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 collect and analyze laboratory indicators of patients of different sexes after blood transfusion, evaluate the effectiveness of blood transfusion, and provide a theoretical basis for formulating more scientific blood transfusion plans.Methods:The clinical data of 808 patients who underwent blood transfusion in The First Affiliated Hospital of Anhui University of Science and Technology from January 2020 to December 2021 were retrospectively analyzed. According to the blood transfusion strategy and the department to which the patients were admitted, these patients were divided into four groups: surgical restrictive blood transfusion group (group A: 72 males and 69 females), surgical non-restricted blood transfusion (group B: 77 males and 118 females), medical restrictive blood transfusion (group C: 184 males and 126 females), and medical non-restricted blood transfusion (group D: 110 males and 52 females). Univariate and multivariate Logistic regression analyses were performed.Results:In group A, after blood transfusion, hemoglobin level in female patients was significantly higher than that in male patients [79.0 (71.5, 87.0) g/L vs. 75.5 (69.0, 82.8) g/L, Z = -2.18, P = 0.029], and C-reactive protein in female patients was significantly lower than that in male patients [21.3 (0.0, 56.0) mg/L vs. 37.0 (3.3, 95.5) mg/L, Z = -1.97, P = 0.049]. In groups B, C, and D, there were no significant differences in hemoglobin, C-reactive protein, and hematocrit between male and female patients (all P > 0.05). Multivariate analysis showed that the difference in hemoglobin levels between before and after blood transfusion was statistically significant ( P = 0.009). After a blood transfusion, hemoglobin level in female patients was 1.44 times that in male patients. Conclusion:The tolerance of female patients to blood loss is higher than that of male patients in surgical restrictive blood transfusion, so the threshold value of hemoglobin given to female patients during blood transfusion can be lower than that of male patients. In the case of the same blood loss, priority of blood transfusion can be given to male patients. In the case of scarce blood resources, the total amount of blood transfused for female patients can be approximately reduced.

Humans ; Asthma/drug therapy* ; Biological Therapy

OBJECTIVE To analyze the odor composition changes of two kinds of traditional Chinese medicine sachet （children type and adults type） with different placement time by using ultra-fast gasphase electronic nose technology. METHODS The change rule of sachet components at different storage times was analyzed by gas chromatography. At the same time， the qualitative results were obtained by combining electronic nose with Arochembase database. Discriminant factor analysis was used to analyze the overall odor composition differences of the two sachet samples. RESULTS A total of 10 odor compositions were identified in children-type sachet， including α-pinene and β-pinene as the functional index compositions； five odor compositions of children-type sachet disappeared after 0.25 days， and most of them disappeared after 7 days； the cumulative contribution rate of discriminant factor analysis was 99.225%. A total of 8 odor compositions were identified in adult-type sachets， including α-pinene and α-phellandrene as the functional index compositions； four odor components disappeared after the adult-type sachet was placed for 0.25 days； after 15 days of placement， the peak 6-8 disappeared， and the intensity of peak 5 decreased by 34.3% compared with 0 day of placement； the cumulative contribution rate of discriminant factor analysis was 91.965%. CONCLUSIONS With the extension of storage time， the smell and composition of the two traditional Chinese medicine sachets are decreasing. It is recommended that the use time of children-type sachet is 7 days， and that of adult-type sachet is 15 days.

Objective:To characterize the histopathological subtypes and their clinicopathological parameters of gender and onset age by common, rare and sparse primary esophageal malignant tumors (PEMT).Methods:A total of 272 437 patients with PEMT were enrolled in this study, and all of the patients were received radical surgery. The clinicopathological information of the patients was obtained from the database established by the State Key Laboratory of Esophageal Cancer Prevention & Treatment from September 1973 to December 2020, which included the clinical treatment, pathological diagnosis and follow-up information of esophagus and gastric cardia cancers. All patients were diagnosed and classified by the criteria of esophageal tumor histopathological diagnosis and classification (2019) of the World Health Organization (WHO). The esophageal tumors, which were not included in the WHO classification, were analyzed separately according to the postoperative pathological diagnosis. The χ 2 test was performed by the SPSS 25.0 software on count data, and the test standard α=0.05. Results:A total of 32 histopathological types were identified in the enrolled PEMT patients, of which 10 subtypes were not included in the WHO classification. According to the frequency, PEMT were divided into common (esophageal squamous cell carcinoma, ESCC, accounting for 97.1%), rare (esophageal adenocarcinoma, EAC, accounting for 2.3%) and sparse (mainly esophageal small cell carcinoma, malignant melanoma, etc., accounting for 0.6%). All the common, rare, and sparse types occurred predominantly in male patients, and the gender difference of rare type was most significant (EAC, male∶ female, 2.67∶1), followed with common type (ESCC, male∶ female, 1.78∶1) and sparse type (male∶ female, 1.71∶1). The common type (ESCC) mainly occurred in the middle thoracic segment (65.2%), while the rare type (EAC) mainly occurred in the lower thoracic segment (56.8%). Among the sparse type, malignant melanoma and malignant fibrous histiocytoma were both predominantly located in the lower thoracic segment (51.7%, 66.7%), and the others were mainly in the middle thoracic segment.Conclusion:ESCC is the most common type among the 32 histopathological types of PEMT, followed by EAC as the rare type, and esophageal small cell carcinoma and malignant melanoma as the major sparse type, and all of which are mainly occur in male patients. The common type of ESCC mainly occur in the middle thoracic segment, while the rare type of EAC mainly in the lower thoracic segment. The mainly sparse type of malignant melanoma and malignant fibrous histiocytoma predominately occur in the lower thoracic segment, and the remaining sparse types mainly occur in the middle thoracic segment.

