Sepsis is a common critical clinical disease with high mortality that can cause approximately 10 million deaths worldwide each year. Acute lung injury (ALI) or acute respiratory distress syndrome (ARDS) is a common clinical complication of sepsis, which occurs primarily as diffuse alveolar injury, hypoxemia, and respiratory distress. The mortality rate of ALI/ARDS is as high as 30-40, which greatly endangers human health. Due to the unclear pathogenesis of ALI/ARDS, its treatment is still a worldwide problem. At present, clinical treatment mainly relies on lung-protective ventilation, prone position ventilation, and fluid management. However, there is a lack of effective and specific treatment measures. In recent years, domestic and foreign scholars have committed to basic research on ALI/ARDS, trying to further clarify its pathogenesis and find new targets and methods for the treatment of ALI/ARDS. In this review, we summarize the signaling pathways related to alveolar injury and repair in sepsis-induced ALI/ARDS and their latest research progress. They include the NF-κB, JAK2/STAT3, mitogen-activated protein kinase (MAPK), mTOR, and Notch signaling pathways. Understanding the molecular mechanisms of these signaling pathways in sepsis-induced ALI/ARDS may provide new targets and ideas for the clinical treatment of this disease.

Radiation-induced intestinal injury (RIII) is one of the most common intestinal complications caused by radiotherapy for pelvic and abdominal tumors and it seriously affects the quality of life of patients. However, the treatment of acute RIII is essentially symptomatic and nutritional support treatment and an ideal means of prevention and treatment is lacking. Researchers have conducted studies at the cellular and animal levels and found that some chemical or biological agents have good therapeutic effects on RIII and may be used as potential candidates for clinical treatment. This article reviews the injury mechanism and potential treatment strategies based on cellular and animal experiments to provide new ideas for the diagnosis and treatment of RIII in clinical settings.

Acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) is an overactivated inflammatory response caused by direct or indirect injuries that destroy lung parenchymal cells and dramatically reduce lung function. Although some research progress has been made in recent years, the pathogenesis of ALI/ARDS remains unclear due to its heterogeneity and etiology. MicroRNAs (miRNAs), a type of small noncoding RNA, play a vital role in various diseases. In ALI/ARDS, miRNAs can regulate inflammatory and immune responses by targeting specific molecules. Regulation of miRNA expression can reduce damage and promote the recovery of ALI/ARDS. Consequently, miRNAs are considered as potential diagnostic indicators and therapeutic targets of ALI/ARDS. Given that inflammation plays an important role in the pathogenesis of ALI/ARDS, we review the miRNAs involved in the inflammatory process of ALI/ARDS to provide new ideas for the pathogenesis, clinical diagnosis, and treatment of ALI/ARDS.

Objective: To retrospectively analyze the rescue and treatment of pediatric patients by the Chinese Red Cross medical team during the Nepal earthquake relief.Methods: The medical team set up a field hospital; the pediatric clinic consisted of 1 pediatrician and several nurses. Children younger than 18 years old were placed in the pediatric clinic for injury examination and treatment.Results: During the 7-day period of medical assistance (the second to third week after the earthquake), a total of 108 pediatric patients were diagnosed and treated, accounting for 2.8% of the total patients. The earthquake-related injuries mainly required surgical dressing and debridement. No severe limb fractures or traumatic brain injuries were found. Infection of the respiratory tract, the gastrointestinal tract, and the skin were the most common ailments, accounting for 42.3%, 18.5%, and 16.7%, respectively, of the total treated patients.Conclusion: Two to 3 weeks after the earthquake, the admitted pediatric patients mainly displayed respiratory and gastrointestinal infections. When developing a rescue plan and arranging medical resources, we should consider the necessity of treating non–disaster-related conditions. (Disaster Med Public Health Preparedness. 2016;page 1 of 4)

Introduction: Growing evidence indicates that variations in the composition of the gut microbiota are linked to the onset and progression of chronic respiratory diseases (CRDs), albeit the causal relationship between the two remains unclear.Methods: We conducted a comprehensive two-sample Mendelian randomization (MR) analysis to investigate the relationship between gut microbiota and five main CRDs, including chronic obstructive pulmonary disease (COPD), asthma, idiopathic pulmonary fibrosis (IPF), sarcoidosis, and pneumoconiosis. For MR analysis, the inverse variance weighted (IVW) method was utilized as the primary method. The MR–Egger, weighted median, and MR-PRESSO statistical methods were used as a supplement. To detect heterogeneity and pleiotropy, the Cochrane and Rucker Q test, MR–Egger intercept test, and MR-PRESSO global test were then implemented. The leave-one-out strategy was also applied to assess the consistency of the MR results.Results: Based on substantial genetic data obtained from genome-wide association studies (GWAS) comprising 3,504,473 European participants, our study offers evidence that several gut microbial taxa, including 14 probable microbial taxa (specifically, 5, 3, 2, 3 and 1 for COPD, asthma, IPF, sarcoidosis, and pneumoconiosis, respectively) and 33 possible microbial taxa (specifically, 6, 7, 8, 7 and 5 for COPD, asthma, IPF, sarcoidosis, and pneumoconiosis, respectively) play significant roles in the formation of CRDs.Discussion: This work implies causal relationships between the gut microbiota and CRDs, thereby shedding new light on the gut microbiota-mediated prevention of CRDs.

Early and accurate prediction of neurological outcomes in comatose patients following cardiac arrest is critical for informed clinical decision-making. Existing studies have predominantly focused on EEG for assessing brain injury, with some exploring ECG data. However, the integration of EEG, ECG, and clinical features remains insufficiently investigated, and its potential to enhance predictive accuracy has not been fully established. Moreover, the limited interpretability of current models poses significant barriers to clinical application. Using the I-CARE database, we analyzed EEG, ECG, and clinical data from comatose cardiac arrest patients. After rigorous preprocessing and feature engineering, machine learning models (Logistic Regression, SVM, Random Forest, and Gradient Boosting) were developed. Performance was evaluated through AUC-ROC, accuracy, sensitivity, and specificity, with SHAP applied to interpret feature contributions. Our multi-modal model outperformed single-modality models, achieving AUC values from 0.75 to 1.0. Notably, the model’s accuracy peaked at a critical point within the 12–24 h window (e.g., 18 h, AUC = 1.0), surpassing EEG-only (AUC 0.7–0.8) and ECG-only (AUC < 0.6) models. SHAP identified Shockable Rhythm as the most influential feature (mean SHAP value 0.17), emphasizing its role in predictive accuracy. This study presents a novel multi-modal approach that significantly enhances early neurological outcome prediction in critical care. SHAP-based interpretability further supports clinical applicability, paving the way for more personalized patient management post-cardiac arrest.