简介：AbstractBackground:Recent advances in surgical and neuroprotective strategies could effectively manage the pathophysiological progression of subarachnoid hemorrhage (SAH). However, pulmonary dysfunction frequently occurs in SAH patients with an increased risk of unsatisfactory outcomes. Based on the similar microvascular structures in the blood-air barrier and blood-brain barrier and possible brain-lung crosstalks, we believe that pericytes may be involved in both neurological and pulmonary dysfunction after SAH.Methods:In our experiments, platelet-derived growth factor B (PDGF-B) retention motif knockout (PDGF-Bret/ret) mice and adeno-associated virus PDGF-B were employed to show the involvement of pericyte deficiency and PDGF-B expression. Neurological score, SAH grade, hematoxylin-eosin staining, and PaO2/FiO2 ratio analysis were performed to evaluate the neurological deficits and pulmonary functions in endovascular perforation SAH models at 24 h after surgery, as well as western blotting and immunofluorescence staining for underlying molecular expressions.Results:We found that neonatal PDGF-Bret/ret mice exhibited pulmonary atelectasis 12 h after birth. Further investigation showed a decrease in PaO2/FiO2 and lung-specific surfactant proteins in adult PDGF-Bret/ret mice. These dysfunctions were much worse than those in wild-type mice at 24 h after SAH. PDGF-B overexpression alleviated pulmonary dysfunction after SAH.Conclusions:These results suggested pulmonary dysfunction after SAH and the pivotal role of PDGF-B signaling for the pathophysiological process and future therapeutic targets of pulmonary injury treatment after SAH. Further studies are needed for pathophysiological investigations and translational studies on pulmonary injuries after SAH.

简介：AbstractBackground:Fever is the most common chief complaint of emergency patients. Early identification of patients at an increasing risk of death may avert adverse outcomes. The aim of this study was to establish an early prediction model of fatal adverse prognosis of fever patients by extracting key indicators using big data technology.Methods:A retrospective study of patients’ data was conducted using the Emergency Rescue Database of Chinese People’s Liberation Army General Hospital. Patients were divided into the fatal adverse prognosis group and the good prognosis group. The commonly used clinical indicators were compared. Recursive feature elimination method was used to determine the optimal number of the included variables. In the training model, logistic regression, random forest, adaboost, and bagging were selected. We also collected the emergency room data from December 2018 to December 2019 with the same inclusion and exclusion criterion. The performance of the model was evaluated by accuracy, F1-score, precision, sensitivity, and the areas under receiver operator characteristic curves (ROC-AUC).Results:The accuracy of logistic regression, decision tree, adaboost and bagging was 0.951, 0.928, 0.924, and 0.924, F1-scores were 0.938, 0.933, 0.930, and 0.930, the precision was 0.943, 0.938, 0.937, and 0.937, ROC-AUC were 0.808, 0.738, 0.736, and 0.885, respectively. ROC-AUC of ten-fold cross-validation in logistic and bagging models were 0.80 and 0.87, respectively. The top six coefficients and odds ratio (OR) values of the variables in the logistic regression were cardiac troponin T (CTnT) (coefficient = 0.346, OR = 1.413), temperature (T) (coefficient = 0.235, OR = 1.265), respiratory rate (RR) (coefficient= –0.206, OR = 0.814), serum kalium (K) (coefficient = 0.137, OR = 1.146), pulse oxygen saturation (SPO2) (coefficient = –0.101, OR = 0.904), and albumin (ALB) (coefficient = –0.043, OR = 0.958). The weights of the top six variables in the bagging model were: CTnT, RR, lactate dehydrogenase, serum amylase, heart rate, and systolic blood pressure.Conclusions:The main clinical indicators of concern included CTnT, RR, SPO2, T, ALB, and K. The bagging model and logistic regression model had better diagnostic performance comprehesively. Those may be conducive to the early identification of critical patients with fever by physicians.