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 fabricate nanosilicate functionalized polycaprolactone(PCL/LAP)electrospun membrane and evaluate its role in bone marrow mesenchymal stem cells(BMSCs)-induced bone repairing.Methods The PCL/LAP electrospun membranes were fabricated via electrospinning technology and co-cultured with rat BMSCs.The cytocompatibility of the membranes was evaluated through cytoskeleton staining,live/dead cell staining and CCK-8 assay.The migration capacity of BMSCs was assessed using scratch assay,Transwell migration experiments and expression of migration-related genes(Pdgf and Tgfβ)was evaluated by qRT-PCR.The os-teogenic differentiation and pro-angiogenesis potential were determined by alkaline phosphatase(ALP)staining,alizarin red staining,expression levels of osteogenesis-related genes(Alp,Col1a1,Runx2,Bglap and Bmp2)and angiogenesis-related genes(Angpt1,Fgf2 and Vegfa)along with RUNX2 protein expression.PCL and PCL/LAP electrospun membranes conditioned medium was subsequently used to stimulate vascular endothelial cells(EAhy926).The expression of angiogenesis-associated genes(KDR,ENOS and HIF1A)was quantified by qRT-PCR.Results BMSCs adhered well to the surface of the PCL/LAP membranes,with no significant impact on cell viability(P＞0.05).PCL/LAP membranes not only promoted the proliferation(P＜0.05),migration(P＜0.05),but also enhanced ALP activity and mineralized nodule formation(P＜0.05),increased osteogenic differentiation gene and protein expression(P＜0.05)of BMSCs.Moreover,PCL/LAP promoted the expression of angiogenic genes of BMSCs(P＜0.05),to indirectly regulate angiogenesis-related gene expression in vascular endothelial cells(P＜0.05).Conclusion PCL/LAP electrospun membranes exhibit excellent biocompatibility and can promote proliferation,migration,osteogenic differentiation and BMSC-mediated angiogenic differentiation,showing great potential for bone defect repairing as barrier membrane.

OBJECTIVE To explore the current status,etiological characteristics and risk factors for nosocomial in-fections in the patients who are treated with extracorporeal membrane oxygenation(ECMO)so as to provide bases for treatment and prevention of nosocomial infections in the ICU patients treated with ECMO.METHODS The clinical data were retrospectively collected from the patients who were treated with ECMO in the ICU of Xinyang Central Hospital from Jan.2021 to Dec.2023.The patients were divided into the infection group and the non-infec-tion group according the status of nosocomial infection during the ECMO treatment period.The constituent ratios of pathogens isolated from the patients with infections were recorded,and multivariate logistic regression analysis was performed for independent risk factors for the ECMO-related nosocomial infections.RESULTS Of 86 patients who were finally enrolled in the study,33(38.37％)had nosocomial infections.Totally 54 strains of pathogens were isolated from the patients with the infections,43(79.63％)of which were gram-negative bacteria,7(12.97％)were gram-positive bacteria,and 4(7.41％)were fungi.There were 36(66.67％)strains of multi-drug-resistant organisms(MDROs)among the 54 strains of pathogens,and 27(81.82％)patients were detected with MDROs.Among the ECMO patients with postoperative nosocomial infections,21(63.64％)cases had pul-monary infections,8(24.24％)cases had bloodstream infection,and 4(12.12％)had urinary system infections.Multivariate logistic analysis showed that the high blood glucose level at the beginning of treatment with ECMO,long duration of ECMO treatment and long time of central venous catheter indwelling were the independent risk factors for the nosocomial infections in the patients treated with ECMO(P＜0.05).CONCLUSION The isolation rate of gram-negative bacteria is relatively high among the pathogens isolated from the ECMO patients with post-operative nosocomial infections,and the drug resistance rates are high.The high blood glucose level,long duration of ECMO supporting treatment and long time of central venous catheter indwelling are the independent risk factors for the nosocomial infections in the patients treated with ECMO.