OBJECTIVE：To esta blish the m ethod for identifying Schizonepeta tenusfolia from different habitats based on odor information. METHODS ：The odor of S. tenusfolia from different habitats were identified by Heracles Ⅱ ultra-fast gas phase electronic nose. Qualitative analysis was conducted according to obtained chromatographic information combined with AroChemBase database and Kovats retention index qualitative database. Principle component analysis （PCA）and discriminant factor analysis （DFA）were conducted by using Alpha Soft V 14.2 software，and cluster analysis （CA）was performed with SPSS 22.2 software. RESULTS ：There were 16 common peaks in 15 batches of S. tenusfolia from different habitats. After comparison with AroChemBase database and Kovates retention index qualitative database ，a total of 13 possible components were obtained. The possible components and sensory description information of S. tenusfolia from different habitats were basically the same ，but only the content was different. The chromatographic peak intensities of common peak No. 2 were in descending order as Anhui ＞ Gansu＞Henan＞Hebei＞Jiangsu，the chromatographic peak intensities of common peak No. 6 were in descending order as Anhui ＞ Hebei＞Gansu≈Henan＞Jiangsu，the chromatographic peak intensities of common peak No. 9 were in descending order as Anhui ＞ Gansu＞Henan＞Jiangsu＞Hebei，the chromatographic peak intensity of common peak No. 13 were in descending order as Anhui ≈ Gansu＞Hebei＞Jiangsu＞Henan，which represented the chromatographic peak intensity of methyl formate （peak No. 2），α-pinene （peak No. 6），3-nonone（peak No. 9）and α-terpineol（peak No. 13）were significantly different due to the change of habitats. PCA results showed that the cumulative contribution rate of the first two principal components was 96.807％. Results of DFA showed that contribution rates of discriminant factor 1 and discriminant factor 2 were 92.089％ and 3.982％. CA results showed that when the distance was 10，15 batches of samples could be clustered into 3 categories，B1-B5 and J 1-J3 into one category ，A1-A3 into one category ，G1，G2，N1 and N 2 into one category. The results were basically consistent with those of PCA and DFA. CONCLUSIONS：Ultra-fast gas phase electronic nose technology can be used to identify S. tenusfolia from different habitats rapidly. Methyl formate ，α-pinene，3-nonone and α-terpineol may be the key factors to distinguish S. tenusfolia from different habitats.

Objective To investigate the clinical effect of three capsule combined with Function injection combined with functional exercise in the treatment of knee osteoarthritis.Methods120 patients with knee osteoarthritis were randomly divided into two groups, the study group took orally hydrochloride capsule combined with joints injection combined with functional exercise therapy, and the control group only oral glucose capsules.two groups of patients after a period of treatment.ResultsIn the VAS score, lequesne index and serum inflammatory molecular level of the two groups were significantly better than the control group (P<0.05).ConclusionWhich the clinical study on treating of the knee osteoarthritis by using glucosamine capsule and injecting sodium hyaluronate combined with appropriate functional exercise has a significant effect on knee osteoarthritis, and more favorable in improving the quality of life of patients.

Objective To investigate the diagnostic parameters,criteria and diagnostic value of pelvic floor ultrasound in female stress urinary incontinence(SUI).Methods Simple factor logistic regression analysis was used to compare the difference of ultrasonic parameters between SUI patients(260 cases) and asymptomatic subjects(60 cases) to find the relevant diagnostic indexes,and to evaluate the diagnostic criteria and diagnostic value by the ROC curve.Results There were significant differences in urethral inclination angle and levator hiatus area in resting and bladder neck position,bladder position,urethral inclination angle,retrovesical angle,levator hiatus area in Valsalva state and urethral rotation angle,bladder neck mobility between the two groups (all P ＜ 0.05).There was no significant difference in age,BMI,bladder neck position,bladder position,retrovesical angle between resting in the two groups (all P ＞0.05).Using the ROC curve analysis,the cut-off points of urethral inclination angle and levator hiatus area in resting,bladder neck and bladder position,urethral inclination angle,retrovesical angle,levator hiatus area in Valsalva,bladder neck mobility and urethra rotation angle to diagnose SUI were 16.5°,13.5 cm2,3.5 mm,0.5 mm,29.5°,139.5°,19.5 cm2,24.5 mm,45.5°,respectively.The sensitivity/specificity were 54.6％/66.7％,49.2％/80.0％,68.1％/95.0％,64.2％/98.3％,67.3％/93.3％,73.5％/50.0％,68.8％/81.7％,70.0％/95.0％,67.2％/85.0％,respectively.The area under the curve were 0.625,0.668,0.855,0.854,0.817,0.622,0.811,0.866,0.817,respectively.Conclusions Pelvic floor ultrasound is a better way to diagnose stress urinary incontinence,and it provides an objective basis for the diagnosis of SUI.