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Record W4411273223 · doi:10.1186/s40635-025-00766-4

Advancing omics technologies in acute respiratory distress syndrome: paving the way for personalized medicine

2025· review· en· W4411273223 on OpenAlexaff
Lou’i Al-Husinat, Mohammad Araydah, Sarah Al Sharie, Saif Azzam, Denise Battaglini, Arqam Alrababah, Rana Haddad, Khaled Al-Asad, Claúdia C. dos Santos, Marcus J. Schultz, Fernanda Ferreira Cruz, Pedro Leme Silva, Patrícia R. M. Rocco

Bibliographic record

VenueIntensive Care Medicine Experimental · 2025
Typereview
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
FundersInstituto Nacional de Ciência e Tecnologia em Medicina RegenerativaFundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de JaneiroConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsMedicineAcute respiratory distressOmicsPersonalized medicineIntensive care medicineRespiratory distressDistressBioinformaticsInternal medicineLungSurgery

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.876
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.388
Teacher spread0.348 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations11
Published2025
Admission routes1
Has abstractyes

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