Objective:To explore the application value of machine learning prediction model for neural invasion in gallbladder cancer based on enhanced computed tomography (CT) and clinical characteristics.Methods:The retrospective cohort study was conducted. The clinical and imaging data of 502 patients with gallbladder cancer who were admitted to The First Affiliated Hospital of Xinxiang Medical University from January 2010 to June 2024 were collected. There were 171 males and 331 females, aged 65(range, 35?91)years. All patients underwent preoperative abdominal enhanced CT and radical resection. The 502 patients were randomly divided into a training set of 351 cases and a test set of 151 cases at a 7:3 ratio. The training set was used to construct prediction model, and the test set was used to validate prediction model. Observation indicators: (1)neural invasion in gallbladder cancer and influencing factor analysis; (2) construction and validation of machine learning prediction models for neural invasion in gallbladder cancer. Comparison of count data between groups was conducted using the chi-square test. Comparison of ordinal data between groups was conducted using the Mann-Whitney U test. Logistic regression model was performed for univariate and multivariate analyses. Independent influencing factors were incor-porated to construct machine learning models using the standard library modules based on Python 3.9. Receiver operating characteristic (ROC) curves were plotted, and the accuracy, sensitivity, specificity, area under the curve (AUC), precision, F1 score, positive predictive value, negative predic-tive value, and Kappa value were calculated to evaluate the predictive performance of the models. The Delong test was used to assess the differences in AUC among different models in the test set. The Hosmer-Lemeshow test and Brier score were used to evaluate the calibration of the models. Results:(1) Neural invasion in gallbladder cancer and influencing factor analysis. Of the 502 patients with gallbladder cancer, 131 cases had neural invasion, and 371 cases had no neural invasion. Results of multivariate analysis showed that total bilirubin, carcinoembryonic antigen, CA199, CA125, neutrophil-lymphocyte ratio, liver invasion detected by CT, vascular invasion detected by CT, hilar or retroperi-toneal lymph node metastasis detected by CT, and tumor stages T3 and T4 were independent influencing factors for neural invasion in patients with gallbladder cancer [ odds ratios=3.747, 2.395, 3.917, 3.596, 2.805, 2.377, 3.523, 2.774, 5.080, 6.809, 95% confidence interval ( CI) as 1.890?7.430, 1.154?4.971, 2.054?7.472, 1.807?7.155, 1.506?5.225, 1.241?4.553, 1.666?7.449, 1.483?5.189, 2.050?12.589, 2.552?18.168, P<0.05]. (2) Construction and validation of machine learning predic-tion models for neural invasion in gallbladder cancer. Based on the independent influencing factors, seven machine learning models were constructed, including logistic regression, K-nearest neighbors, support vector machine, random forest, decision tree, back-propagation neural network, and gradient boosting machine. The ROC curves of seven machine learning models in the test set were plotted, and the AUC were 0.900(95% CI as 0.851?0.948), 0.741(95% CI as 0.646?0.829), 0.836(95% CI as 0.762?0.895), 0.782(95% CI as 0.701?0.855), 0.839(95% CI as 0.770?0.901), 0.817(95% CI as 0.738?0.887), 0.843(95% CI as 0.770?0.909), respectively. Results of Delong test showed that the logistic regression model had the highest AUC. The sensitivity and specificity of the logistic regression model were 0.868 and 0.805 respectively, indicating the best balance. Results of Hosmer-Lemeshow test showed that the logistic regression model had a good goodness-of-fit ( χ2=5.320, P>0.05). The Brier score of the logistic regression model was relatively low, as 0.168, which verified its calibration advantage. Conclusion:Total bilirubin, carcinoembryonic antigen, CA199, CA125, neutrophil-to-lymphocyte ratio, liver invasion detected by enhanced CT, vascular invasion detected by enhanced CT, hilar or retroperitoneal lymph node metastasis detected by enhanced CT, and tumor stages T3 and T4 are independent influencing factors for nerve invasion in patients with gallbladder cancer. Seven machine learning models are constructed based on enhanced CT and clinical characteristics to predict neural invasion in gallbladder cancer, of which the logistic regression model demonstrates good predictive performance.

