Objective:To construct a prediction model for postoperative poor in-hospital prognosis in patients with traumatic brain injury (TBI) and evaluate its predictive performance.Methods:A retrospective case control study was conducted to analyze the clinical data of 1 120 TBI patients admitted to Changde Hospital Affiliated to Xiangya Medical College of Central South University from May 2019 to December 2024. The patients were divided into the training set ( n=784) and verification set ( n=336) at a ratio of 7∶3. Based on the Glasgow outcome scale-extended (GOS-E) at discharge, the training set was stratified into favorable prognosis group ( n=335, GOS-E 5-8 points) and poor prognosis group ( n=449, GOS-E 1-4 points). The two groups in the training set were compared in terms of general baseline indicators, TBI-related clinical indicators, and admission laboratory blood test results. Univariate analysis and Lasso regression analysis were employed to screen risk factors associated with postoperative poor in-hospital prognosis in TBI patients. Multivariate Logistic regression analysis was used to determine independent risk factors and construct a regression equation. The regression equation was presented using R language to create a visual nomogram for predicting postoperative poor in-hospital prognosis in TBI patients. In both the training set and verification set, the predictive performance of the model was evaluated by calculating the area under the receiver operating characteristic (ROC) curve (AUC), plotting calibration curves, and performing decision curve analysis (DCA). Results:The results of the univariate analysis indicated that the age, Charlson complication index (CCI), time from trauma to admission, time from trauma to operation, cause of injury, abbreviated injury scale (AIS) (head and neck), injury severity score (ISS), admission Glasgow coma scale (GCS), admission pupil responsiveness, multiple craniocerebral injuries, subdural hematoma, intracerebral hematoma, intraventricular hemorrhage, subarachnoid hemorrhage, decompressive craniotomy, intraoperative blood loss, intraoperative blood transfusion, traumatic cerebral infarction, postoperative delayed bleeding, epilepsy seizures, as well as the following admission tested results including red blood cell count, white blood cell count, platelet count, neutrophil percentage, percentage of lymphocytes, albumin, total bilirubin, urea nitrogen, thrombin time (TT), prothrombin time (PT), international standardized ratio (INR), glutamic aminotransferase, alanine aminotransferase, creatinine, and blood glucose were statistically different between the two groups in the training set ( P<0.05). Lasso regression analysis suggested 14 risk factors of age, CCI, cause of injury, head and neck AIS, ISS, admission GCS, admission pupil responsiveness, multiple craniocerebral injuries, subdural hematoma, intracerebral hematoma, intraoperative blood loss, admission platelet count, admission albumin, admission blood glucose for postoperative poor in-hospital prognosis. The results of the multivariate Logistic regression analysis showed that age ( OR=1.02, 95% CI 1.00, 1.03, P<0.01), CCI ( OR=1.46, 95% CI 1.02, 2.09, P<0.05), head and neck AIS ( OR=1.43, 95% CI 1.11, 1.85, P<0.01), ISS ( OR=2.16, 95% CI 1.39, 3.35, P<0.01), admission GCS ( OR=1.59, 95% CI 1.19, 2.13, P<0.01), intracerebral hematoma ( OR=4.41, 95% CI 2.15, 9.44, P<0.01), intraoperative blood loss ( OR=1.05, 95% CI 1.00, 1.09, P<0.05), admission platelet count ( OR=0.98, 95% CI 0.97, 0.99, P<0.01), admission blood glucose ( OR=1.08, 95% CI 1.02, 1.15, P<0.05) could be the main risk factors to construct a prediction model for postoperative poor in-hospital prognosis in TBI patients. Meanwhile, a regression equation was constructed: Logit[ P/(1- P)]=-2.4+ 0.02×"age"+0.38×"CCI"+0.36×"head and neck AIS"+0.77×"ISS"+0.47×"admission GCS"+1.48×"intracerebral hematoma"+0.05×intraoperative blood loss-0.02×admission platelet count+0.08×admission blood glucose. In the training set, the predictive model for poor postoperative in-hospital prognosis in TBI patients achieved an AUC of 0.87 (95% CI 0.84, 0.89), with a Youden′s index of 0.57, sensitivity of 73.70%, and specificity of 83.00%. In the verification set, the model showed an AUC of 0.80 (95% CI 0.76, 0.85), with a Youden′s index of 0.63, sensitivity of 65.20%, and specificity of 77.90%. In the training set, the Brier score for the calibration curve was 0.14 (95% CI 0.13, 0.16). In the verification set, the Brier score for the calibration curve was 0.18 (95% CI 0.15, 0.20). The DCA diagram indicated that the nomogram prediction model provided high clinical net benefit for predicting postoperative poor in-hospital prognosis in TBI patients. Conclusion:The prediction model for postoperative poor in-hospital prognosis in TBI patients, constructed based on age, CCI, head and neck AIS, ISS, admission GCS, intracerebral hematoma, intraoperative blood loss, admission platelet count, and admission blood glucose, exhibits good predictive performance.

