Objective To investigate the relationship between the comorbidity of dyslipidemia, hypertension and diabetes and lifestyle of adult residents in Hebei Province in 2018, and to provide reference for the development of chronic diseases prevention measures. Methods Using data from the surveillance of chronic diseases and their risk factors among adults in Hebei in 2018, 7 711 permanent residents aged ≥18 years were selected as the research subjects. Multivariate logistic regression analysis was used to analyze the relationship between the comorbidity of dyslipidemia, hypertension, and diabetes and lifestyle in adult residents. Results A total of 7 711 subjects aged ≥18 years were included in 2018. The comorbidity rates of hypertension and dyslipidemia, diabetes and dyslipidemia, and hypertension and diabetes were 16.88%, 7.91%, and 8.13%, respectively. The comorbidity rate of hypertension, diabetes, and dyslipidemia was 22.65%. These comorbidity rates increased with age and BMI. Multivariate logistic regression results showed that male, age, overweight/obesity, physical inactivity, daily sedentary behavior time ≥5 hours, and sleep problems were risk factors for the comorbidity of the "three highs". Conclusion The prevalence of the "three highs" comorbidities is relatively high in Hebei Province, and there are multiple common risk factors. Maintaining a healthy lifestyle and implementing comprehensive prevention and control measures are the key to improving the health level of residents.

AIM: To develop a novel gene-delivery therapeutic based on CRISPR/Cas9 genome editing technology capable of specifically targeting and knocking out the VEGFA gene, thereby achieving sustained suppression of VEGFA expression in retinal pigment epithelial(RPE)cells and providing a new strategy for gene therapy in retinal neovascular diseases.METHODS:Single guide RNAs targeting the human VEGFA gene for knockout were designed, and corresponding recombinant plasmids were constructed. A novel polymer(PTEE)was used to encapsulate the plasmids to prepare a PTEE-loaded anti-VEGFA plasmid(PLAP)gene delivery system. PTEE materials at concentrations of 0.1, 0.2, 0.4, 0.8, and 1.6 μg/μL were co-incubated with ARPE-19 cells, and the biocompatibility of PTEE was evaluated using the cell counting kit-8(CCK-8)assay. Recombinant plasmids expressing green fluorescent protein(GFP)were constructed. Lipofectamine 3000 and jetOPTIMUS®DNA transfection reagents were used as control groups, and PTEE nanomaterials were used as the experimental group to encapsulate the plasmids. When the cell confluence reached 80%, the formulations were transfected into ARPE-19 and 293T cells. GFP expression was observed under light microscopy, and the transfection efficiencies of each group were compared. ARPE-19 cells were induced under hypoxia, and PLAP was transfected into the cells. The expression level of VEGFA was detected by enzyme-linked immunosorbent assay(ELISA)to evaluate the efficacy of this novel gene delivery system.RESULTS: After co-incubation of ARPE-19 cells with different concentrations of PTEE for 24 h and 48 h, no significant effect on cell viability was observed in any group. The transfection efficiency of PLAP in ARPE-19 cells was higher than that in the Lipo3000 and jetOPTIMUS groups, with statistically significant differences(P<0.01). Hypoxia for 6 h significantly induced the upregulation of VEGFA mRNA expression in ARPE-19 cells, and under hypoxic conditions, the PTEE group exhibited a significant inhibitory effect on VEGFA expression(P<0.01).CONCLUSION:PLAP exhibits favorable biocompatibility and prominent VEGFA inhibitory effects in vitro, making it a potential candidate drug for gene therapy of retinal neovascular diseases.