Objective To explore the correlation between TCM inspection and coronary heart disease(CHD)symptoms and TCM syndrome types.Methods Totally 336 patients with CHD or non-CHD were included in Guangdong Provincial Hospital of Traditional Chinese Medicine.A self-developed scoring standard for TCM inspection was used to collect information on TCM inspection and related symptoms and signs,and the correlation between TCM inspection and age,symptoms and TCM syndrome types was explored.Results The positive rates of Frank's sign,skin fold in nasal root(SFNR),poker face(PF),sublingual meridians and TCM comprehensive inspection increased with age(P<0.05);However,there was no significant difference in the distribution of positive and negative groups in different age groups,including hand inspection,pulse diagnosis,vertical fold between eyebrows(VFE),greasy tongue coating and dark tongue texture(P>0.05).The subjects in positive group of hand inspection,Frank's sign,PF,pulse diagnosis,dark tongue texture and TCM comprehensive inspection were more prone to suffer from chest pain than those in the negative group(P<0.05);as the score increased,the proportion of chest pain increased.The Frank's sign positive group is more prone to have palpitations than the negative group(P<0.05);the PF and pulse diagnosis positive group were more prone to have dizziness than the negative group(P<0.05);the main TCM syndrome types in the positive group of TCM inspection were yang qi deficiency combined with phlegm dampness and stasis obstruction.Conclusion The subjects in positive group of TCM inspection were more prone to suffer from chest pain,palpitations and dizziness than those in the negative group.The main TCM syndrome types in the positive group were yang qi deficiency combined with phlegm dampness and stasis obstruction.

Objective To fabricate nanosilicate functionalized polycaprolactone(PCL/LAP)electrospun membrane and evaluate its role in bone marrow mesenchymal stem cells(BMSCs)-induced bone repairing.Methods The PCL/LAP electrospun membranes were fabricated via electrospinning technology and co-cultured with rat BMSCs.The cytocompatibility of the membranes was evaluated through cytoskeleton staining,live/dead cell staining and CCK-8 assay.The migration capacity of BMSCs was assessed using scratch assay,Transwell migration experiments and expression of migration-related genes(Pdgf and Tgfβ)was evaluated by qRT-PCR.The os-teogenic differentiation and pro-angiogenesis potential were determined by alkaline phosphatase(ALP)staining,alizarin red staining,expression levels of osteogenesis-related genes(Alp,Col1a1,Runx2,Bglap and Bmp2)and angiogenesis-related genes(Angpt1,Fgf2 and Vegfa)along with RUNX2 protein expression.PCL and PCL/LAP electrospun membranes conditioned medium was subsequently used to stimulate vascular endothelial cells(EAhy926).The expression of angiogenesis-associated genes(KDR,ENOS and HIF1A)was quantified by qRT-PCR.Results BMSCs adhered well to the surface of the PCL/LAP membranes,with no significant impact on cell viability(P＞0.05).PCL/LAP membranes not only promoted the proliferation(P＜0.05),migration(P＜0.05),but also enhanced ALP activity and mineralized nodule formation(P＜0.05),increased osteogenic differentiation gene and protein expression(P＜0.05)of BMSCs.Moreover,PCL/LAP promoted the expression of angiogenic genes of BMSCs(P＜0.05),to indirectly regulate angiogenesis-related gene expression in vascular endothelial cells(P＜0.05).Conclusion PCL/LAP electrospun membranes exhibit excellent biocompatibility and can promote proliferation,migration,osteogenic differentiation and BMSC-mediated angiogenic differentiation,showing great potential for bone defect repairing as barrier membrane.

OBJECTIVE To explore the current status,etiological characteristics and risk factors for nosocomial in-fections in the patients who are treated with extracorporeal membrane oxygenation(ECMO)so as to provide bases for treatment and prevention of nosocomial infections in the ICU patients treated with ECMO.METHODS The clinical data were retrospectively collected from the patients who were treated with ECMO in the ICU of Xinyang Central Hospital from Jan.2021 to Dec.2023.The patients were divided into the infection group and the non-infec-tion group according the status of nosocomial infection during the ECMO treatment period.The constituent ratios of pathogens isolated from the patients with infections were recorded,and multivariate logistic regression analysis was performed for independent risk factors for the ECMO-related nosocomial infections.RESULTS Of 86 patients who were finally enrolled in the study,33(38.37％)had nosocomial infections.Totally 54 strains of pathogens were isolated from the patients with the infections,43(79.63％)of which were gram-negative bacteria,7(12.97％)were gram-positive bacteria,and 4(7.41％)were fungi.There were 36(66.67％)strains of multi-drug-resistant organisms(MDROs)among the 54 strains of pathogens,and 27(81.82％)patients were detected with MDROs.Among the ECMO patients with postoperative nosocomial infections,21(63.64％)cases had pul-monary infections,8(24.24％)cases had bloodstream infection,and 4(12.12％)had urinary system infections.Multivariate logistic analysis showed that the high blood glucose level at the beginning of treatment with ECMO,long duration of ECMO treatment and long time of central venous catheter indwelling were the independent risk factors for the nosocomial infections in the patients treated with ECMO(P＜0.05).CONCLUSION The isolation rate of gram-negative bacteria is relatively high among the pathogens isolated from the ECMO patients with post-operative nosocomial infections,and the drug resistance rates are high.The high blood glucose level,long duration of ECMO supporting treatment and long time of central venous catheter indwelling are the independent risk factors for the nosocomial infections in the patients treated with ECMO.

