Daily Ards Research Analysis

BACKGROUND: Two biological subphenotypes in acute respiratory distress syndrome (ARDS) have been identified in retrospective analyses, with differential clinical outcomes and post-hoc responses to investigational treatments. The ability to identify biological subphenotypes in real-time is unknown. We aimed to evaluate the feasibility of using the multisite ISPY COVID Network to prospectively evaluate biological subphenotypes in real-time. METHODS: This prospective, observational, cohort study enrolled patients with ARDS and severe acute hypoxaemic respiratory failure (AHRF) and assessed the feasibility of real-time stratification into biological subphenotypes using plasma concentrations of IL-6, soluble tumour necrosis factor-1 (TNFR1), and clinical variables. Participants were eligible if they were receiving mechanical ventilation, non-invasive positive pressure ventilation, or heated high flow nasal oxygen (at flow rates ≥30 L/min); had severe AHRF (defined by an SpO FINDINGS: From June 15, 2023, to Oct 31, 2024, 844 patients at 17 hospitals in the ISPY COVID Network across the USA were screened for the study. After 504 exclusions and two withdrawals of consent, 338 patients were enrolled. 124 (37%) of the enrolled cohort were classified as AHRF, and 214 (63%) were classified as ARDS. 199 (59%) of patients were male and 138 (41%) were female, and the median age at enrolment was 64 years (IQR 54-74). The majority of patients were white (239 [71%]). 250 (74%) of the enrolled cohort completed subphenotype assignment using fresh plasma and were defined as successfully subphenotyped. Successful real-time subphenotyping increased from 59 for the first 100 enrolled participants (59% [95% CI 49-69]) to 82 for the last 100 enrolled participants (82% [73-89]), meeting the predefined feasibility threshold. Median time to subphenotype assignment from blood collection in the overall cohort and the successfully subphenotyped subgroup was 2·2 h (IQR 1·5-19·8) and 1·9 h (1·3-2·3) from the time of blood collection, respectively. The hyperinflammatory subphenotype was identified in 61 (29%) of 214 participants with ARDS and 29 (23%) of the 124 participants with severe AHRF. Clinical outcomes including mortality, organ support-free days and ventilator-free days were worse in patients with hyperinflammatory ARDS compared with those with hypoinflammatory ARDS. INTERPRETATION: Rapid real-time biological subphenotyping for ARDS and severe AHRF in a multisite US hospital network is feasible; and successful real-time subphenotyping both improved over the study time-course and was completed within 2·2 h from study blood collection. These results support the feasibility of real-time precision trials of therapies targeting biological subphenotypes in ARDS. FUNDING: COVID R&D Consortium, Allergan, Amgen, Takeda Pharmaceutical Company, Ingenus Pharmaceuticals, Implicit Bioscience, Johnson & Johnson, Pfizer, Roche-Genentech, Apotex, FAST Grant from Emergent Venture George Mason University, and The Grove Foundation. This work was supported by the US Defense Threat Reduction Agency (MCDC-2013-001). This project has been funded in whole or in part with Federal funds from the US Department of Health and Human Services; Administration for Strategic Preparedness and Response; and Biomedical Advanced Research and Development Authority (MCDC-2014-001).

Mechanisms underlying paediatric acute respiratory distress syndrome (PARDS) remain poorly understood, limiting advances in diagnosis and treatment. To address this, we conduct a high-dimensional, multi-omics analysis of paired pulmonary and blood samples from children with PARDS and age-matched controls. Our approach includes transcriptomics, proteomics, flow and mass cytometry, and single-cell RNA sequencing, with further validation using cytokine assays and in vitro models. Severe PARDS is characterised by three convergent immune abnormalities; Pulmonary CD8

BACKGROUND: Acute lung injury and its severe form, acute respiratory distress syndrome (ARDS), represent life-threatening conditions with high mortality rates. Since the lung is the primary target organ in systemic inflammatory conditions like sepsis, this study aimed to identify key regulatory genes by analyzing systemic inflammation-related transcriptomic data, and to explore their roles in lung injury. METHODS: We performed bioinformatic screening on the GSE32707 dataset (systemic inflammation patients vs. controls) using differential expression analysis, WGCNA, and machine learning. Functional enrichment, PPI network, immune infiltration, ceRNA network, and molecular docking analyses were conducted. Validation was carried out using human samples, cell-based assays, and murine lung injury models. RESULTS: We identified 506 differentially expressed genes, predominantly enriched in immune-related pathways. Machine learning algorithms prioritized lactoferrin (LTF) and matrix metalloproteinase-8 (MMP8) as key genes. Co-IP assays demonstrated a physical association between LTF and MMP8. A predicted ceRNA network suggested post-transcriptional regulation of LTF, while molecular docking indicated binding potential of LTF/MMP8 with methylprednisolone. Experimental validation confirmed that both genes are upregulated during lung injury and can be modulated by anti-inflammatory treatment. CONCLUSION: LTF and MMP8 play critical roles in lung injury, potentially serving as molecular connectors between systemic inflammation and pulmonary damage, and represent promising therapeutic targets for further investigation.