Objective:To construct a prediction model for postoperative poor in-hospital prognosis in patients with traumatic brain injury (TBI) and evaluate its predictive performance.Methods:A retrospective case control study was conducted to analyze the clinical data of 1 120 TBI patients admitted to Changde Hospital Affiliated to Xiangya Medical College of Central South University from May 2019 to December 2024. The patients were divided into the training set ( n=784) and verification set ( n=336) at a ratio of 7∶3. Based on the Glasgow outcome scale-extended (GOS-E) at discharge, the training set was stratified into favorable prognosis group ( n=335, GOS-E 5-8 points) and poor prognosis group ( n=449, GOS-E 1-4 points). The two groups in the training set were compared in terms of general baseline indicators, TBI-related clinical indicators, and admission laboratory blood test results. Univariate analysis and Lasso regression analysis were employed to screen risk factors associated with postoperative poor in-hospital prognosis in TBI patients. Multivariate Logistic regression analysis was used to determine independent risk factors and construct a regression equation. The regression equation was presented using R language to create a visual nomogram for predicting postoperative poor in-hospital prognosis in TBI patients. In both the training set and verification set, the predictive performance of the model was evaluated by calculating the area under the receiver operating characteristic (ROC) curve (AUC), plotting calibration curves, and performing decision curve analysis (DCA). Results:The results of the univariate analysis indicated that the age, Charlson complication index (CCI), time from trauma to admission, time from trauma to operation, cause of injury, abbreviated injury scale (AIS) (head and neck), injury severity score (ISS), admission Glasgow coma scale (GCS), admission pupil responsiveness, multiple craniocerebral injuries, subdural hematoma, intracerebral hematoma, intraventricular hemorrhage, subarachnoid hemorrhage, decompressive craniotomy, intraoperative blood loss, intraoperative blood transfusion, traumatic cerebral infarction, postoperative delayed bleeding, epilepsy seizures, as well as the following admission tested results including red blood cell count, white blood cell count, platelet count, neutrophil percentage, percentage of lymphocytes, albumin, total bilirubin, urea nitrogen, thrombin time (TT), prothrombin time (PT), international standardized ratio (INR), glutamic aminotransferase, alanine aminotransferase, creatinine, and blood glucose were statistically different between the two groups in the training set ( P<0.05). Lasso regression analysis suggested 14 risk factors of age, CCI, cause of injury, head and neck AIS, ISS, admission GCS, admission pupil responsiveness, multiple craniocerebral injuries, subdural hematoma, intracerebral hematoma, intraoperative blood loss, admission platelet count, admission albumin, admission blood glucose for postoperative poor in-hospital prognosis. The results of the multivariate Logistic regression analysis showed that age ( OR=1.02, 95% CI 1.00, 1.03, P<0.01), CCI ( OR=1.46, 95% CI 1.02, 2.09, P<0.05), head and neck AIS ( OR=1.43, 95% CI 1.11, 1.85, P<0.01), ISS ( OR=2.16, 95% CI 1.39, 3.35, P<0.01), admission GCS ( OR=1.59, 95% CI 1.19, 2.13, P<0.01), intracerebral hematoma ( OR=4.41, 95% CI 2.15, 9.44, P<0.01), intraoperative blood loss ( OR=1.05, 95% CI 1.00, 1.09, P<0.05), admission platelet count ( OR=0.98, 95% CI 0.97, 0.99, P<0.01), admission blood glucose ( OR=1.08, 95% CI 1.02, 1.15, P<0.05) could be the main risk factors to construct a prediction model for postoperative poor in-hospital prognosis in TBI