Objective To evaluate the application value of artificial intelligence (AI) in the quality control of mammographic images and explore its feasibility for improving image quality. Methods A retrospective analysis was conducted on 500 mammographic images from 125 female patients. These images were acquired in December 2024 at the Department of Radiology, Obstetrics and Gynecology Hospital of Tongji University by two junior technologists with qualification certificate for junior radiologic technologists and ≤ 2 months of independent operation. The reference standard was the evaluation by a panel of senior experts comprising one associate chief radiologist and two intermediate radiologic technologists with over 10 years of experience in mammography. The evaluation was based on nine criteria within an AI quality control system. The correlation and consistency (Kappa test) of the assessment outcomes were compared among the junior technologist group, the AI quality control group, and the panel group. Additionally, the differences in the proportions of high-, medium-, and low-quality images rated by the three groups were analyzed. After one month of AI assistance, 200 images from 50 additional patients acquired by the same two junior technologists in January 2025 were collected. The area under the receiver operating characteristic curve and 95% confidence interval (95%CI) were calculated for the assessment performance of the junior technologist group before and after AI assistance. Differences were compared using the DeLong test. Changes in the disqualification rates for the nine criteria were analyzed. Results In consistency analysis, the AI quality control group showed high consistency with the panel group across multiple key criteria, with Kappa values ranging from 0.41 to 1.00. In contrast, the consistency between the junior technologist group and the panel group was generally low, with Kappa values ranging from 0.13 to 0.49. In comparison of image quality classification, no significant differences were observed in image quality classification between the AI group and the panel group (P>0.05). However, the proportion of images rated as high quality by the junior technologist group was significantly higher, while the proportions rated as medium and low quality were significantly lower, compared to those rated by the panel group (P<0.05). After AI assistance, the area under the receiver operating characteristic curve for the assessment performance of junior technologist group increased significantly from 0.56 (95%CI: 0.51-0.61) to 0.91 (95%CI: 0.87-0.94) (P<0.001). The disqualification rates for skin folds, incomplete inclusion of the pectoralis major muscle, and nipple not in profile decreased significantly. Conclusion AI demonstrates assessment capability comparable to the panel group in mammographic quality control. AI effectively enhances the quality of images acquired by junior technologists through real-time, objective feedback.

Objective To analyze the prevalence of hyperuricemia (HUA) among adults in Hebei Province and its influencing factors related to comorbidities with ^“three highs^”, and to provide a theoretical basis for the prevention and control of HUA and multi-disease co-management. Methods Data were derived from the 2018 China Chronic Disease and Risk Factors Surveillance program conducted in Hebei Province. A multi-stage stratified cluster random sampling method was used to select permanent residents aged ≥18 years. All statistical analyses were adjusted for complex weighting. Design-based logistic regression was employed to analyze factors influencing HUA prevalence, and ordinal logistic regression was used to analyze the influencing factors of HUA patients and their comorbidity with ^“three highs^”. Results The prevalence of HUA among adults in Hebei Province was 10.42%, with males showing higher rates than females (P<0.05). The highest prevalence of HUA was observed in the 18-39 age group, followed by a declining trend, and a resurgence in those aged ≥70 (P<0.05). Among HUA patients with comorbidities, isolated HUA, 1 comorbidity, 2 comorbidities, and 3 comorbidities accounted for 13.94%, 33.33%, 39.17%, and 13.56%, respectively. HUA combined with hypertension and dyslipidemia represented the highest proportion (31.07%). Analysis of the influencing factors of HUA revealed that higher education levels, obesity, central obesity, and dyslipidemia increased the risk of HUA (P<0.05). Ordinal logistic regression analysis of HUA combined with three highs showed that HUA patients aged ≥50 years, overweight individuals, and those with chronic kidney disease had a higher number of patients with three highs (P<0.05). Conclusion HUA exhibits insidious onset, and it is recommended to include uric acid testing in routine physical examinations. Targeted prevention measures, such as early uric acid testing and health education, should be implemented by Hebei authorities for populations with higher education levels, obesity, central obesity, and dyslipidemia to reduce HUA incidence. Additionally, health education and regular medical check-ups are recommended for overweight HUA patients and those with chronic kidney disease to prevent comorbidities and alleviate disease burden.

