Daily Ards Research Analysis

Acute respiratory distress syndrome (ARDS) is a severe condition with complex pathogenesis, and emerging evidence highlights the potential role of metabolic factors, though the exact mechanisms are not fully understood. In this study, we used Mendelian randomisation (MR) and multi-omics approaches to investigate the causal relationship between plasma metabolites, immune cell profiles and ARDS risk. MR analysis of 1400 metabolites identified two causal metabolites linked to increased ARDS risk, primarily involved in pantothenate and CoA biosynthesis. Single-cell RNA sequencing of ARDS samples revealed that monocytes exhibited the highest levels of pantothenate synthesis. Intercellular communication and pseudotime analysis suggested that the pantothenate synthesis pathway influenced monocyte differentiation and interactions with other cell types. Gene set enrichment analysis showed that monocytes with high pantothenate synthesis were significantly enriched in phagocytosis-related pathways. Subsequent MR analysis demonstrated that CD33dim HLA DR+ CD11b+%CD33dim HLA DR+ were a risk factor against ARDS. Notably, monocytes with high pantothenate synthesis exhibited decreased expression of antigen presentation markers HLA-DRB5, HLA-DRB1 and HLA-DRA, suggesting that the high pantothenate synthesis monocytes exhibit attenuated antigen presentation and enhanced phagocytic function. Moreover, we developed a diagnostic model using machine learning algorithms. Shapley Additive explanation (SHAP) was leveraged to evaluate the model performance, with CALM2 identified as the most influential feature across the CatBoost and XGBoost models. In summary, this study integrates genetic, multi-omics and machine learning approaches to provide novel insights into the pathogenesis of ARDS and its potential therapeutic strategies targeting monocyte metabolism and function.

BACKGROUND: Acute respiratory distress syndrome (ARDS) related to acute pancreatitis in pregnancy (APIP) is associated with a higher risk of maternal and fetal death. This study aimed to explore early predictors and develop a predictive model for ARDS associated with APIP aligned with the updated global ARDS definition. METHODS: The APIP data of two hospitals over an 8-year period were retrospectively collected. The variables were analyzed using Least Absolute Shrinkage and Selection Operator regression, and binary logistic regression to build a predictive model, visualized with a nomogram. The performance of the predictive model was then evaluated. RESULTS: Of 6597 patients with acute pancreatitis, 103 pregnant patients were included, and 24 pregnant patients had ARDS. Lower oxygen saturation as measured by pulse oximetry to fraction of inspired oxygen (SpO CONCLUSION: A new accurate utility predictive model for ARDS related to APIP, including three simple variables (HR, TCH, and SpO

BACKGROUND: Hypertensive disorders of pregnancy remain a leading cause of preventable maternal and perinatal mortality, particularly in low‑ and middle‑income countries (LMICs). Eclampsia, the most severe manifestation, is responsible for a disproportionate share of maternal deaths in sub‑Saharan Africa, yet contemporary data from Tigray, Ethiopia are scarce. OBJECTIVE: To determine the prevalence, clinical presentation, and factors associated with adverse feto‑maternal outcomes among women managed for eclampsia at Ayder Comprehensive Specialized Hospital (ACSH), Tigray, Ethiopia, between 1 January 2017 and 31 December 2021. METHODS: We conducted a retrospective cross‑sectional review of all women with a discharge diagnosis of eclampsia. A piloted extraction tool, adapted from recent studies, was used to abstract socio‑demographic, obstetric, clinical, laboratory, management, and outcome variables from medical charts. EpiData v4.6 was used for data entry, and data were analysed with Stata v16. Descriptive statistics summarised prevalence and presentation. Multivariate logistic regression identified independent predictors of (i) maternal end‑organ injury and (ii) perinatal death, reporting adjusted odds ratios (aOR) with 95% confidence intervals (CI). Model fitness was assessed with the Hosmer-Lemeshow test (p > 0.05). RESULTS: Of 23,090 deliveries during the study period, 252 women (1.1%, 95% CI 1.0-1.2%) were diagnosed with eclampsia; 240 charts were analysed. The case‑fatality rate was 3.3% and perinatal mortality 20.1%. Antepartum eclampsia accounted for 63.8%, intrapartum 9.6%, and postpartum 26.7%. Headache (77.5%), visual disturbance (53.8%), and epigastric/right‑upper‑quadrant pain (46.3%) were the most frequent prodromal symptoms. Independent predictors of maternal end-organ injury were referral from another facility (aOR 4.9, 95% CI 1.8-13.9) and having perinatal death (aOR 2.7, 95% CI 1.2-6.1). Vaginal delivery (aOR 5.5, 95% CI 2.3-13.3), and pregnancies complicated with postpartum haemorrhage (aOR 3.2, 95% CI 1.2-8.3), acute respiratory distress syndrome (aOR 3.2, 95% CI 1.1-9.3), and dialysis‑requiring acute kidney injury (aOR 24.7, 95% CI 5.6-109.9) were independent predictors of perinatal death. CONCLUSIONS: Eclampsia prevalence at ACSH remains high and is associated with substantial maternal and perinatal mortality. Strengthening antenatal surveillance, streamlining referral pathways, and ensuring timely definitive delivery are critical to improving outcomes. Context-specific quality‑improvement initiatives should prioritise the prevention and aggressive management of hypertensive disorders of pregnancy.