patients. Meanwhile, a regression equation was constructed: Logit[ P/(1- P)]=-2.4+ 0.02×"age"+0.38×"CCI"+0.36×"head and neck AIS"+0.77×"ISS"+0.47×"admission GCS"+1.48×"intracerebral hematoma"+0.05×intraoperative blood loss-0.02×admission platelet count+0.08×admission blood glucose. In the training set, the predictive model for poor postoperative in-hospital prognosis in TBI patients achieved an AUC of 0.87 (95% CI 0.84, 0.89), with a Youden′s index of 0.57, sensitivity of 73.70%, and specificity of 83.00%. In the verification set, the model showed an AUC of 0.80 (95% CI 0.76, 0.85), with a Youden′s index of 0.63, sensitivity of 65.20%, and specificity of 77.90%. In the training set, the Brier score for the calibration curve was 0.14 (95% CI 0.13, 0.16). In the verification set, the Brier score for the calibration curve was 0.18 (95% CI 0.15, 0.20). The DCA diagram indicated that the nomogram prediction model provided high clinical net benefit for predicting postoperative poor in-hospital prognosis in TBI patients. Conclusion:The prediction model for postoperative poor in-hospital prognosis in TBI patients, constructed based on age, CCI, head and neck AIS, ISS, admission GCS, intracerebral hematoma, intraoperative blood loss, admission platelet count, and admission blood glucose, exhibits good predictive performance.

Objective:To investigate the causality between intestinal flora and hypertrophic scars (HS) of human.Methods:This study was a study based on two-sample Mendelian randomization (TSMR) analysis. The data on intestinal flora ( n=18 473) and HS ( n=208 248) of human were obtained from the genome-wide association study database. Genetically variable genes at five levels (phylum, class, order, family, and genus) of known intestinal flora, i.e., single nucleotide polymorphisms (SNPs), were extracted as instrumental variables for linkage disequilibrium (LD) analysis. Human genotype-phenotype association analysis was performed using PhenoScanner V2 database to exclude SNPs unrelated to HS in intestinal flora and analyze whether the selected SNPs were weak instrumental variables. The causal relationship between intestinal flora SNPs and HS was analyzed through four methods of TSMR analysis, namely inverse variance weighted (IVW), MR-Egger regression, weighted median, and weighted mode. Scatter plots of significant results from the four aforementioned analysis methods were plotted to analyze the correlation between intestinal flora SNPs and HS. Both IVW test and MR-Egger regression test were used to assess the heterogeneity of intestinal flora SNPs, MR-Egger regression test and MR-PRESSO outlier test were used to assess the horizontal multiplicity of intestinal flora SNPs, and leave-one-out sensitivity analysis was used to determine whether HS was caused by a single SNP in the intestinal flora. Reverse TSMR analyses were performed for HS SNPs and genus Intestinimonas or genus Ruminococcus2, respectively, to detect whether there was reverse causality between them. Results:A total of 196 known intestinal flora, belonging to 9 phyla, 16 classes, 20 orders, 32 families, and 119 genera, were obtained, and multiple SNPs were obtained from each flora as instrumental variables. LD analysis showed that the SNPs of the intestinal flora were consistent with the hypothesis that genetic variation was strongly associated with exposure factors, except for rs1000888, rs12566247, and rs994794. Human genotype-phenotype association analysis showed that none of the selected SNPs after LD analysis was excluded and there were no weak instrumental variables. IVW, MR-Egger regression, weighted median, and weighted mode of TSMR analysis showed that both genus Intestinimonas and genus Ruminococcus2 were