Objective To evaluate the application value of artificial intelligence (AI) in the quality control of mammographic images and explore its feasibility for improving image quality. Methods A retrospective analysis was conducted on 500 mammographic images from 125 female patients. These images were acquired in December 2024 at the Department of Radiology, Obstetrics and Gynecology Hospital of Tongji University by two junior technologists with qualification certificate for junior radiologic technologists and ≤ 2 months of independent operation. The reference standard was the evaluation by a panel of senior experts comprising one associate chief radiologist and two intermediate radiologic technologists with over 10 years of experience in mammography. The evaluation was based on nine criteria within an AI quality control system. The correlation and consistency (Kappa test) of the assessment outcomes were compared among the junior technologist group, the AI quality control group, and the panel group. Additionally, the differences in the proportions of high-, medium-, and low-quality images rated by the three groups were analyzed. After one month of AI assistance, 200 images from 50 additional patients acquired by the same two junior technologists in January 2025 were collected. The area under the receiver operating characteristic curve and 95% confidence interval (95%CI) were calculated for the assessment performance of the junior technologist group before and after AI assistance. Differences were compared using the DeLong test. Changes in the disqualification rates for the nine criteria were analyzed. Results In consistency analysis, the AI quality control group showed high consistency with the panel group across multiple key criteria, with Kappa values ranging from 0.41 to 1.00. In contrast, the consistency between the junior technologist group and the panel group was generally low, with Kappa values ranging from 0.13 to 0.49. In comparison of image quality classification, no significant differences were observed in image quality classification between the AI group and the panel group (P>0.05). However, the proportion of images rated as high quality by the junior technologist group was significantly higher, while the proportions rated as medium and low quality were significantly lower, compared to those rated by the panel group (P<0.05). After AI assistance, the area under the receiver operating characteristic curve for the assessment performance of junior technologist group increased significantly from 0.56 (95%CI: 0.51-0.61) to 0.91 (95%CI: 0.87-0.94) (P<0.001). The disqualification rates for skin folds, incomplete inclusion of the pectoralis major muscle, and nipple not in profile decreased significantly. Conclusion AI demonstrates assessment capability comparable to the panel group in mammographic quality control. AI effectively enhances the quality of images acquired by junior technologists through real-time, objective feedback.

Objective To evaluate the application value of artificial intelligence (AI) in the quality control of mammographic images and explore its feasibility for improving image quality. Methods A retrospective analysis was conducted on 500 mammographic images from 125 female patients. These images were acquired in December 2024 at the Department of Radiology, Obstetrics and Gynecology Hospital of Tongji University by two junior technologists with qualification certificate for junior radiologic technologists and ≤ 2 months of independent operation. The reference standard was the evaluation by a panel of senior experts comprising one associate chief radiologist and two intermediate radiologic technologists with over 10 years of experience in mammography. The evaluation was based on nine criteria within an AI quality control system. The correlation and consistency (Kappa test) of the assessment outcomes were compared among the junior technologist group, the AI quality control group, and the panel group. Additionally, the differences in the proportions of high-, medium-, and low-quality images rated by the three groups were analyzed. After one month of AI assistance, 200 images from 50 additional patients acquired by the same two junior technologists in January 2025 were collected. The area under the receiver operating characteristic curve and 95% confidence interval (95%CI) were calculated for the assessment performance of the junior technologist group before and after AI assistance. Differences were compared using the DeLong test. Changes in the disqualification rates for the nine criteria were analyzed. Results In consistency analysis, the AI quality control group showed high consistency with the panel group across multiple key criteria, with Kappa values ranging from 0.41 to 1.00. In contrast, the consistency between the junior technologist group and the panel group was generally low, with Kappa values ranging from 0.13 to 0.49. In comparison of image quality classification, no significant differences were observed in image quality classification between the AI group and the panel group (P>0.05). However, the proportion of images rated as high quality by the junior technologist group was significantly higher, while the proportions rated as medium and low quality were significantly lower, compared to those rated by the panel group (P<0.05). After AI assistance, the area under the receiver operating characteristic curve for the assessment performance of junior technologist group increased significantly from 0.56 (95%CI: 0.51-0.61) to 0.91 (95%CI: 0.87-0.94) (P<0.001). The disqualification rates for skin folds, incomplete inclusion of the pectoralis major muscle, and nipple not in profile decreased significantly. Conclusion AI demonstrates assessment capability comparable to the panel group in mammographic quality control. AI effectively enhances the quality of images acquired by junior technologists through real-time, objective feedback.

