite for the scientific impact and social license of exposome research. Institutionalizing the proposed roadmap-via an international Exposome Ethics Consortium, expanded training for Institutional Review Boards, harmonized regulatory guidance, and sustained community co‑governance-will help protect privacy, promote equity, and foster public trust. Embedding systematic ethical reflection as core infrastructure will enable the Human Exposome Project to realize its promise of precision public health without replicating patterns of opaque surveillance, marginalization, or data commodification. The Human Exposome Project (HEP) represents an ambitious endeavor to characterize lifelong environmental exposures in relation to health. Yet, this vision brings profound ethical challenges: from managing massive, sensitive datasets to ensuring justice for disproportionately exposed communities. This article synthesizes foundational work on exposome ethics, outlines core ethical challenges, and proposes a proactive ethical governance model that ensures scientific integrity and social legitimacy.