Objective To track and evaluate the implementation and application of the occupational health standard Radiation shielding requirements for radiotherapy room—Part 4: Radiotherapy room of ²⁵²Cf neutron afterloading (GBZ/T 201.4-2015) by radiation health technical service agencies, medical institutions, health supervision agencies, and radiotherapy facility design units, and to provide a scientific basis for the further revision and implementation of this standard. Methods Following the Guideline for health standards tracking evaluation (WS/T 536-2017) and the project implementation plan, relevant practitioners were randomly selected for a questionnaire survey. The survey primarily focused on their awareness, standard training, application, and revision suggestions of GBZ/T 201.4-2015. The results were summarized and analyzed. Results A total of 168 evaluation questionnaires were collected from relevant practitioners in 28 provinces. Only 31.6% of the respondents reported being “well familiar” or “ familiar” with the standard, 27.4% of the respondents believed that the standard was widely used, and 45.2% of the respondents believed that the standard could meet the needs of their work. Only 14.9% of the respondents had received relevant training on the standard, more than half of the respondents had not applied the standard within the past 10 years, and 45.2% of the respondents believed that the standard "needs to be revised". Conclusion Due to the small number of californium-252 neutron afterloading radiotherapy devices in operation on the market, the overall awareness of the standard is low, suggesting that relevant authorities need to strengthen training and publicity of the standard, and that certain sections of the standard need to be revised or merged.

OBJECTIVE: To explore effectiveness of TiRobot-assisted screw implantation in the treatment of coracoid process fractures of the scapula. METHODS: A retrospective analysis was conducted on the clinical data from 24 patients with coracoid process fractures of the scapula admitted between September 2019 and January 2024 and met selection criteria. Among them, 12 patients underwent TiRobot-assisted screw implantation (robot group) and 12 underwent manual screw implantation (control group) during internal fixation. There was no significant difference ( P>0.05) in baseline data such as gender, age, body mass index, disease duration, cause of injury, coracoid process fracture classification, and proportion of patients with associated injuries between the two groups. The incision length, operation time, intraoperative blood loss, hospital stay, accuracy of screw placement, coracoid process fracture healing time, and complications were recorded and compared, as well as pain visual analogue scale (VAS) score, and Constant-Murley score at last follow-up. RESULTS: The intraoperative blood loss and incision length in the robot group were significantly lower than those in the control group ( P<0.05); however, there was no significant difference in operation time and hospital stay between the two groups ( P>0.05). All patients were followed up 8-27 months (mean, 17.5 months), and the difference in follow-up time between the two groups was not significant ( P>0.05). At last follow-up, the VAS score for shoulder pain in the robot group was signifncatly lower compared to the control group, and the Constant-Murley score was significantly higher ( P<0.05). In the robot group, 16 screws were implanted intraoperatively, while 13 screws were implanted in the control group. Radiographic re-evaluation showed that the excellent and good rate of screw implantation was higher in the robot group (93.8%, 15/16) than in the control group (61.5%, 8/13), but the difference in the precision of screw implantation between the two groups was not significant ( P>0.05). Four patients in the robot group and 1 in the control group achieved double screws fixation; however, the difference in achieving double screws fixation between the two groups was not significant ( P>0.05). All fractures healed in both groups with 1 case of malunion in the control group. There was no significant difference in healing time between the two groups ( P>0.05). During follow-up, 1 patient in the control group experienced screw loosening and displacement. There was no significant difference in the incidence of screw loosening and fracture malunion between the two groups ( P>0.05). CONCLUSION Compared with manual screw implantation, TiRobot-assisted minimally invasive treatment of coracoid process fractures of the scapula can reduce intraoperative blood loss, shorten incision length, alleviate pain, and obtain better promote shoulder joint functional recovery.
Humans ; Male ; Female ; Retrospective Studies ; Fracture Fixation, Internal/instrumentation* ; Minimally Invasive Surgical Procedures/instrumentation* ; Adult ; Middle Aged ; Fractures, Bone/surgery* ; Bone Screws ; Coracoid Process/surgery* ; Robotic Surgical Procedures/methods* ; Scapula/surgery* ; Treatment Outcome ; Operative Time ; Young Adult ; Length of Stay ; Blood Loss, Surgical