Objective:To explore the application value of machine learning prediction model for neural invasion in gallbladder cancer based on enhanced computed tomography (CT) and clinical characteristics.Methods:The retrospective cohort study was conducted. The clinical and imaging data of 502 patients with gallbladder cancer who were admitted to The First Affiliated Hospital of Xinxiang Medical University from January 2010 to June 2024 were collected. There were 171 males and 331 females, aged 65(range, 35?91)years. All patients underwent preoperative abdominal enhanced CT and radical resection. The 502 patients were randomly divided into a training set of 351 cases and a test set of 151 cases at a 7:3 ratio. The training set was used to construct prediction model, and the test set was used to validate prediction model. Observation indicators: (1)neural invasion in gallbladder cancer and influencing factor analysis; (2) construction and validation of machine learning prediction models for neural invasion in gallbladder cancer. Comparison of count data between groups was conducted using the chi-square test. Comparison of ordinal data between groups was conducted using the Mann-Whitney U test. Logistic regression model was performed for univariate and multivariate analyses. Independent influencing factors were incor-porated to construct machine learning models using the standard library modules based on Python 3.9. Receiver operating characteristic (ROC) curves were plotted, and the accuracy, sensitivity, specificity, area under the curve (AUC), precision, F1 score, positive predictive value, negative predic-tive value, and Kappa value were calculated to evaluate the predictive performance of the models. The Delong test was used to assess the differences in AUC among different models in the test set. The Hosmer-Lemeshow test and Brier score were used to evaluate the calibration of the models. Results:(1) Neural invasion in gallbladder cancer and influencing factor analysis. Of the 502 patients with gallbladder cancer, 131 cases had neural invasion, and 371 cases had no neural invasion. Results of multivariate analysis showed that total bilirubin, carcinoembryonic antigen, CA199, CA125, neutrophil-lymphocyte ratio, liver invasion detected by CT, vascular invasion detected by CT, hilar or retroperi-toneal lymph node metastasis detected by CT, and tumor stages T3 and T4 were independent influencing factors for neural invasion in patients with gallbladder cancer [ odds ratios=3.747, 2.395, 3.917, 3.596, 2.805, 2.377, 3.523, 2.774, 5.080, 6.809, 95% confidence interval ( CI) as 1.890?7.430, 1.154?4.971, 2.054?7.472, 1.807?7.155, 1.506?5.225, 1.241?4.553, 1.666?7.449, 1.483?5.189, 2.050?12.589, 2.552?18.168, P<0.05]. (2) Construction and validation of machine learning predic-tion models for neural invasion in gallbladder cancer. Based on the independent influencing factors, seven machine learning models were constructed, including logistic regression, K-nearest neighbors, support vector machine, random forest, decision tree, back-propagation neural network, and gradient boosting machine. The ROC curves of seven machine learning models in the test set were plotted, and the AUC were 0.900(95% CI as 0.851?0.948), 0.741(95% CI as 0.646?0.829), 0.836(95% CI as 0.762?0.895), 0.782(95% CI as 0.701?0.855), 0.839(95% CI as 0.770?0.901), 0.817(95% CI as 0.738?0.887), 0.843(95% CI as 0.770?0.909), respectively. Results of Delong test showed that the logistic regression model had the highest AUC. The sensitivity and specificity of the logistic regression model were 0.868 and 0.805 respectively, indicating the best balance. Results of Hosmer-Lemeshow test showed that the logistic regression model had a good goodness-of-fit ( χ2=5.320, P>0.05). The Brier score of the logistic regression model was relatively low, as 0.168, which verified its calibration advantage. Conclusion:Total bilirubin, carcinoembryonic antigen, CA199, CA125, neutrophil-to-lymphocyte ratio, liver invasion detected by enhanced CT, vascular invasion detected by enhanced CT, hilar or retroperitoneal lymph node metastasis detected by enhanced CT, and tumor stages T3 and T4 are independent influencing factors for nerve invasion in patients with gallbladder cancer. Seven machine learning models are constructed based on enhanced CT and clinical characteristics to predict neural invasion in gallbladder cancer, of which the logistic regression model demonstrates good predictive performance.

