Objective:To explore the investigation and verification effect of the patient activation scale (PAM) on high-risk population of early stage cervical cancer.Methods:It was a cross-sectional study. From June to December in 2023, 1 253 women aged 18-65 years who underwent physical examination in the Department of Health Medicine of the Northern Theater General Hospital were selected continuously, and 102 women who scored ≥8 in the early warning screening questionnaire for cervical cancer were selected as the research objects. PAM was used to investigate the subjects, and the reliability and validity of the results were tested to analyze the self-health management ability and enthusiasm of the patients.Results:Among the 102 cases of high-risk population of early stage cervical cancer included in the analysis, the age was (38.5±5.2) years, 65 cases were married, 89 cases had a history of sexual activity, and 89 cases had a monthly income of ≥3 000 yuan. The results of PAM reliability analysis showed that the Cronbach′s α coefficient was 0.87, split-half reliability was 0.82, and the correlation coefficient between item and total score was 0.45-0.68. The results of PAM validity analysis showed that KMO value in structural validity was 0.75 (>0.6, suitable for factor analysis), and Bartlett′s spherical test χ2=120.78, P<0.001. Principal component analysis extracted two common factors, the cumulative variance contribution rate was 58.6%, factor 1 (self-management ability) items 1, 2, 4, 7, 8 (load 0.48-0.75), factor 2 (decision confidence) items 3, 5, 6 (load 0.51-0.58). The item-level content validity index (I-CVI) was 0.83-1.00, and the scale-level content validity index (S-CVI) was 0.92. Conclusion:PAM has a good reliability and validity test for high-risk population of early stage cervical cancer, which is suitable for evaluating the activation level of the patients in cervical cancer screening and can accurately reflect the enthusiasm of the participants.

OBJECTIVE To investigate the positive test of high-risk human papillomavirus(HR-HPV)among the female population undergoing physical examination in Shenyang,analyze the influencing factors and establish and validate the risk prediction model.METHODS The data were collected from the female population who received HPV test in the physical examination center of a three-A hospital in the whole year of 2023.The prevalence rates of HR-HPV infections and subtypes were described,the influencing factors for the infections were identified.Uni-variate analysis and multivariate logistic regression analysis were performed for the influencing factors for positive test of HR-HPV,and the prediction model was established and validated.RESULTS Totally 6 130 out of 7 759 fe-male population who received HPV test were from Shenyang,the total positive rate of HR-HPV was 10.72％a-mong the population from Shenyang,11.11％among the population from other areas,and there was no signifi-cant difference.The population from Shenyang aged between 21 and 84 years old,with the mean age(48.58±11.64)years old.Among the local population who had the infections,80.21％were the single HPV infection,and 19.79％were multiple infections;HPV52 was the predominant subtype of HPV causing the infections,followed by HPV58 and HPV 16.The result of multivariate analysis showed age,smoking history,gynecological surgery history,allergic history,family annual income and sleep condition were the influencing factors for the positive HR-HPV.The prediction model was established based on the result of the multivariate analysis,the internal vali-dation of the model was carried out by modeling data and receiver operating characteristic(ROC)curves,the area under the curve(AUC)of the prediction model was 0.919,and 95％CI was 0.878 to 0.960,indicating that the prediction model had a high efficiency.CONCLUSIONS The positive rate of HR-HPV test is not relatively high among the physical examination female population in Shenyang,and the positive result is affected by a variety of factors.The population can be vaccinated for prevention and control based on the prediction model targeting to the non-variable factors such as age,meanwhile,the measures such as enhancement of health education,adjustment of health polies and interven-tion to health behaviors should be taken for other controllable factors.

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 investigate the positive test of high-risk human papillomavirus(HR-HPV)among the female population undergoing physical examination in Shenyang,analyze the influencing factors and establish and validate the risk prediction model.METHODS The data were collected from the female population who received HPV test in the physical examination center of a three-A hospital in the whole year of 2023.The prevalence rates of HR-HPV infections and subtypes were described,the influencing factors for the infections were identified.Uni-variate analysis and multivariate logistic regression analysis were performed for the influencing factors for positive test of HR-HPV,and the prediction model was established and validated.RESULTS Totally 6 130 out of 7 759 fe-male population who received HPV test were from Shenyang,the total positive rate of HR-HPV was 10.72％a-mong the population from Shenyang,11.11％among the population from other areas,and there was no signifi-cant difference.The population from Shenyang aged between 21 and 84 years old,with the mean age(48.58±11.64)years old.Among the local population who had the infections,80.21％were the single HPV infection,and 19.79％were multiple infections;HPV52 was the predominant subtype of HPV causing the infections,followed by HPV58 and HPV 16.The result of multivariate analysis showed age,smoking history,gynecological surgery history,allergic history,family annual income and sleep condition were the influencing factors for the positive HR-HPV.The prediction model was established based on the result of the multivariate analysis,the internal vali-dation of the model was carried out by modeling data and receiver operating characteristic(ROC)curves,the area under the curve(AUC)of the prediction model was 0.919,and 95％CI was 0.878 to 0.960,indicating that the prediction model had a high efficiency.CONCLUSIONS The positive rate of HR-HPV test is not relatively high among the physical examination female population in Shenyang,and the positive result is affected by a variety of factors.The population can be vaccinated for prevention and control based on the prediction model targeting to the non-variable factors such as age,meanwhile,the measures such as enhancement of health education,adjustment of health polies and interven-tion to health behaviors should be taken for other controllable factors.