causally associated with HS. Among them, forest plots of IVW and MR-Egger regression analyses also showed that 16 SNPs (the same SNPs number of this genus below) of genus Intestinimonas and 15 SNPs (the same SNPs number of this genus below) of genus Ruminococcus2 were protective factors for HS. Further, IVW analysis showed that genus Intestinimonas SNPs (with odds ratio of 0.62, 95% confidence interval of 0.41-0.93, P<0.05) and genus Ruminococcus2 SNPs (with odds ratio of 0.62, 95% confidence interval of 0.40-0.97, P<0.05) were negatively correlated with the risk of HS. Scatter plots showed that SNPs of genus Intestinimonas and genus Ruminococcus2 were protective factors of HS. Both IVW test and MR-Egger regression test showed that SNPs of genus Intestinimonas (with Q values of 5.73 and 5.76, respectively, P>0.05) and genus Ruminococcus2 (with Q values of 13.67 and 15.61, respectively, P>0.05) were not heterogeneous. MR-Egger regression test showed that the SNPs of genus Intestinimonas and genus Ruminococcus2 had no horizontal multiplicity (with intercepts of 0.01 and 0.06, respectively, P>0.05); MR-PRESSO outlier test showed that the SNPs of genus Intestinimonas and genus Ruminococcus2 had no horizontal multiplicity ( P>0.05). Leave-one-out sensitivity analysis showed that no single intestinal flora SNP drove the occurrence of HS. Reverse TSMR analysis showed no reverse causality between HS SNPs and genus Intestinimonas or genus Ruminococcus2 (with odds ratios of 1.01 and 0.99, respectively, 95% confidence intervals of 0.97-1.06 and 0.96-1.04, respectively, P>0.05). Conclusions:There is a causal relationship between intestinal flora and HS of human, in which genus Intestinimonas and genus Ruminococcus2 have a certain effect on inhibiting HS.

Objective To investigate the prevention effect of curcumin loaded nano - liposomes on diabetic cardiomyopathy. Methods The curcumin-loaded nano-liposomes were prepared by Film dispersion and ultrasonic hydration technology and their quality inspections were also investigated.Sixty SD rats were randomly divided into normal control group,model control group,blank and curcumin-loaded nano-liposomes group ( n＝15). Diabetes model was induced by intraperitoneal single injection of STZ(70 mg·kg-1).After two weeks of STZ injection,the rats with model control were used for this study.The curcumin loaded nano-liposomes treatment group rats were treated with curcumin loaded nano-liposomes ( 5 mg·kg-1) via caudal vein administration for 12 weeks (three times a week).Rats of normal control group,blank nano-liposomes treated group and model control group were administrated equivalent volume of 0. 9% sodium chloride solution or blank nano-liposomes solution. After treatment for 12 weeks,the experimental animals underwent ultrasonic heart function examination.Then the rats were sacrificed and their hearts were arrested after saline perfusion. The myocardial cell collagen volume fraction ( CVF) and apoptosis index were detected. Results Curcumin loaded nano-liposomes showed good morphology and curcumin encapsulation efficiency ( 88. 37 ± 1.21) %with high stability and dispersibility. From the animal experiments, the evaluation indexes in curcumin loaded nano-liposomes treated group including LVIDd and LVFS were significantly higher than model control group and nano-liposomes treated group(P<0.05),and the LVPW,CVF and apoptosis index were significantly lower than model control group and nano-liposomes treated group(P<0.05). Conclusion Curcumin loaded nano-liposomes can improve the cardiac function of diabetic rats by reducing the fibrosis and apoptosis index of myocardial cells in diabetic rats, which could be used to prevent the diabetic cardiomyopathy.