Advancing omics technologies in acute respiratory distress syndrome: paving the way for personalized medicine
Bibliographic record
Abstract
Despite advances in critical care, acute respiratory distress syndrome (ARDS) remains a potentially life-threatening condition with high mortality. The heterogeneous nature of ARDS, caused by diverse etiologies, poses considerable challenges to accurate diagnosis, treatment, and prognosis. Conventional methods often fail to elucidate the pathophysiology of ARDS, thus limiting therapeutic efficacy. However, recent advances in omics technologies, including genomics, transcriptomics, proteomics, metabolomics, lipidomics, and epigenomics, have provided deeper insights into ARDS mechanisms. Genomic studies have identified genetic variants associated with ARDS susceptibility, such as polymorphisms in genes encoding angiotensin-converting enzyme, surfactant proteins, toll-like receptor 4, interleukin-6, Fas/FasL, and vascular endothelial growth factor, offering potential therapeutic targets. Transcriptomic and proteomic reveal distinct biomarker profiles associated with ARDS pathogenesis, including dysregulated inflammatory signaling, epithelial and endothelial barrier dysfunction, and compromised immune responses. Metabolomics has highlighted biomarkers, such as phenylalanine and choline, aiding in severity assessment, subphenotype stratification, and treatment response prediction. Lipidomics has uncovered disruptions in lipid metabolism, including altered phospholipids, sphingolipids, and eicosanoids, with key lipid species such as lysophosphatidylcholine and ceramide emerging as biomarkers for severity and outcomes. Epigenomics explores DNA methylation, histone modifications, and non-coding RNAs, revealing their role in regulating inflammation, immune responses, and tissue repair in ARDS. These epigenetic changes hold promise for biomarker discovery and personalized therapy. Integrating these omics technologies advances our understanding of ARDS pathophysiology, enabling precision medicine approaches. This review examines the latest advancements in omics research related to ARDS, emphasizing its role in developing personalized diagnostics and therapeutic strategies to improve disease monitoring, prognosis, and treatment outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".