Objective To explore the correlation between TCM inspection and coronary heart disease(CHD)symptoms and TCM syndrome types.Methods Totally 336 patients with CHD or non-CHD were included in Guangdong Provincial Hospital of Traditional Chinese Medicine.A self-developed scoring standard for TCM inspection was used to collect information on TCM inspection and related symptoms and signs,and the correlation between TCM inspection and age,symptoms and TCM syndrome types was explored.Results The positive rates of Frank's sign,skin fold in nasal root(SFNR),poker face(PF),sublingual meridians and TCM comprehensive inspection increased with age(P<0.05);However,there was no significant difference in the distribution of positive and negative groups in different age groups,including hand inspection,pulse diagnosis,vertical fold between eyebrows(VFE),greasy tongue coating and dark tongue texture(P>0.05).The subjects in positive group of hand inspection,Frank's sign,PF,pulse diagnosis,dark tongue texture and TCM comprehensive inspection were more prone to suffer from chest pain than those in the negative group(P<0.05);as the score increased,the proportion of chest pain increased.The Frank's sign positive group is more prone to have palpitations than the negative group(P<0.05);the PF and pulse diagnosis positive group were more prone to have dizziness than the negative group(P<0.05);the main TCM syndrome types in the positive group of TCM inspection were yang qi deficiency combined with phlegm dampness and stasis obstruction.Conclusion The subjects in positive group of TCM inspection were more prone to suffer from chest pain,palpitations and dizziness than those in the negative group.The main TCM syndrome types in the positive group were yang qi deficiency combined with phlegm dampness and stasis obstruction.

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 screen the main characteristic variables affecting the incidence of cardiovascular and cerebrovascular diseases,and to construct the Bayesian network model of cardiovascular and cerebrovascular disease incidence risk based on the top 10 characteristic variables,and to provide the reference for predicting the risk of cardiovascular and cerebrovascular disease incidence.Methods:From the UK Biobank Database,315 896 participants and related variables were included.The feature selection was performed by categorical boosting(CatBoost)algorithm,and the participants were randomly divided into training set and test set in the ratio of 7∶3.A Bayesian network model was constructed based on the max-min hill-climbing(MMHC)algorithm.Results:The prevalence of cardiovascular and cerebrovascular diseases in this study was 28.8％.The top 10 variables selected by the CatBoost algorithm were age,body mass index(BMI),low-density lipoprotein cholesterol(LDL-C),total cholesterol(TC),the triglyceride-glucose(TyG)index,family history,apolipoprotein A/B ratio,high-density lipoprotein cholesterol(HDL-C),smoking status,and gender.The area under the receiver operating characteristic(ROC)curve(AUC)for the CatBoost training set model was 0.770,and the model accuracy was 0.764;the AUC of validation set model was 0.759 and the model accuracy was 0.763.The clinical efficacy analysis results showed that the threshold range for the training set was 0.06-0.85 and the threshold range for the validation set was 0.09-0.81.The Bayesian network model analysis results indicated that age,gender,smoking status,family history,BMI,and apolipoprotein A/B ratio were directly related to the incidence of cardiovascular and cerebrovascular diseases and they were the significant risk factors.TyG index,HDL-C,LDL-C,and TC indirectly affect the risk of cardiovascular and cerebrovascular diseases through their impact on BMI and apolipoprotein A/B ratio.Conclusion:Controlling BMI,apolipoprotein A/B ratio,and smoking behavior can reduce the incidence risk of cardiovascular and cerebrovascular diseases.The Bayesian network model can be used to predict the risk of cardiovascular and cerebrovascular disease incidence.