Daily Ards Research Analysis

BACKGROUND AND PURPOSE: Acute respiratory distress syndrome (ARDS) and acute lung injury (ALI) are characterised by increased pulmonary capillary permeability, but lack effective pharmacotherapies. Emerging evidence implicates gut-lung axis dysregulation in ARDS pathogenesis through microbiome-host interactions, but the specific role of the microbiota-derived metabolite, trimethylamine-N-oxide (TMAO), remains unclear. EXPERIMENTAL APPROACH: Plasma TMAO and hypersensitive C-reactive protein (hs-CRP) levels were measured in ARDS patients and healthy controls. A lipopolysaccharide (LPS)-induced ALI mouse model was employed to evaluate the effects of TMAO administration versus the inhibition of its gut microbiome-derived synthesis. In vitro, the necessity of VAV3 in the mechanism of TMAO was confirmed using gene knockdown techniques to assess endothelial barrier integrity. KEY RESULTS: Plasma TMAO levels were significantly elevated in ARDS patients compared with healthy controls, and showed a positive correlation with hs-CRP. In the murine ALI model, TMAO administration reduced lung vascular leakage and neutrophil infiltration, whereas inhibiting its synthesis worsened the injury. Mechanistically, TMAO enhances the integrity of the endothelial barrier by up-regulating VAV3, which in turn drives Rac1-dependent cortical actin reorganisation. Knockdown of VAV3 abolished the protective effects of TMAO on the endothelial barrier integrity. CONCLUSION AND IMPLICATIONS: This study identifies TMAO as an adaptive mediator within the gut-lung axis that mitigates pulmonary vascular hyperpermeability. The protective mechanism operates via the VAV3-Rac1-cytoskeletal signalling pathway, highlighting the therapeutic potential of TMAO in ALI/ARDS.

BACKGROUND: It is supposed that acute respiratory distress syndrome (ARDS) patients with increased dead space, indicated by elevated ventilatory ratio (VR), had higher mortality. The difference in mortality predictors among ARDS patients categorized by VR remains unclear, so we aimed to investigate the risk factors for mortality prediction in subgroups defined by VR and develop risk models to predict the 30-day mortality in distinct ARDS subgroups. METHODS: This study performed a retrospective analysis using the Medical Information Mart for Intensive Care IV (MIMIC-IV) and eICU Collaborative Research Database (eICU-CRD) databases, as well as data from NHLBI ARDS Clinical Trials Network (ARDSnet). Patients were divided into high VR (VR ≥2) and low VR (VR <2) subgroups based on baseline VR. In ARDSnet cohort, two 30-day mortality risk prediction models were constructed using logistic regression, internally validated using the bootstrap method, and externally validated in the MIMIC-IV and eICU-CRD cohorts. The performance of models was evaluated using receiver operating characteristic curves, Brier scores, calibration curves, and decision curve analysis (DCA) curves, and DeLong test was used to compare the predictive efficacy of different models in each subgroup. RESULTS: This study included a total of 2977 ARDS patients: 1031 in the high VR training cohort, 1506 in the low VR training cohort, 159 in the high VR external validation cohort, and 281 in the low VR external validation cohort. Through stepwise regression analysis, 11 predictors were finally selected to construct high VR prediction model including age, body mass index (BMI), heart rate, mean arterial pressure, body temperature, blood urea nitrogen (BUN), bilirubin, minute ventilation, peak inspiratory pressure, inspired oxygen fraction (FiO CONCLUSION: This study developed and validated prognostic prediction models for patients of high and low VR subgroups, respectively. The models demonstrated good discrimination, calibration, and clinical utility. Prognostic risk factors differed between high and low VR subgroups, and prediction models developed for specific VR subgroups exhibited better predictive performance within their respective subgroup populations.

Optimizing pulmonary exogenous surfactant delivery remains a critical challenge in neonatal care, particularly for achieving uniform distal lung deposition while minimizing airway obstruction. This study aimed to establish a preclinical imaging framework for quantitative assessment of exogenous surfactant lung distribution in vivo using contrast-enhanced ultrashort echo time (UTE) MRI and to evaluate the impact of physiological processes on surfactant retention and localization. Six juvenile rabbits received intratracheal instillation of a gadolinium-enhanced surfactant solution under clinically relevant conditions. High-resolution, motion-robust 3D UTE MRI datasets were acquired, enabling voxel-wise quantification of signal enhancement, gadolinium concentration, and spatial surfactant lung distribution. Peripheral volume fraction, laterality index, and distality index were computed to characterize deposition homogeneity and distal penetration. In vivo measurements were compared with previously reported ex vivo data from isolated rabbit thoraces. Quantitative MRI enabled precise mapping of surfactant within peripheral alveolar regions. Forty-two percent of the instilled dose was retained in vivo, compared with 93.5% ex vivo. Laterality indices confirmed balanced right-left distribution, while distality indices demonstrated consistent peripheral deposition. The radial UTE sequence minimized motion artifacts and enabled robust voxel-wise quantification under physiological breathing conditions. Quantitative in vivo MRI provides a sensitive and reliable method for assessing pulmonary surfactant delivery, spatial distribution, distal penetration, and homogeneity of deposition. Comparison with ex vivo data underscores the role of physiological processes in surfactant retention. This framework supports optimization of administration strategies in preclinical models and may be extended to other intrapulmonary therapies, establishing a versatile imaging platform for translational research.