Objective To elucidate how the overexpression of cystathionine-β-synthase(CBS)plays an antihypertensive role by affecting peroxisome proliferator-activated receptor γ coactivator-1α(PGC-1α)expression.Methods The adeno-associated viruses(AAVs),ones that overexpressed CBS,and another knocked down PGC-1α,were injected into the hypothalamic paraventricular nucleus(PVN)of spontaneously hypertensive rats(SHRs).The rats'blood pressure was monitored,and the level of norepinephrine(NE)was examined by ELISA;PVN inflammatory response,oxidative stress and tyrosine hydroxylase(TH)expression were detected with RT-qPCR and immunofluorescence.Results PVN overexpression of CBS could increase the transcription level of CBS(by 3.8 times,P＜0.05)and PGC-1α(by 1.6 times,P＜0.05)in PVN of SHR.PVN overexpression of CBS could reduce blood pressure in SHR(from 177.81 mmHg to 128.77 mmHg,P＜0.001),but PVN knockdown of PGC-1αweakened such effect(from 128.77 mmHg to 152.79 mmHg,P＜0.05).PVN overexpression of CBS could alleviate PVN inflammatory response and oxidative stress,but this effect was weakened or even eliminated when knocking down PGC-1α was performed at the same time.Conclusion PVN overexpression of CBS can reduce blood pressure in SHR,and this effect may be achieved by increasing the transcriptional level of PGC-1α,alleviating PVN inflammatory response,oxidative stress,and improving sympathetic nerve excitation.

ObjectiveTo investigate the trends in diabetes mortality and potential years of life lost (PYLL) among residents of Huangpu District, Shanghai from 1993 to 2021, to analyze the long-term trends of diabetic patients with different characteristics and to provide a reference for scientific prevention and control of diabetes in aging urban areas. MethodsDiabetes mortality data were obtained from the Huangpu District cause of death registration records in the Shanghai death cause registration system. Indicators such as crude mortality rate, standardized mortality rate, potential years of life lost (PYLL), average years of life lost (AYLL), annual percentage change (APC), and average annual percentage change (AAPC) were used to analyze diabetes-related mortality and life loss. Statistical analyses were performed using software SPSS 21.0 and Joinpoint 5.0.2. ResultsFrom 1993 to 2021, the average annual crude mortality rate of diabetes in Huangpu District was 46.56/100 000, and the average annual standardized mortality rate was 20.44/100 000. The crude mortality rate and standardized mortality rate of diabetes for female residents were higher than those for males. The crude mortality rate showed an overall increasing trend [AAPC=2.81% (95%CI: 0.20%‒5.49%), P<0.05], while the increase in standardized mortality rate significantly slowed [AAPC=0.15% (95%CI: -2.27%‒2.63%)], P<0.05]. The mortality rate rose rapidly in the 70‒74 years age group and peaked in the 85‒ years age group (607.69/100 000). Diabetes accounted for a cumulative PYLL of22 741 person-years, with an average annual AYLL of 1.88 years and an average annual potential years of life lost rate (PYLLR) of 0.82‰. Male residents had higher PYLL, AYLL, and PYLLR than females. ConclusionDiabetes mortality rates in Huangpu District have increased year by year, resulting in significant life loss. However, the age-standardized mortality rate increase has markedly slowed. Efforts should focus on elderly diabetic patients aged ≥70 years, by leveraging platforms such as community-based chronic disease health support centers, efforts should be made to enhance diabetes screening service for middle-aged and elderly residents. Consequently, elderly diabetic patients’ awareness of diabetes and responce to related complications is improved, which would be conducive to controling the progression of complications and reducing the mortolity risk of diabetes.

