OBJECTIVE To study the improvement effect and mechanism of Tripterygium wilfordii multiglucoside （TWM） on nephrotic syndrome in rats. METHODS The nephrotic syndrome model was established by intravenous injection of adriamycin via the tail vein. The modeling rats were randomly divided into the model group （distilled water）， prednisone group （10 mg/kg）， and TWM high- and low-dose groups （10 and 5 mg/kg， respectively）. Additionally， blank group （distilled water） without model induction was established. Each group consisted of 9 rats. Rats in each group were administered the corresponding drugs or distilled water by gavage， once a day， for 6 consecutive weeks. The histopathological morphology of kidney tissues in rats was observed； the levels of 24-hour urinary protein （24 h-UTP） and serum biochemical indicators [albumin （ALB）， blood urea nitrogen （BUN）， serum creatinine （SCr）， cholesterol （CHOL）， and triglyceride （TG）] in rats were determined； the levels of oxidative stress indicators [superoxide dismutase （SOD）， malondialdehyde （MDA）] in kidney tissue of rats were determined； expressions of hypoxia-inducible factor-1α （HIF-1α）/microRNA-155-5p （miR-155-5p）/nuclear factor erythriod 2- related factor 2 （Nrf2） signaling pathway-related mRNA and protein in the renal tissues of rats were detected. RESULTS Compared with the blank group， the rats in the model group exhibited disordered renal tissue structure， with a small amount of glomerular necrosis and edema of the renal tubular epithelial cells. 24 h-UTP， serum levels of SCr， BUN， CHOL and TG， MDA content， mRNA and protein expressions of HIF-1α and Keap1 as well as the expression of miR-155-5p in renal tissues were increased significantly （ P ＜0.05）. Serum level of ALB， SOD level in renal tissue as well as mRNA and protein expressions of Nrf2 were decreased significantly （ P ＜0.05）. Compared with the model group， TWM high-dose and low-dose groups exhibited significant improvements in renal injury， with notable reversals in the levels of the above quantitative indicators （ P ＜0.05）. CONCLUSIONS TWM can alleviate oxidative stress-induced damage and thereby improve nephrotic syndrome in rats by regulating the HIF-1α/miR-155-5p/Nrf2 signaling pathway.

OBJECTIVE: To propose a machine learning-based method for predicting the trajectories during manual acupuncture manipulation (MAM), aiming to improve the precision and consistency of acupuncture practitioner' operation and provide the real-time suggestions on MAM error correction. METHODS: Computer vision technology was used to analyze the hand micromotion when holding needle during acupuncture, and provide a three-dimensional coordinate description method of the index finger joints of the holding hand. Focusing on the 4 typical motions of MAM, a machine learning-based MAM trajectory predictive model was designed. By integrating the changes of phalangeal joint angle and hand skeletal information of acupuncture practitioner, the motion trajectory of the index finger joint was predicted accurately. Besides, the roles of machine learning-based MAM trajectory predictive model in the skill transmission of acupuncture manipulation were verified by stratified randomized controlled trial. RESULTS: The performance of MAM trajectory predictive model, based on the long short-term memory network (LSTM), obtained the highest stability and precision, up to 98%. The learning effect was improved when the model applied to the skill transmission of acupuncture manipulation. CONCLUSION The machine learning-based MAM predictive model provides acupuncture practitioner with precise action prediction and feedback. It is valuable and significant for the inheritance and error correction of manual operation of acupuncture.
Humans ; Acupuncture Therapy/instrumentation* ; Machine Learning ; Adult ; Male ; Female

OBJECTIVE: To investigate the effect of manual acupuncture manipulations (MAMs) on subcutaneous muscle tissue, by developing quantitative models of "lifting and thrusting" and "twisting and rotating", based on machine learning techniques. METHODS: A depth camera was used to capture the acupuncture operator's hand movements during "lifting and thrusting" and "twisting and rotating" of needle. Simultaneously, the ultrasound imaging was employed to record the muscle tissue responses of the participants. Amplitude and angular features were extracted from the movement data of operators, and muscle fascicle slope features were derived from the data of ultrasound images. The dynamic time warping barycenter averaging algorithm was adopted to align the dual-source data. Various machine learning techniques were applied to build quantitative models, and the performance of each model was compared. The most optimal model was further analyzed for its interpretability. RESULTS: Among the quantitative models built for the two types of MAMs, the random forest model demonstrated the best performance. For the quantitative model of the "lifting and thrusting" technique, the coefficient of determination (R²) was 0.825. For the "twisting and rotating" technique, R² reached 0.872. CONCLUSION Machine learning can be used to effectively develop the models and quantify the effects of MAMs on subcutaneous muscle tissue. It provides a new perspective to understand the mechanism of acupuncture therapy and lays a foundation for optimizing acupuncture technology and designing personalized treatment regimen in the future.
Humans ; Acupuncture Therapy/methods* ; Machine Learning ; Male ; Adult ; Female ; Subcutaneous Tissue/diagnostic imaging* ; Young Adult

