OBJECTIVES: To construct an intelligent analysis model for classifying fetal orientation during intrapartum ultrasound videos based on multi-feature fusion. METHODS: The proposed model consists of the Input, Backbone Network and Classification Head modules. The Input module carries out data augmentation to improve the sample quality and generalization ability of the model. The Backbone Network was responsible for feature extraction based on Yolov8 combined with CBAM, ECA, PSA attention mechanism and AIFI feature interaction module. The Classification Head consists of a convolutional layer and a softmax function to output the final probability value of each class. The images of the key structures (the eyes, face, head, thalamus, and spine) were annotated with frames by physicians for model training to improve the classification accuracy of the anterior occipital, posterior occipital, and transverse occipital orientations. RESULTS: The experimental results showed that the proposed model had excellent performance in the tire orientation classification task with the classification accuracy reaching 0.984, an area under the PR curve (average accuracy) of 0.993, and area under the ROC curve of 0.984, and a kappa consistency test score of 0.974. The prediction results by the deep learning model were highly consistent with the actual classification results. CONCLUSIONS The multi-feature fusion model proposed in this study can efficiently and accurately classify fetal orientation in intrapartum ultrasound videos.
Humans ; Female ; Ultrasonography, Prenatal/methods* ; Pregnancy ; Fetus/diagnostic imaging* ; Neural Networks, Computer ; Video Recording

BACKGROUND:In recent years,artificial intelligence has been increasingly integrated into the diagnosis and treatment of lumbar disc herniation,enhancing the accuracy and efficiency of diagnostic procedures and diversifying therapeutic approaches.This integration has positioned artificial intelligence as a burgeoning focal point within the field.OBJECTIVE:To provide a comprehensive overview of the current applications of artificial intelligence in the diagnosis and treatment of lumbar disc herniation,to analyze the limitations of the relevant technologies.METHODS:A systematic computer-assisted literature search was performed in PubMed,CNKI,WanFang Database,and VIP Database for relevant publications regarding the application of artificial intelligence in the diagnosis and treatment of lumbar disc herniation from database inception up to August 2024.The search keywords included"lumbar disc herniation,artificial intelligence,machine learning,deep learning,big data,robot,neural network,model,algorithm."A total of 101 articles were selected based on predefined inclusion criteria and were reviewed.RESULTS AND CONCLUSION:Different artificial intelligence technologies have propelled the intelligent treatment of lumbar disc herniation,showing great potential for future development.Deep learning technology,based on advanced algorithms,constructs corresponding learning models to optimize the processing of X-ray,CT,and MRI images,achieving precise localization,identification,and analysis of degenerated intervertebral discs,and improving the accuracy of automated diagnosis.Big data technology utilizes data platforms to analyze vast medical data,simulate disease development trends,and provide a new perspective for disease assessment and prediction.Surgical robots,combined with robotic arms,3D high-definition vision systems,and 5G communication technology,support remote precise surgical operations,demonstrating significant technological advantages.In the future,the diagnosis and treatment of lumbar disc herniation by artificial intelligence will evolve towards standardization,efficiency,and precision through continuous optimization of algorithms and the professional development of data platforms.

Drynaria fortunei,commonly known as"bone setting herb",has been widely included in various traditional Chinese herb-al classics for treating bone injuries.It is used medicinally from its rhizome,which has a bitter taste and warm property.It is known to nourish the kidneys,strengthen bones,and alleviate pain from injuries.The chemical constituents mainly include flavonoids,phenylpro-panoids,triterpenoids,phenolic acids,lignans,and sterols.Modern medical research indicates that Drynaria fortunei has anti-osteoporo-sis effects,promotes fracture healing,has anti-inflammatory properties,and benefits dental health.This article reviews the historical use of Drynaria fortunei and recent research on its chemical composition and pharmacological effects,summarizing some of the mechanisms of action.The aim is to provide a reference for further research on this medicinal herb.

Drynaria fortunei,commonly known as"bone setting herb",has been widely included in various traditional Chinese herb-al classics for treating bone injuries.It is used medicinally from its rhizome,which has a bitter taste and warm property.It is known to nourish the kidneys,strengthen bones,and alleviate pain from injuries.The chemical constituents mainly include flavonoids,phenylpro-panoids,triterpenoids,phenolic acids,lignans,and sterols.Modern medical research indicates that Drynaria fortunei has anti-osteoporo-sis effects,promotes fracture healing,has anti-inflammatory properties,and benefits dental health.This article reviews the historical use of Drynaria fortunei and recent research on its chemical composition and pharmacological effects,summarizing some of the mechanisms of action.The aim is to provide a reference for further research on this medicinal herb.