This study aims to develop an abdominal acupoint localization system based on computer vision and convolutional neural networks (CNNs). To address the challenge of abdominal acupoint localization, a multi-task CNNs architecture was constructed and trained to locate the Shenque (CV8) and human body boundaries. Based on the identified Shenque (CV8), the system further deduces key characteristics of four acupoints: Shangwan (CV13), Qugu (CV2), and bilateral Daheng (SP15). An affine transformation matrix is applied to accurately map image coordinates to an acupoint template space, achieving precise localization of abdominal acupoints. Testing has verified that this system can accurately identify and locate abdominal acupoints in images. The development of this localization system provides technical support for TCM remote education, diagnostic assistance, and advanced TCM equipment, such as intelligent acupuncture robots, facilitating the standardization and intelligent advancement of acupuncture.
Acupuncture Points ; Humans ; Deep Learning ; Abdomen/diagnostic imaging* ; Neural Networks, Computer ; Acupuncture Therapy ; Image Processing, Computer-Assisted

With the emergence of deep learning techniques based on convolutional neural networks, artificial intelligence (AI) has driven transformative developments in the field of medical image analysis. Recently, large language models (LLMs) such as ChatGPT have also started to achieve distinction in this domain. Increasing research shows the undeniable role of AI in reshaping various aspects of medical image analysis, including processes such as image enhancement, segmentation, detection in image preprocessing, and postprocessing related to medical diagnosis and prognosis in clinical settings. However, despite the significant progress in AI research, studies investigating the recent advances in AI technology in the aforementioned aspects, the changes in research hotspot trajectories, and the performance of studies in addressing key clinical challenges in this field are limited. This article provides an overview of recent advances in AI for medical image analysis and discusses the methodological profiles, advantages, disadvantages, and future trends of AI technologies.
Artificial Intelligence ; Humans ; Image Processing, Computer-Assisted/methods* ; Neural Networks, Computer ; Deep Learning ; Diagnostic Imaging/methods*

Objective:To analyze the association between inflammation-related dietary patterns and the risk for cognitive impairment in older adults aged ≥65 years in longevity areas in China by using reduced rank regression (RRR) analysis.Methods:This study used cross-sectional data from the 2021 Healthy Aging and Biomarkers Cohort Study, including the information about study participants' demographic characteristics, lifestyles, daily life activities, and disease histories. Dietary intake was obtained by using a simplified food frequency questionnaire. Cognitive impairment was evaluated based on the Mini-Mental State Examination Scale combined with years of education. Fasting venous blood samples were collected to detect inflammatory markers, especially high-sensitivity C-reactive protein (hs-CRP) and the platelet-to-lymphocyte ratio (PLR). RRR analysis was used to obtain inflammation-related dietary patterns using hs-CRP and PLR as response variables. Multivariate logistic regression model was used to analyze the association between dietary pattern score and the risk for cognitive impairment. Restricted cubic spline was used to explore the dose response relationship, and mediation analysis was used to quantify the mediating effects of hs-CRP and PLR.Results:Two dietary patterns were identified with RRR. The primary pattern was characterized by higher intakes of flour, red meat, and dairy products, and lower intake of fresh vegetables, explaining 6.84% of the variance in food intake and 0.50% of the variance in inflammatory markers. Compared with the T1 group, the T3 group had significantly higher risk for cognitive impairment ( OR=1.242, 95% CI: 1.034-1.491). Each one standard deviation increase in the dietary pattern score was associated with an 8.7% increase in the risk for cognitive impairment ( OR=1.087, 95% CI: 1.008-1.172), with a significant linear trend (overall-model P<0.001, non-linear P=0.295). Mediation analysis indicated that hs-CRP mediated 6.2% of the association between the dietary pattern and the risk for cognitive impairment. Conclusion:The inflammation- related dietary pattern characterized by higher consumption of flour, red meat, and dairy products and lower consumption of fresh vegetables is associated with an increased risk for cognitive impairment in older adults, and hs-CRP partially mediates this association.