INTRODUCTION: The rising rate of adolescent suicide, and the burden of self-harm and mental health disorders, pose significant threats to Singapore's future health outcomes and human potential. This study sought to examine the risk profile and healthcare utilisation patterns of Singaporean adolescents who presented to the emergency department (ED) for suicidal or self-harm behaviour. METHOD: A retrospective review of medical records for patients aged 10 to 19 years who visited Singapore's KK Women's and Children's Hospital ED for suicidal or self-harm attempts from January to December 2021 was conducted. RESULTS: A total of 221 patients were identified, with a predominance of female patients (85.5%) over males (14.5%). The mean age was 14.2 ± 1.4 years. Intentional drug overdose (52.0%) was the most commonly used method. Significantly more females presented for intentional paracetamol overdose (46.6% versus [vs] 28.1%, P=0.049), whereas jumping from a height was more common among males (18.8% vs 5.8%, P=0.022). The most frequently observed mental health challenges were stress-related and emotional coping difficulties (50.7%), followed by mood and anxiety symptoms (53.4%). A history of self-harm and suicidal behaviours were the most common psychosocial risk factors. Within the year prior to their ED presentation, 15.4% had accessed healthcare services for mild medical ailments, 19.5% for medically unexplained symptoms, and 17.2% for previous self-harm or suicide attempts. CONCLUSION Most cases involved psychosocial and emotional regulation difficulties, some of which displayed sex-specific patterns, rather than complex psychiatric disorders. The identified predictive factors can help inform Singapore's National Mental Health and Well-being Strategy, to guide targeted and transdiagnostic interventions in schools and community settings.
Humans ; Adolescent ; Singapore/epidemiology* ; Female ; Male ; Suicide, Attempted/psychology* ; Emergency Service, Hospital/statistics & numerical data* ; Self-Injurious Behavior/psychology* ; Retrospective Studies ; Child ; Young Adult ; Drug Overdose/epidemiology* ; Risk Factors ; Acetaminophen/poisoning* ; Patient Acceptance of Health Care/statistics & numerical data* ; Sex Factors

Objective: To evaluate the adherence of large language model (LLM)-based healthcare research to the Minimum Reporting Items for Clear Evaluation of Accuracy Reports of Large Language Models in Healthcare (MI-CLEAR-LLM) checklist, a framework designed to enhance the transparency and reproducibility of studies on the accuracy of LLMs for medical applications. Materials and Methods: A systematic PubMed search was conducted to identify articles on LLM performance published in high-ranking clinical medicine journals (the top 10% in each of the 59 specialties according to the 2023 Journal Impact Factor) from November 30, 2022, through June 25, 2024. Data on the six MI-CLEAR-LLM checklist items: 1) identification and specification of the LLM used, 2) stochasticity handling, 3) prompt wording and syntax, 4) prompt structuring, 5) prompt testing and optimization, and 6) independence of the test data—were independently extracted by two reviewers, and adherence was calculated for each item. Results: Of 159 studies, 100% (159/159) reported the name of the LLM, 96.9% (154/159) reported the version, and 91.8% (146/159) reported the manufacturer. However, only 54.1% (86/159) reported the training data cutoff date, 6.3% (10/159) documented access to web-based information, and 50.9% (81/159) provided the date of the query attempts. Clear documentation regarding stochasticity management was provided in 15.1% (24/159) of the studies. Regarding prompt details, 49.1% (78/159) provided exact prompt wording and syntax but only 34.0% (54/159) documented prompt-structuring practices. While 46.5% (74/159) of the studies detailed prompt testing, only 15.7% (25/159) explained the rationale for specific word choices. Test data independence was reported for only 13.2% (21/159) of the studies, and 56.6% (43/76) provided URLs for internet-sourced test data. Conclusion Although basic LLM identification details were relatively well reported, other key aspects, including stochasticity, prompts, and test data, were frequently underreported. Enhancing adherence to the MI-CLEAR-LLM checklist will allow LLM research to achieve greater transparency and will foster more credible and reliable future studies.

