Objective To explore the risk factors associated with the development of acute respir-atory distress syndrome(ARDS)in patients with severe traumatic brain injury(sTBI)and to construct and validate a risk prediction model for ARDS in these patients.Methods Clinical data from 371 sTBI patients admitted to Yangzhou Affiliated Hospital of Yangzhou University between January 2017 and December 2023 were retrospectively collected.Patients were randomly divided into modeling group(n=259)and validation group(n=112)at a 7-to-3 ratio.A nomogram model was constructed after screening for risk factors using the Least Absolute Shrinkage and Selection Operator(LASSO)and multivariate Logistic regression analysis.Model performance was evaluated using the receiver operating characteristic(ROC)curve,area under the curve(AUC),Hosmer-Lemeshow test,calibration curve,and deci-sion curve analysis(DCA).Results Statistically significant differences were observed in heart rate,respiratory rate,pupil size,percutaneous oxygen saturation(SpO2),Glasgow Coma Scale(GCS)score,Acute Physiology and Chronic Health Evaluation Ⅱ(APACHE Ⅱ)score,head Ab-breviated Injury Scale(AIS)score,chest AIS score,emergency intubation,pulmonary infection,associated chest trauma,midline shift,blood transfusion within 12 hours of admission,fluid intake within 24 hours of admission,shock,mechanical ventilation,hemoglobin level,hematocrit,white blood cell count,prothrombin time,international normalized ratio,total protein,albumin,serum calcium,oxygenation index,and base excess between the two groups(P＜0.05).Multivariate Lo-gistic regression analysis revealed that SpO2,pulmonary infection,and fluid intake within 24 hours of admission were predictors of ARDS in sTBI patients.The Hosmer-Lemeshow test results for the modeling and validation groups showed good fit(x2=10.373,P=0.240;x2=13.21,P=0.105).DCA results for both groups indicated net benefit at threshold probabilities ranging from 0％to 72％and 0％to 50％,respectively.Conclusion SpO2,pulmonary infection,and fluid in-take within 24 hours of admission are risk factors for ARDS in sTBI patients.The model constructed using these factors demonstrates good performance and provides a reliable tool for clinical screening of high-risk ARDS populations among sTBI patients.

Objective:To investigate the effect of galactose lectin 3 (Gal-3) on the pathogenesis of atrial fibrillation.Methods:This study adopts a case-control study method. 55 patients with non valvular atrial fibrillation (atrial fibrillation group) admitted to the First People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine from February to July 2019 were selected, and another 55 healthy individuals who underwent physical examination at our hospital during the same period were selected as the control group. Compare the general data and levels of various laboratory indicators between two groups, including blood routine, fasting blood glucose, blood lipids, liver and kidney function, and plasma Gal-3. Analyze the influencing factors of atrial fibrillation and the predictive value of plasma Gal-3 levels for the onset of atrial fibrillation. The measurement data with normal distribution and the measurement data converted to normal distribution after taking natural logarithm are expressed in xˉ± s. The comparison between the two groups is performed by independent sample t test; The measurement data of non normal distribution is represented by [ M ( Q1, Q3)], and Wilcoxon signed rank sum test is used for inter group comparison; The counting data is represented by examples (%), and the comparison between groups is conducted using χ 2 test. The influencing factors of atrial fibrillation were analyzed using logistic regression analysis. Results:The age, NLR, and blood creatinine levels in the atrial fibrillation group were higher than those in the control group [(71.16±9.17) years vs (60.71±10.11) years, (2.32±0.85) vs (1.74±0.81), (74.18±21.61) μmol/L vs (64.69±18.30) μmol/L, t-values are 5.68, 3.66, 2.48, P-values are <0.001, <0.001, 0.015], total cholesterol, HDL-C, LDL-C Albumin and eGFR water were on average lower than those in the control group [(4.31±1.67) mmol/L vs (5.13±0.78) mmol/L, (0.96±0.21) mmol/L vs (1.21±0.32) mmol/L, (2.35±0.65) mmol/L vs (3.04±0.62) mmol/L, (39.58±3.83) g/L vs (44.66±5.61) g/L, (94.84±29.22) mL/(min·1.73 m 2) vs (111.77±21.51) mL/(min·1.73 m 2)] ,The t-values are 3.30, 4.87, 5.69, 5.54, 3.46, and the P-values are 0.001,<0.001,<0.001,<0.001, 0.001, respectively. The plasma Gal-3 levels in the atrial fibrillation group were higher than those in the control group [(12.79±4.24)] μg/L vs (7.31±2.28) μg/L], the difference was statistically significant ( t=8.43, P<0.001), and the plasma Gal-3 level in the persistent atrial fibrillation group was higher than that in the paroxysmal atrial fibrillation group [(14.03±3.95) μg/L vs (11.51±4.21) μg/L], the difference was statistically significant ( t=2.29, P=0.026). The results of multivariate logistic regression analysis showed that after excluding other factors, Gal-3 remained an independent influencing factor for atrial fibrillation (odds ratio=1.66, 95% confidence interval: 1.29-2.12, P<0.001). Conclusions:Plasma Gal-3 is an influencing factor for the onset of atrial fibrillation. After excluding other factors, Gal-3 remains an independent influencing factor for atrial fibrillation, with an increase of 1 μg/L in Gal-3 increases the risk of atrial fibrillation by 1.66 times.

Objective:To explore whether hyperuricemia was an independent risk factor for all-cause mortality in patients with atrial fibrillation.Methods:Patients with atrial fibrillation who were confirmed by 12-lead electrocardiogram in 11 hospitals of Kailuan Group from 2006 to 2007 were selected as the research objects.All patients were followed up by prospective cohort study, and all-cause deaths were observed.The last follow-up time was December 31, 2013.Kaplan-Meier curve and Cox proportional hazards model were used to analyze and compare the risk of all-cause mortality in patients with atrial fibrillation in the hyperuricemia group compared with the normal uric acid group.Results:A total of 388 community-based patients with atrial fibrillation were included in the final statistical analysis, with 136 all-cause deaths occurred during an average follow-up period of 6.93 years.The incidence of all-cause mortality was 9.24% per year(36/390)in the hyperuricemia group, whereas 5.16% per year(100/1 937) in the normal uric acid group.In the univariate Cox proportional risk model analysis, the risk ratio (95% CI) of all-cause death in patients with atrial fibrillation in the hyperuricemia group (95% CI) was 1.84(1.26-2.69) times that in the normal uric acid group ( P<0.01). After adjusting for potential confounding variables, the adjusted risk ratio (95% CI) of all-cause death in patients with atrial fibrillation in hyperuricemia group was still 1.94(1.32-2.85) times of that in normal uric acid group ( P<0.01). After adjustment for potential confounding variables, for each 0.01 g/L increase in uric acid (1 g/L=5 950 μmol/L), the risk of all-cause death in patients with atrial fibrillation increased by 1.15 (1.05-1.26) times ( P<0.01). Conclusion:Hyperuricemia was an independent risk factor for all-cause death in patients with atrial fibrillation in community.

Recently, the 2019 novel coronavirus (2019-nCoV) pneumonia outbroke in Wuhan and rapidly spread to all over China and even the world. Because of the strong infectivity and various clinical symptoms, it has brought certain difficulties to the epidemic prevention and control. Currently there is no specific drug for 2019-nCoV. Previous drugs used to treat other coronaviruses may be effective, but further clinical trials remain needed. We reviewed literature on the epidemiology, etiology, clinical manifestations, imaging manifestations, laboratory examination, diagnosis, complications, treatment and outcome of 2019-nCoV pneumonia.