[Abstract] Objective: To develop a new type of CD7 chimeric antigen receptor modified T cell (CD7-CAR-T) for the treatment of CD7 positive acute myeloid leukemia (AML), and to observe its killing effect on CD7 positive AML cells. Methods: The CD7-CAR lentiviral vector was constructed based on the CD7 Nanobody sequence and costimulatory domain sequence of CD28 and 4-1BB. The lentiviral particles were packaged and used to co-transfect human T cells with protein expression blocker (PEBL), so as to prepare CD7- CAR-T cells. Real time cellular analysis (RTCA) was used to monitor the cytotoxicity of CD7-CAR-T cells on CD7 overexpressed 293T cells. Flow cytometry assay was used to detect the effect of CD7-CAR-T cells on proliferation and cytokine secretion of AML cells with high, medium and low CD7 expressions (KG-1, HEL and Kasumi-1 cells, respectively). Results: CD7-CAR-T cell was successfully constructed and its surface expression of CD7 was successfully blocked. Compared with T cells, CD7-CAR-T cells could significantly inhibit the proliferation of CD7-293T cells and promote the release of TNF, Granzyme B and INF-γ; in addition, CD7-CAR-T cells also significantly promoted the apoptosis (t=147.1, P<0.01; t=23.57, P<0.01) and cytokine release (P<0.05 or P<0.01) in CD7 positive KG-1 and HEL cells, but had little effect on Kasumi-1 cells that only expressed minimal CD7 antigen (t=0.7058, P>0.05). Conclusion: CD7-CAR-T cells can specifically kill CD7-positive AML cells in vitro.

OBJECTIVETo improve the occupational health management levels in electroplating enterprises with quantitative classification measures and to provide a scientific basis for the prevention and control of occupational hazards in electroplating enterprises and the protection of workers' health.

METHODSA quantitative classification table was created for the occupational health management in electroplating enterprises. The evaluation indicators included 6 items and 27 sub-items, with a total score of 100 points. Forty electroplating enterprises were selected and scored according to the quantitative classification table. These electroplating enterprises were classified into grades A, B, and C based on the scores.

RESULTSAmong 40 electroplating enterprises, 11 (27.5%) had scores of >85 points (grade A), 23 (57.5%) had scores of 60∼85 points (grade B), and 6 (15.0%) had scores of <60 points (grade C).

CONCLUSIONQuantitative classification management for electroplating enterprises is a valuable attempt, which is helpful for the supervision and management by the health department and provides an effective method for the self-management of enterprises.

Objective To investigate the prevalence and pattern of androgenetic alopecia (AGA) in Shanghai through a community-based survey. Methods A cluster sampling survey was done among the residents in Beixinjing Community, Changning District, Shanghai. All the subjects were asked to fill a questionnaire to provide their general information, including sex, age, native place, physical status, life habit, family history, etc. The diagnosis of AGA was made by dermatologists. To determine the pattern of hair loss,Norwood-Hamilton classification system and Ludwig classification system were used for male AGA and female AGA, respectively. All the data were statistically analyzed by EpiData and SPSS11.5 software. Results Totally, 7056 subjects completed the questionnaire, including 3519 males and 3537 females, and the response rate was 72.5%. AGA was diagnosed in 809 patients, consisting of 701 males aging from 19 to 91 years (mean 64.16±11.9 years) and 108 females aging from 35 to 91 years (mean 70.46±18.89 years). The standardized prevalence (SP) was 9.47% in total, 15.73% in males and 2.73% in females; the difference was significant between males and females (χ2=356.00, P<0.001). A family history of AGA was observed in 52.7% of all subjects including 391 (55.78%) males and 35 (32.41%) females. Type Ⅲ vertex involvement was the most common type in men aging from 20 to 70 years old, and type Ⅵ in those over 70 years old. Grade Ⅰ and Ⅱ predominated in female AGA. Conclusions The results of this survey indicate that the prevalence of AGA is remarkably higher in men than that in women. Furthermore, the prevalence is steadily increased with advancing age in Shanghai.