Purpose: This study compared the differences in health and dietary characteristics according to the subjective health perception among postmenopausal middle-aged women. Methods: Data from the 8 th Korea National Health and Nutrition Examination Survey (2019–2021) were utilized. The participants were naturally postmenopausal women aged 45–59 years, categorized into three groups (good, moderate, and bad) based on their subjective health perception. The general and biochemical characteristics, prevalence of diseases, mental health indicators, dietary behavior factors, food groups, and nutrient intake were compared according to subjective health perception. Results: Bad subjective health perception was associated with lower education levels, not engaging in economic activity, and higher rates of alcohol drinking and smoking. Women with bad subjective health perception had higher fasting blood glucose levels, hemoglobin A1c levels, blood insulin concentrations, and triglyceride concentrations, as well as lower total cholesterol and high-density lipoprotein cholesterol concentrations. In addition, the prevalence of hyperlipidemia and anemia was higher in this group. Women with bad subjective health perceptions were more likely to perceive themselves as fat or thin, experience activity restrictions, perceive stress, have suicidal ideation, and have sought medical assistance for mental issues. They also had higher rates of skipping lunch, lower frequency of fruit consumption, engaging in dietary therapy, feeling chewing discomfort, and higher total daily energy intake. Conclusion These findings suggest that bad subjective health perception in postmenopausal middle-aged women is associated with a higher prevalence of diseases, worse mental health status, and less healthy dietary behaviors. These results can serve as foundational data for future guidelines on desirable health and dietary behaviors aimed at improving the subjective health perceptions of middle-aged women after menopause.

Objective: To evaluate the adherence of large language model (LLM)-based healthcare research to the Minimum Reporting Items for Clear Evaluation of Accuracy Reports of Large Language Models in Healthcare (MI-CLEAR-LLM) checklist, a framework designed to enhance the transparency and reproducibility of studies on the accuracy of LLMs for medical applications. Materials and Methods: A systematic PubMed search was conducted to identify articles on LLM performance published in high-ranking clinical medicine journals (the top 10% in each of the 59 specialties according to the 2023 Journal Impact Factor) from November 30, 2022, through June 25, 2024. Data on the six MI-CLEAR-LLM checklist items: 1) identification and specification of the LLM used, 2) stochasticity handling, 3) prompt wording and syntax, 4) prompt structuring, 5) prompt testing and optimization, and 6) independence of the test data—were independently extracted by two reviewers, and adherence was calculated for each item. Results: Of 159 studies, 100% (159/159) reported the name of the LLM, 96.9% (154/159) reported the version, and 91.8% (146/159) reported the manufacturer. However, only 54.1% (86/159) reported the training data cutoff date, 6.3% (10/159) documented access to web-based information, and 50.9% (81/159) provided the date of the query attempts. Clear documentation regarding stochasticity management was provided in 15.1% (24/159) of the studies. Regarding prompt details, 49.1% (78/159) provided exact prompt wording and syntax but only 34.0% (54/159) documented prompt-structuring practices. While 46.5% (74/159) of the studies detailed prompt testing, only 15.7% (25/159) explained the rationale for specific word choices. Test data independence was reported for only 13.2% (21/159) of the studies, and 56.6% (43/76) provided URLs for internet-sourced test data. Conclusion Although basic LLM identification details were relatively well reported, other key aspects, including stochasticity, prompts, and test data, were frequently underreported. Enhancing adherence to the MI-CLEAR-LLM checklist will allow LLM research to achieve greater transparency and will foster more credible and reliable future studies.

