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.

HDAC7, a member of class IIa HDACs, plays a pivotal regulatory role in tumor, immune, fibrosis, and angiogenesis, rendering it a potential therapeutic target. Nevertheless, due to the high similarity in the enzyme active sites of class IIa HDACs, inhibitors encounter challenges in discerning differences among them. Furthermore, the substitution of key residue in the active pocket of class IIa HDACs renders them pseudo-enzymes, leading to a limited impact of enzymatic inhibitors on their function. In this study, proteolysis targeting chimera (PROTAC) technology was employed to develop HDAC7 drugs. We developed an exceedingly selective HDAC7 PROTAC degrader B14 which showcased superior inhibitory effects on cell proliferation compared to TMP269 in various diffuse large B cell lymphoma (DLBCL) and acute myeloid leukemia (AML) cells. Subsequent investigations unveiled that B14 disrupts BCL6 forming a transcriptional inhibition complex by degrading HDAC7, thereby exerting proliferative inhibition in DLBCL. Our study broadened the understanding of the non-enzymatic functions of HDAC7 and underscored the importance of HDAC7 in the treatment of hematologic malignancies, particularly in DLBCL and AML.

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.

Objective To investigate the association between the Chinese visceral adiposity index (CVAI) and diabetes mellitus, hypertension, diabetes mellitus or hypertension, and diabetes with hypertension among elderly people in Hebei Province. Methods In 2020, a stratified multi-stage random sampling was used to conduct questionnaire survey, physical examination and laboratory detection among permanent residents of 10 monitoring sites in Hebei Province. Logistic regression was used to analyze the association between CVAI and diabetes mellitus, hypertension, diabetes mellitus or hypertension, and diabetes with hypertension. The area under the ROC curve (AUC) was used to evaluate the predictive value of CVAI for diabetes mellitus, hypertension, diabetes mellitus or hypertension, and diabetes with hypertension. Results The detection rates of diabetes mellitus, hypertension, diabetes mellitus or hypertension, and diabetes with hypertension were 19.8%, 74.6%, 78.2%, and 16.2%, respectively. Multivariate logistic regression analysis showed that compared with the lowest quartile of CVAI group Q1, the OR (95% CI) of diabetes mellitus, hypertension, diabetes mellitus or hypertension, and diabetes with hypertension in the highest quartile Q4 group were 3.55 (2.58~4.89), 2.52 (1.92~3.31), 3.09 (2.31~4.12), and 4.92 (3.40~7.12), respectively. The ROC curve results showed that CVAI had the best predictive value in the diagnosis of diabetes with hypertension, and the optimized critical values in males and females were 128.54 and 141.88, respectively. Conclusion The detection rates of diabetes mellitus and hypertension are high in the elderly population in Hebei Province. CVAI is positively associated with the risk of diabetes mellitus, hypertension, diabetes mellitus or hypertension, and diabetes with hypertension among the elderly in Hebei. CVAI has the strongest prediction ability for diabetes with hypertension.

OBJECTIVE: To evaluate the effects of acupuncture at Hegu (LI4), Taichong (LR3) and Sanyinjiao (SP6) on pain, anxiety, intrapartum blood loss, labor stage, and neonatal outcomes in primiparas. METHODS: One hundred primiparas were randomly divided into an acupuncture group (50 cases, 1 case was eliminated) and a control group (50 cases). The conventional obstetrical nursing was given in the control group. On the basis of the intervention in the control group, acupuncture was applied at bilateral Hegu (LI4), Taichong (LR3) and Sanyinjiao (SP6) in the acupuncture group. The delivery mode and labor stage, the scores of visual analogue scale (VAS) for uterine contraction pain and Hamilton anxiety scale (HAMA) before and after acupuncture, the intrapartum/postpartum blood loss and massive hemorrhage, as well as the neonatal Apgar score after 1, 5, and 10 min of birth, were compared in the two groups. RESULTS: The cesarean section rate was 4.1% (2/49) in the acupuncture group, which was superior to 10.0% (5/50) in the control group (P<0.05). In the acupuncture group, the time of latent phase of 2-cm cervical dilation, active phase, first and second stages of labor, and total labor stage was shorter than that in the control group (P<0.001), the intrapartum blood loss and massive hemorrhage rate were lower than those in the control group (P<0.001, P<0.05). After acupuncture, the VAS and HAMA scores were decreased compared with those before acupuncture in the acupuncture group (P<0.001), the VAS and HAMA scores were increased compared with those before acupuncture in the control group (P<0.001). In the acupuncture group, the VAS and HAMA scores after acupuncture were lower than those in the control group (P<0.001), the changes of the VAS and HAMA scores before and after acupuncture were larger than those in the control group (P<0.001). There were no statistical differences in neonatal Apgar scores between the two groups (P>0.05). CONCLUSION Acupuncture at Hegu (LI4), Taichong (LR3) and Sanyinjiao (SP6) can effectively alleviate the pain and anxiety, shorten the labor stage, reduce the intrapartum blood loss and incidence rate of massive hemorrhage, and promote spontaneous delivery, thereby enhancing maternal comfort and safety in primiparas.
Humans ; Female ; Pregnancy ; Acupuncture Points ; Acupuncture Therapy ; Adult ; Young Adult ; Labor, Obstetric ; Parity