Objective:To analyze the prevalence of chronic kidney disease (CKD) and influencing factors of adults in Hebei Province, and provide scientific evidence for the development of comprehensive CKD prevention and control strategies.Methods:In China Adult Chronic Disease and Nutrition Surveillance in Hebei in 2018, a total of 7 562 permanent residents aged ≥18 years were selected by multi-stage stratified cluster random sampling from 13 surveillance points in Hebei for questionnaire survey, medical examination and laboratory test.Results:A total of 1 067 CKD patients were detected in the adults aged ≥18 years in this survey, with a weighted prevalence rate of 12.10%. The results of multivariate analysis showed that the daily total static behavior time ( OR=1.07, 95% CI: 1.04-1.09), living in rural area ( OR=1.50, 95% CI: 1.14-1.97), coal use ( OR=1.37, 95% CI: 1.16-1.61), coal gas/liquefied gas/natural gas/biogas use ( OR=2.92, 95% CI: 2.40-3.54) and solar energy/electricity use ( OR=1.75, 95% CI: 1.36-2.25), insufficient fruit intake ( OR=1.39, 95% CI: 1.06-1.83), insufficient physical activity ( OR=1.35, 95% CI: 1.11-1.64), suffering from hypertension ( OR=1.80, 95% CI: 1.44-2.24) and suffering from diabetes ( OR=1.77, 95% CI: 1.27-2.45) were risk factors for CKD in adults in Hebei. High education level ( OR=0.41, 95% CI: 0.19-0.91), excessive drinking ( OR=0.53, 95% CI: 0.28-0.99), central obesity ( OR=0.75, 95% CI: 0.58-0.97), history of allergic diseases ( OR=0.44, 95% CI: 0.27-0.72) were protective factors for CKD. Conclusions:The prevalence of CKD in adults in Hebei was relatively high, especially in those who had too long average daily static behavior, lived in rural area, used coal, gas/liquefied gas/natural gas/biogas, solar energy/electricity, had inadequate intake of fruits, lacked physical activity and suffered from hypertension and diabetes. It is necessary to pay attention to the early prevention and treatment of CKD, strengthen the health education about healthy lifestyle and improve the management of patients with chronic disease, such as hypertension and diabetes, to further reduce the risk for CKD.

Under the current situation of "low prevalence and low infection" of schistosomiasis in China, and to provide a basis for achieving the goal of eliminating schistosomiasis by 2030 proposed by the Healthy China Action (2019 - 2030) as scheduled, the Hunan Provincial Corps Hospital of the Chinese People's Armed Police Force established a schistosomiasis monitoring and early warning index system based on the previous studies on schistosomiasis early warning index system and the recent literature analysis, combined with the current potential risk factors affecting the transmission and prevalence of schistosomiasis, and organized two rounds of expert consultation and carried out project promotion meetings. The experts reached a consensus on the comprehensiveness and practicability of the index system, aiming to lay a solid foundation for construction of China's schistosomiasis prevention and control early warning system.

This paper reports an early-onset diabetes patient combined with intrahepatic cholestasis and hepatocellular carcinoma caused by a mutation of ABCB11 gene.After surgery and drug treatment,the patient's digestive tract symptoms and diabetes were alleviated.Diabetes with primary intrahepatic cholestasis has not been reported in the literature.By introducing this case's clinical manifestations,genetic characteristics and clinical course,we hope to provide a reference for clinicians to understand the diagnosis,treatment and follow-up of this disease.

Objective:To understand the status of bone health literacy in middle-aged and older female breast cancer patients undergoing chemotherapy and analyze its influencing factors using structural equation modeling.Methods:A convenience sampling method was used to select 250 middle-aged and older breast cancer patients undergoing chemotherapy in two tertiary hospitals in Xi'an from May to October 2024. Patients were surveyed using a general information questionnaire, the Bone Health Literacy Scale for Middle-Aged and Older Women, the Perceived Social Support Scale, and the General Self-Efficacy Scale. Pearson correlation analysis was used to examine the correlations among bone health literacy, perceived social support, and self-efficacy.Results:A total of 250 questionnaires were distributed, and 239 valid questionnaires were recovered, yielding an effective response rate of 95.6% (239/250). The mean score of the Bone Health Literacy Scale among the 239 patients was (39.71±8.16). Bone health literacy was positively correlated with perceived social support and self-efficacy ( P<0.01). Perceived social support directly affected bone health literacy and could also indirectly influence it through general self-efficacy, with an indirect effect value of 0.367, accounting for 45.2% of the total effect (0.367/0.812) . Conclusions:Healthcare professionals should implement effective interventions to enhance patients' perceived social support and strengthen their self-efficacy, thereby improving bone health literacy.