BACKGROUND:In recent years,artificial intelligence has been increasingly integrated into the diagnosis and treatment of lumbar disc herniation,enhancing the accuracy and efficiency of diagnostic procedures and diversifying therapeutic approaches.This integration has positioned artificial intelligence as a burgeoning focal point within the field.OBJECTIVE:To provide a comprehensive overview of the current applications of artificial intelligence in the diagnosis and treatment of lumbar disc herniation,to analyze the limitations of the relevant technologies.METHODS:A systematic computer-assisted literature search was performed in PubMed,CNKI,WanFang Database,and VIP Database for relevant publications regarding the application of artificial intelligence in the diagnosis and treatment of lumbar disc herniation from database inception up to August 2024.The search keywords included"lumbar disc herniation,artificial intelligence,machine learning,deep learning,big data,robot,neural network,model,algorithm."A total of 101 articles were selected based on predefined inclusion criteria and were reviewed.RESULTS AND CONCLUSION:Different artificial intelligence technologies have propelled the intelligent treatment of lumbar disc herniation,showing great potential for future development.Deep learning technology,based on advanced algorithms,constructs corresponding learning models to optimize the processing of X-ray,CT,and MRI images,achieving precise localization,identification,and analysis of degenerated intervertebral discs,and improving the accuracy of automated diagnosis.Big data technology utilizes data platforms to analyze vast medical data,simulate disease development trends,and provide a new perspective for disease assessment and prediction.Surgical robots,combined with robotic arms,3D high-definition vision systems,and 5G communication technology,support remote precise surgical operations,demonstrating significant technological advantages.In the future,the diagnosis and treatment of lumbar disc herniation by artificial intelligence will evolve towards standardization,efficiency,and precision through continuous optimization of algorithms and the professional development of data platforms.

Sleep deprivation refers to the loss of sleep caused by self-inflicted or external factors. There is increasing evidence that pregnancy is prone to sleep deprivation, which not only disrupts maternal functions but also affects offspring′s cognitive function. This article reviews the effects of sleep deprivation during pregnancy on offspring cognition and its underlying mechanisms, in order to establish a foundation for developing scientifically sound sleep strategies during pregnancy and to provide clinical insights for improving the neurodevelopment and cognitive function of offspring.

This study was designed to evaluate the correlation between immune antibodies and pelvic inflammatory disease(PID)using retrospective analysis.Cases were selected from 171 patients who met the diagnosis of PID in Liuzhou People's Hospital of Guangxi Province from January 2022 to March 2023,and the PID patients were further divided into simple PID group(53 cases)and in PID combined with reproductive tract infection group(118 cases)according to the presence or absence of reproductive tract infections,while 83 cases of women who did not meet the specific diagnostic criteria of PID and did not have reproductive tract infections were selected as the control group during the same period.The positive rate of immune antibodies in the three groups were observed and compared to explore the relationship between immune antibodies and PID.Data showed that the positive rates of immune antibodies were significantly higher in the PID alone group and the PID combined with reproductive tract infection group than that in the control group.Furthermore,the positive rate of immune antibody TPOAb was significant difference in the PID combined with reproductive tract infection group and the PID alone group(P<0.05).In conclusion,TPOAb is closely associated with reproductive tract infections.