Objective:To explore the prospective associations between physical activity and incident ischemic stroke in adults.Methods:Data of China Kadoorie Biobank study in Tongxiang of Zhejiang were used. After excluding participants with cancers, strokes, heart diseases and diabetes at baseline study, a total of 53 916 participants aged 30-79 years were included in the final analysis. The participants were divided into 5 groups according to the quintiles of their physical activity level. Cox proportional hazard regression models was used to calculate the hazard ratios ( HR) for the analysis on the association between baseline physical activity level and risk for ischemic stroke. Results:The total physical activity level in the participants was (30.63±15.25) metabolic equivalent (MET)-h/d, and it was higher in men [(31.04±15.48) MET-h/d] than that in women [(30.33±15.07) MET-h/d] ( P<0.001). In 595 526 person-years of the follow-up (average 11.4 years), a total of 1 138 men and 1 082 women were newly diagnosed with ischemic stroke. Compared to participants with the lowest physical activity level (<16.17 MET-h/d), after adjusting for socio-demographic factors, lifestyle, BMI, waist circumference, and SBP, the HRs for the risk for ischemic stroke in those with moderate low physical activity level (16.17-24.94 MET-h/d), moderate physical activity level (24.95-35.63 MET-h/d), moderate high physical activity level (35.64-43.86 MET-h/d) and the highest physical activity level (≥43.87 MET-h/d) were 0.93 (95% CI: 0.83-1.04), 0.87 (95% CI: 0.76-0.98), 0.82 (95% CI: 0.71-0.95) and 0.76 (95% CI: 0.64-0.89), respectively. Conclusion:Improving physical activity level has an effect on reducing the risk for ischemic stroke.

Purpose/Significance The paper studies the co-fund patterns and effectiveness of research fund from the perspective of organization types,and provides theoretical reference for improving medical research funding system in China.Method/Process It takes the field of cervical cancer research as an example,dividing research funds into 5 types:government funds,academic funds,enterprise funds,medical institution funds,and social organization funds.The effectiveness of co-funding is studied based on journal impact index(JII),annual citation index(ACI),annual usage index(AUI)and comprehensive effectiveness score,and pattern recognition and a-nalysis are carried out based on the indicator results.Result/Conclusion The comprehensive effectiveness score of co-funding by univer-sities,enterprises,and social organizations is the highest.Research funding in the medical field can be based on specific research goals and combined multiple funds to achieve overlapping effects and improving the effectiveness of output results.

Objective     To recognize the different phases of Korotkoff sounds through deep learning technology, so as to improve the accuracy of blood pressure measurement in different populations. Methods     A classification model of the Korotkoff sounds phases was designed, which fused attention mechanism (Attention), residual network (ResNet) and bidirectional long short-term memory (BiLSTM). First, a single Korotkoff sound signal was extracted from the whole Korotkoff sounds signals beat by beat, and each Korotkoff sound signal was converted into a Mel spectrogram. Then, the local feature extraction of Mel spectrogram was processed by using the Attention mechanism and ResNet network, and BiLSTM network was used to deal with the temporal relations between features, and full-connection layer network was applied in reducing the dimension of features. Finally, the classification was completed by SoftMax function. The dataset used in this study was collected from 44 volunteers (24 females, 20 males with an average age of 36 years), and the model performance was verified using 10-fold cross-validation. Results     The classification accuracy of the established model for the 5 types of Korotkoff sounds phases was 93.4%, which was higher than that of other models. Conclusion     This study proves that the deep learning method can accurately classify Korotkoff sounds phases, which lays a strong technical foundation for the subsequent design of automatic blood pressure measurement methods based on the classification of the Korotkoff sounds phases.

Currently, in precision cardiac surgery, there are still some pressing issues that need to be addressed. For example, cardiopulmonary bypass remains a critical factor in precise surgical treatment, and many core aspects still rely on the experience and subjective judgment of cardiopulmonary bypass specialists and surgeons, lacking precise data feedback. With the increasing elderly population and rising surgical complexity, precise feedback during cardiopulmonary bypass becomes crucial for improving surgical success rates and facilitating high-complexity procedures. Overcoming these key challenges requires not only a solid medical background but also close collaboration among multiple interdisciplinary fields. Establishing a multidisciplinary team encompassing professionals from the medical, information, software, and related industries can provide high-quality solutions to these challenges. This article shows several patents from a collaborative medical and electronic information team, illustrating how to identify unresolved technical issues and find corresponding solutions in the field of precision cardiac surgery while sharing experiences in applying for invention patents.

Medication during pregnancy is widespread, but there are few reports on its fetal safety. Recent studies suggest that medication during pregnancy can affect fetal morphological and functional development through multiple pathways, multiple organs, and multiple targets. Its mechanisms involve direct ways such as oxidative stress, epigenetic modification, and metabolic activation, and it may also be indirectly caused by placental dysfunction. Further studies have found that medication during pregnancy may also indirectly lead to multi-organ developmental programming, functional homeostasis changes, and susceptibility to related diseases in offspring by inducing fetal intrauterine exposure to too high or too low levels of maternal-derived glucocorticoids. The organ developmental toxicity and programming alterations caused by medication during pregnancy may also have gender differences and multi-generational genetic effects mediated by abnormal epigenetic modification. Combined with the latest research results of our laboratory, this paper reviews the latest research progress on the developmental toxicity and functional programming alterations of multiple organs in offspring induced by medication during pregnancy, which can provide a theoretical and experimental basis for rational medication during pregnancy and effective prevention and treatment of drug-related multiple fetal-originated diseases.