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 investigation and verification effect of the patient activation scale (PAM) on high-risk population of early stage cervical cancer.Methods:It was a cross-sectional study. From June to December in 2023, 1 253 women aged 18-65 years who underwent physical examination in the Department of Health Medicine of the Northern Theater General Hospital were selected continuously, and 102 women who scored ≥8 in the early warning screening questionnaire for cervical cancer were selected as the research objects. PAM was used to investigate the subjects, and the reliability and validity of the results were tested to analyze the self-health management ability and enthusiasm of the patients.Results:Among the 102 cases of high-risk population of early stage cervical cancer included in the analysis, the age was (38.5±5.2) years, 65 cases were married, 89 cases had a history of sexual activity, and 89 cases had a monthly income of ≥3 000 yuan. The results of PAM reliability analysis showed that the Cronbach′s α coefficient was 0.87, split-half reliability was 0.82, and the correlation coefficient between item and total score was 0.45-0.68. The results of PAM validity analysis showed that KMO value in structural validity was 0.75 (>0.6, suitable for factor analysis), and Bartlett′s spherical test χ2=120.78, P<0.001. Principal component analysis extracted two common factors, the cumulative variance contribution rate was 58.6%, factor 1 (self-management ability) items 1, 2, 4, 7, 8 (load 0.48-0.75), factor 2 (decision confidence) items 3, 5, 6 (load 0.51-0.58). The item-level content validity index (I-CVI) was 0.83-1.00, and the scale-level content validity index (S-CVI) was 0.92. Conclusion:PAM has a good reliability and validity test for high-risk population of early stage cervical cancer, which is suitable for evaluating the activation level of the patients in cervical cancer screening and can accurately reflect the enthusiasm of the participants.

Objective:To explore the perception and perceived characteristics of risk of diabetes in people at high risk of diabetes under different glycemic states, and to provide a reference for clinicians to develop targeted risk interventions.Methods:Descriptive phenomenology research method was used to conduct semi-structured interviews with 17 diabetes high-risk persons selected by purpose sampling from August 2020 to January 2022 in the Health Management Center of General Hospital of Northern Theater Command. Data was transcribed and analyzed using Colaizzi ′s method. Results:Different glycemic states led to different themes for extraction. Each distilled 3 themes, normoglycemia including ignoring the existence of risk and creating doubt, risk perception relies on individual feelings, and demonstrate a positive attitude towards their risk control capabilities; initial diagnosis of dysglycemia including perceive the severity of the risk and express fear, mediated by health professionals and a strong desire to learn, accept by high-risk status and self-reflection on lifestyle; persistent glycemic impairment including tend to ignore risks to avoid undue stress and anxiety, determined based on experience and information, laissez-faire attitude or positive change. The differences among the 3 categories were mainly reflected in attitudes, emotions, perceived predisposing factors and risk control.Conclusions:It is still necessary to strengthen the education on diabetes prevention awareness and risk factors. And blood glucose can be considered as a classification guideline for targeted education.

Helminth infections may trigger host innate and adaptive immune responses. Group 2 innate lymphoid cells (ILC2) are an important factor involved in type 2 immune responses, and produce a large number of T helper 2 cell (Th2) cytokines following stimulation by interleukin (IL)-25, IL-33 and thymic stromal lymphopoietin (TSLP), which play a critical role in parasite clearance and tissue repair. Following helminth infections, autocrine factors, mast cells, enteric nervous system and Th2 cells have been recently found to be involved in regulation of ILC2. Unraveling the role of ILC2 in immune response against helminth infections is of great value for basic research and drug development. This review summarizes the research progress on ILC2 and its role in helminth infections.

Generic drugs account for more than 95% of the chemicals market in China, and their quality is directly related to the efficacy and safety of the people. The bioequivalence evaluation with pharmacokinetic parameters as the end point is the main content of the consistency evaluation of the quality and efficacy of generic drugs. Gut microbiota is considered to have an important influence on pharmacokinetics. This article reviewed the influence of gut microbiota on pharmacokinetics and analyzed its potential significance in the evaluation of the consistency of quality and efficacy of generic drugs.

[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.