Objective:To explore the impact of sleep duration, physical exercise, and their interactions on the risk of dyslipidemia in older adults aged ≥80 (the oldest old) in China.Methods:The study subjects were the oldest old from four rounds of Healthy Aging and Biomarkers Cohort Study (2008-2009, 2011-2012, 2014 and 2017-2018). The information about their demographic characteristics, lifestyles, physical examination results and others were collected, and fasting venous blood samples were collected from them for blood lipid testing. Competing risk model was used to analyze the causal associations of sleep duration and physical exercise with the risk for dyslipidemia. Restricted cubic spline (RCS) function was used to explore the dose-response relationship between sleep duration and the risk for dyslipidemia. Additive and multiplicative interaction model were used to explore the interaction of sleep duration and physical exercise on the risk for dyslipidemia.Results:The average age of 1 809 subjects was (93.1±7.7) years, 65.1% of them were women. The average sleep duration of the subjects was (8.0±2.5) hours/day, 28.1% of them had sleep duration for less than 7 hours/day, and 27.2% had sleep for duration more than 9 hours/day at baseline survey. During the 9-year cumulative follow-up of 6 150.6 person years (follow-up of average 3.4 years for one person), there were 304 new cases of dyslipidemia, with an incidence density of 4 942.6/100 000 person years. The results of competitive risk model analysis showed that compared with those who slept for 7-9 hours/day, the risk for dyslipidemia in oldest old with sleep duration >9 hours/day increased by 22% ( HR=1.22, 95% CI: 1.07-1.39). Compared with the oldest old having no physical exercise, the risk for dyslipidemia in the oldest old having physical exercise decreased by 33% ( HR=0.67, 95% CI: 0.57-0.78). The RCS function showed a linear positive dose-response relationship between sleep duration and the risk for hyperlipidemia. The interaction analysis showed that physical exercise and sleep duration had an antagonistic effect on the risk for hyperlipidemia. Conclusion:Physical exercise could reduce the adverse effects of prolonged sleep on blood lipids in the oldest old.

Objective:To describe the current situation of mental health service utilization of community pa-tients with five mental disorders in Inner Mongolia Autonomous Region and provide reference for health education and formulating relevant policies.Methods:The multistage stratified sampling method with unequal probability was used to select a total of 12 315 community residents aged 18 and over in Inner Mongolia Autonomous Region.Using Composite International Diagnostic Interview,mood disorders,anxiety disorders,substance use disorders,intermit-tent explosive disorders,and eating disorders,and health service utilization were investigated.Descriptive statistics was completed by single factor analysis method.Results:The lifetime rates of consultation and treatment of any mental disorder were 18.7％and 10.2％,respectively.The highest proportion of patients received treatment by non-medical professionals was 31.4％,followed by psychiatrists in psychiatric hospital or psychologists in general hospitals.Among the patients,1.1％of them received medication,and 2.5％received psychotherapy.Conclusion:The utilization rate of mental health services in community patients with five mental disorders is relatively low.It is necessary to conduct health education for medical help seeking properly.

Objective:To explore changes of acid-base metabolism in the brain tissue of patients with chronic ischemic cerebrovascular disease (CICVD) using MRI amide proton transfer-weighted (APTw) imaging.Methods:This was a cross-sectional study. From January 2021 to July 2022, thirty-nine patients with CICVD at West China Hospital, Sichuan University were retrospectively included. All patients received CT perfusion (CTP) and APTw imaging. NeuBrainCARE brain perfusion software was used to analyze the impaired perfusion sites and measure the mean transit time (MTT) and time to peak (TTP). Standard spatial matching between CTP and APTw images was performed to measure the APTw values of the same sites. For comparison with normal tissue, APTw values were measured for normal-appearing white matter (NAWM) in the ipsilateral cerebral hemisphere, the contralateral cerebral hemisphere, and the ipsilateral cerebellar hemisphere in areas of impaired perfusion. ANOVA was used to compare the APTw values of impaired perfusion brain tissue, ipsilateral cerebral NAWM, contralateral cerebral NAWM, and ipsilateral cerebellar NAWM. The Bonferroni method was used to correct for multiple comparisons. Pearson correlation coefficient was used to analyze the correlation between APTw values and MTT and TTP in the cerebral tissue with impaired perfusion.Results:In 39 patients with CICVD, both the mean and minimum APTw values of cerebral tissue with impaired perfusion were significantly lower than those in the NAWM of the ipsilateral cerebral hemisphere, the contralateral cerebral hemisphere, and the ipsilateral cerebellar hemisphere ( P<0.001). In the NAWM of the cerebellar hemispheres with unimpaired perfusion, both the mean and minimum APTw values were significantly higher than those in the ipsilateral cerebral hemispheres and the contralateral cerebral hemisphere ( P<0.001). Correlation analysis showed that MTT was significantly negatively correlated with both the mean APTw and the minimum APTw ( r values were -0.90 and -0.82, P<0.001). TTP was significantly negatively correlated with both the mean APTw and the minimum APTw ( r values were -0.86 and -0.78, P<0.001). Conclusion:APTw value can reflect acidosis in cerebral tissue with impaired perfusion in patients with CICVD.