Objective: To evaluate the adherence of large language model (LLM)-based healthcare research to the Minimum Reporting Items for Clear Evaluation of Accuracy Reports of Large Language Models in Healthcare (MI-CLEAR-LLM) checklist, a framework designed to enhance the transparency and reproducibility of studies on the accuracy of LLMs for medical applications. Materials and Methods: A systematic PubMed search was conducted to identify articles on LLM performance published in high-ranking clinical medicine journals (the top 10% in each of the 59 specialties according to the 2023 Journal Impact Factor) from November 30, 2022, through June 25, 2024. Data on the six MI-CLEAR-LLM checklist items: 1) identification and specification of the LLM used, 2) stochasticity handling, 3) prompt wording and syntax, 4) prompt structuring, 5) prompt testing and optimization, and 6) independence of the test data—were independently extracted by two reviewers, and adherence was calculated for each item. Results: Of 159 studies, 100% (159/159) reported the name of the LLM, 96.9% (154/159) reported the version, and 91.8% (146/159) reported the manufacturer. However, only 54.1% (86/159) reported the training data cutoff date, 6.3% (10/159) documented access to web-based information, and 50.9% (81/159) provided the date of the query attempts. Clear documentation regarding stochasticity management was provided in 15.1% (24/159) of the studies. Regarding prompt details, 49.1% (78/159) provided exact prompt wording and syntax but only 34.0% (54/159) documented prompt-structuring practices. While 46.5% (74/159) of the studies detailed prompt testing, only 15.7% (25/159) explained the rationale for specific word choices. Test data independence was reported for only 13.2% (21/159) of the studies, and 56.6% (43/76) provided URLs for internet-sourced test data. Conclusion Although basic LLM identification details were relatively well reported, other key aspects, including stochasticity, prompts, and test data, were frequently underreported. Enhancing adherence to the MI-CLEAR-LLM checklist will allow LLM research to achieve greater transparency and will foster more credible and reliable future studies.

This article outlines the systematic radiological approach preoperative evaluation of perihilar cholangiocarcinoma (pCCA) using CT and MRI to provide key information regarding the suitability for curative surgical resection. It discusses older classification systems (BismuthCorlette, Memorial Sloan Kettering Cancer Center T staging) and follows the Korean Society of Abdomi­nal Radiology 2019 consensus recommendations for step-by-step assessment.The correlation between radiological, surgical, and pathological findings is illustrated through a pictorial review of pathologically proven cases. Benign and malignant mimics of pCCA are included to provide a comprehensive overview.

Objective:To analyze the epidemiological characteristics of cases involving monkey injuries at medical institutions surrounding Qianlingshan Park in Guiyang City, and to provide a reference basis for preventive measures to reduce monkey injuries and standardized post-exposure treatment.Methods:A retrospective cross-sectional study was conducted, collecting 1 900 cases of monkey-induced injuries in Qianlingshan Park treated at the outpatient clinic of Guizhou Provincial Center for Disease Control and Prevention and the Department of Surgery at Qianling Hospital, Guiyang City, from 2021 to 2024. Statistical analysis was performed using Pearson′s chi-square test.Results:Total of 1 900 cases of monkey-related injuries in Qianlingshan Park were collected from 2021 to 2024. The exposure time distribution exhibited significant seasonality, with 48.58% of cases occurring during July and August, totaling 923 cases, indicating a peak in the summer. There were 774 male patients and 1 126 female patients, with a ratio of 1∶1.45.and significant differences were observed between different age groups and genders (χ2=195.00, P<0.001), with the highest number of cases occurring in the 0-9 and 20-29 age groups, accounting for 22.05%(419 cases) and 21.79%(414 cases), respectively. The upper limbs were the most common injury site, accounting for 50.84% of the total cases(966 cases in total), with significant differences between gender and injury location (χ2=22.00, P<0.001), Among females, the proportion of injuries to the upper and lower limbs (30.11% and 16.47%, respectively) was higher than that among males (20.74% and 8.63%, respectively). The majority of injuries were classified as Grade Ⅲ, making up 57.38% of cases(1 069 cases in total). Self-treatment after exposure was the most common approach(60.44%), with significant differences observed between wound severity and treatment method (χ2=6.90, P=0.032), Patients with Grade Ⅱ and Grade Ⅲ wounds were more likely to choose self-management (26.84% and 33.23%, respectively) than outpatient management (15.14% and 24.15%). Approximately 98.05% (1 863 cases) of monkey-injured patients had received rabies vaccinations. Conclusions:This study analyzes monkey-related injuries in Qianlingshan Park from 2021 to 2024, clarifying the temporal distribution of injuries, demographic characteristics, injury sites, and treatment methods. The findings provide references for optimizing human-monkey conflict management and the prevention and control of zoonotic diseases in urban ecological parks.