Objective To understand the current situation and explore the influencing factors of delay in seeking medical treatment for common symptoms of residents in the rural areas of Sichuan province. Methods In July 2019,multi-stage random sampling was carried out in Zigong city,Sichuan province,and the data were collected by face-to-face questionnaire interview.The residents who had lived at hometown for more than half a year in the past year and had seen a doctor in the most recent month were surveyed.Logistic regression was adopted to predict the influencing factors of delay in seeking medical treatment. Results A total of 342 subjects were enrolled,and the incidence of delay in seeking medical treatment was 13.45%(46/342).Compared with the young and middle-aged(<65 years)people,the elderly(≥65 years)people were more likely to have delay in seeking medical treatment (OR=2.187,95%CI=1.074-4.457,P=0.031).The rural residents who gave higher score of the overall quality of township health centers were less likely to have delay in seeking medical treatment (OR=0.854,95%CI=0.735-0.992,P=0.039). Conclusions The occurrence of delay in seeking medical treatment for common symptoms of rural residents in Sichuan province is low.Age and the overall quality evaluation of township health centers affect the occurrence of delay in medical treatment among the rural residents in Sichuan province.Efforts should be made to improve the awareness of disease prevention among the elderly in rural areas.The investment in health resources in township health centers should be increased to strengthen the introduction and training of talents.These measures can improve the health services in township health centers,guide residents to make timely use of health resources,and reduce the occurrence of delay in seeking medical treatment.
Middle Aged ; Aged ; Humans ; Surveys and Questionnaires ; Logistic Models ; Rural Population ; China/epidemiology*

bjective    To use machine learning technology to predict the recurrence of atrial fibrillation (AF) after radiofrequency ablation, and try to find the risk factors affecting postoperative recurrence. Methods    A total of 300 patients with valvular AF who underwent radiofrequency ablation in West China Hospital and its branch (Shangjin Hospital) from January 2017 to January 2021 were enrolled, including 129 males and 171 females with a mean age of 52.56 years. We built 5 machine learning models to predict AF recurrence, combined the 3 best performing models into a voting classifier, and made prediction again. Finally, risk factor analysis was performed using the SHApley Additive exPlanations method. Results    The voting classifier yielded a prediction accuracy rate of 75.0%, a recall rate of 61.0%, and an area under the receiver operating characteristic curve of 0.79. In addition, factors such as left atrial diameter, ejection fraction, and right atrial diameter were found to have an influence on postoperative recurrence. Conclusion    Machine learning-based prediction of recurrence of valvular AF after radiofrequency ablation can provide a certain reference for the clinical diagnosis of AF, and reduce the risk to patients due to ineffective ablation. According to the risk factors found in the study, it can provide patients with more personalized treatment.

Objective     To establish a machine learning based framework to rapidly screen out high-risk patients who may develop atrial fibrillation (AF) from patients with valvular heart disease and provide the information related to risk prediction to clinicians as clinical guidance for timely treatment decisions. Methods     Clinical data were retrospectively collected from 1 740 patients with valvular heart disease at West China Hospital of Sichuan University and its branches, including 831 (47.76%) males and 909 (52.24%) females at an average age of 54 years. Based on these data, we built classical logistic regression, three standard machine learning models, and three integrated machine learning models for risk prediction and characterization analysis of AF. We compared the performance of machine learning models with classical logistic regression and selected the best two models, and applied the SHAP algorithm to provide interpretability at  the population and single-unit levels. In addition, we provided visualization of feature analysis results. Results    The Stack model performed best among all models (AF detection rate 85.6%, F1 score 0.753), while XGBoost outperformed the standard machine learning models (AF detection rate 71.9%, F1 score 0.732), and both models performed significantly better than the logistic regression model (AF detection rate 65.2%, F1 score 0.689). SHAP algorithm showed that left atrial internal diameter, mitral E peak flow velocity (Emv), right atrial internal diameter output per beat, and cardiac function class were the most important features affecting AF prediction. Both the Stack model and XGBoost had excellent predictive ability and interpretability. Conclusion    The Stack model has the highest AF detection performance and comprehensive performance. The Stack model loaded with the SHAP algorithm can be used to screen high-risk patients for AF and reveal the corresponding risk characteristics. Our framework can be used to guide clinical intervention and monitoring of AF.