Objective:To investigate the relationship between dietary behavior and sarcopenia in older adults aged ≥65 years in longevity areas of China based on latent class analysis.Methods:A total of 4 358 older adults aged ≥65 years were selected from the 2021 Healthy Aging and Biomarkers Cohort Study. The information about their demographic characteristics, lifestyles, and chronic disease histories were collected. A simplified food frequency questionnaire was used to collect information about their dietary intake in the last month. The food intake frequency and food category score were calculated, and the higher the food category score, the richer the dietary intake. Latent class analysis was used to identify the latent classes of the dietary behavior. Sarcopenia was diagnosed using the SARC-CalF. Multivariate logistic regression model was used to analyze the association of food category scores and different latent classes of the dietary behavior with the risk for sarcopenia.Results:In 4 358 older adults, 1 841 (42.24%) had sarcopenia. The frequencies of intakes of cereals and potatoes, vegetable and fruit, meat and bean products were lower in the sarcopenia group than in the non-sarcopenia group. The risk for sarcopenia decreased with the increase of food category score in older adults ( OR=0.850, 95% CI: 0.796-0.907). Latent class analysis identified 4 latent classes of the dietary behavior. Compared with those with class 1 (frequency of intake of all 5 food species was higher probability in T3 group), those with class 2 (frequency of intake of vegetables and fruits and energy-only foods were less likely to be in the T3 group) and class 3 (frequency of intake of all 5 food species was lower probability in T3 group) had significantly increased risk for sarcopenia ( OR=1.377, 95% CI: 1.131-1.676) and ( OR=1.354, 95% CI: 1.091-1.680), 37.7% and 35.4% increased risk for sarcopenia, respectively. Conclusion:Increasing dietary intake category and sufficient intake of various foods for a balanced dietary pattern can reduce the risk of sarcopenia in older adults.

To analyze the impact of salt reduction interventions on the knowledge, attitude and behavior regarding the salt reduction of students′ parents based on the home-school interaction model. In April 2021, parents of students in grades 3-5 from three primary schools in Yichang City were selected as the target population using a cluster sampling method, and the parent population was divided into an intervention group and a control group. In the intervention group, a comprehensive home-school interaction salt reduction intervention was implemented, and in the control group, no intervention measures were taken for students′ parents. Baseline and final surveys were conducted before and after the intervention period, which included general information, previous salt reduction interventions received, and salt reduction knowledge, attitude and behavior. Difference-in-difference (DID) method was used to compare the knowledge, attitude and behavior status of two groups before and after the intervention, and stratified analysis of parents with different literacy levels was conducted to assess the net effect of intervention implementation. The results showed that 740 parents completed the baseline and final surveys, with 231 in the intervention group and 509 in the control group. After propensity score matching, there were 231 (33.33%) in the intervention group and 462 (66.67%) in the control group. After the intervention, the proportion of the intervention group who obtained salt control spoons and pots, as well as salt reduction knowledge and advice through school, was 87.45%, 86.58% and 75.45%, respectively, which was significantly higher than that in the control group ( P<0.05). After the intervention, the proportion of parents with a high school and lower education who obtained salt control pots was higher in the intervention group (89.23%) than in the control group (74.49%), with significant differences ( P<0.05). The proportion of parents with a college degree or above who obtained salt control spoons and pots, as well as salt reduction knowledge and advice through school, was higher than that of the control group ( P<0.05). The results of DID method showed that after controlling for monthly income and other factors, the scores of parents′ salt reduction-related knowledge and low-salt behavior in the intervention group increased significantly higher than those in the control group, with DID values (95% CI) of 1.18 (0.15-2.21) and 0.62 (0.16-1.09), respectively, indicating a significant net effect of intervention implication. After stratification according to the education level of parents, this difference still existed in the college degree or above group, with DID values (95% CI) of 1.39 (0.13-2.66) and 0.76 (0.16-1.36), respectively. The home-school interaction model for salt reduction measures can improve the salt-related knowledge and low-salt behavioral choices of students′ parents.

Objective Focusing on gynecological surgery,we constructed a prediction model for surgical duration by extracting features from unstructured surgical planning texts and integrating multimodal data via artificial intelligence technology.Methods The clinical data of 34 614 patients who underwent gynecologic surgeries at West China Second University Hospital,Sichuan University between January 2022 and October 2024 were collected.An embedding-transformer model was constructed to convert surgical planning texts into a one-dimensional numerical feature,referred to as the step feature.The predictive value of the step feature was assessed by comparing the performance improvements of linear regression,random forest,eXtreme Gradient Boosting(XGBoost),support vector regression,K-nearest neighbor regression,and artificial neural network algorithms in two scenarios—with and without the step feature as an input.The out-of-sample prediction accuracy of the models was assessed using mean absolute error(MAE),root mean squared error(RMSE),and R-squared(R2).Furthermore,the model interpretability was examined using SHapley Additive exPlanations(SHAP)values.Results SHAP results showed that the step feature had the highest predictive contribution.Temporal factors in surgical scheduling also influenced gynecological surgery duration.The XGBoost model demonstrated optimal performance on the test set,significantly improving prediction accuracy with a 40.43%increase in R2,while reducing MAE and RMSE by 21.27%and 20.13%,respectively,compared to the baseline model without the step feature.Conclusion The embedding-transformer model developed in this study effectively extracts features from surgical planning texts and enhances the predictive performance of machine learning models.The XGBoost prediction model can assist hospital administrators in implementing more refined management of gynecological surgeries and improving the utilization efficiency of surgical resources.

Objective:To evaluate the clinical outcomes of endoscopic-assisted polyether ether ketone (PEEK) patient-specific implant (PSI) revision for over-resected mandibles caused by the mandibular angle osteotomy.Methods:A retrospective analysis was conducted on 24 patients [8 males, 16 females, aged 19-57 (32.5±9.5) years] with 39 over-resected mandibles that underwent PEEK-PSI mandibular angle revision surgery at the Affiliated Friendship Plastic Surgery Hospital of Nanjing Medical University from January 2019 to December 2023. Preoperative cone-beam computed tomography (CBCT) data were used to design and fabricate customized PEEK PSIs based on individual anatomical requirements. An intraoral incision approach with endoscopic assistance was employed to meticulously dissect soft tissue attachment around the angle region, followed by the implantation of a customized PEEK PSI. Postoperative CBCT scans were performed for 3D reconstruction, with root mean square error (RMSE) and maximum deviation (MaxD) as accuracy metrics. Patients′ satisfaction was assessed preoperatively and ≥6 months postoperatively using the face questionnaire (FACE-Q) scores, which included overall facial appearance, lower face and jawline, appearance distress, psychological health and social function.Results:All 24 patients achieved satisfactory recovery with primary healing of intraoral incisions. No complications such as infection, nerve injury, or implant rejection occurred during follow-up period. Patients′ facial appearance and jaw line contouring were significantly improved. Fine anatomical fitting between PEEK-PSI and defect areas was observed: RMSE ranged from 0.117 to 0.315 mm, and MaxD was (5.485±1.300) mm. FACE-Q scores demonstrated significant improvements after surgery in overall facial appearance [(49.8±5.4) vs (65.0±5.3) scores], lower face and jawline [(42.5±5.3) vs (56.1±4.6) scores], appearance distress [(60.0±6.9) vs (70.6±6.5) scores], psychological health [(62.0±5.0) vs (70.8±5.3) scores], and social function [(60.3±4.3) vs (69.3±5.8) scores] (all P<0.001). Conclusion:Endoscopic-assisted PEEK-PSI revision for over-resected mandibles following mandibular angle osteotomy exhibits high surgical precision and safety, effectively restoring mandibular contour and significantly enhancing patients′ satisfaction.

Objective:To investigate the clinical outcomes of precapsular pocket repositioning for correcting implant malposition following augmentation mammoplasty.Methods:A retrospective analysis was conducted on 29 female patients aged 25-37 (28.8±3.4) years who underwent precapsular pocket repositioning at the Affiliated Friendship Plastic Surgery Hospital of Nanjing Medical University from December 2015 to August 2024. The surgical technique involved preserving the original capsule, creating a new implant pocket anterior to the capsule, and closing the original capsular space. Postoperative complications were recorded, and breast satisfaction was evaluated preoperatively and at 6 months postoperatively using the BREAST-questionnaire (BREAST-Q).Results:All 29 patients successfully underwent precapsular pocket repositioning with primary wound healing. During the follow-up period, all patients were satisfied with the correction of implant malposition. The mean BREAST-Q score improved significantly from (43.56±3.17) scores preoperatively to (72.56±13.49) scores at 6 months postoperatively ( P<0.001). No hematoma, implant rupture, recurrent malposition, capsular contracture, or surgical site infection occurred in any patient. Conclusion:Precapsular pocket repositioning provides favorable clinical outcomes for patients with implant malposition after augmentation mammoplasty, and there are no severe complications .