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 evaluate the accuracy and reasoning capabilities of large multimodal language models compared with those of neuroradiology subspecialty-trained radiologists in neuroradiology case interpretation. Materials and Methods: This experimental study used custom-made 401 radiologic quizzes derived from articles published in RadioGraphics covering neuroradiology and head and neck topics (October 2020 to February 2024). We prompted the GPT-4 Turbo with Vision (GPT-4V), GPT-4 Omni, Gemini Flash, and Claude models to provide the top three differential diagnoses with a rationale and describe examination characteristics such as imaging modality, sequence, use of contrast, image plane, and body part. The temperature was adjusted to 0 and 1 (T1). Two neuroradiologists answered the same questions.The accuracies of the large language models (LLMs) and the neuroradiologists were compared using generalized estimating equations. Three neuroradiologists assessed the rationale provided by the LLMs for their differential diagnoses using four-point scales, separately for specific lesion locations and imaging findings, and evaluated the presence of hallucinations and the overall acceptability of the responses. Results: Top-3 accuracy (i.e., correct answers present among top-3 differential diagnoses) of LLMs ranged from 29.9% (120 of 401) to 49.4% (198 of 401, obtained with GPT-4V in the T1 setting), while radiologists achieved 80.3% (322 of 401) and 68.3% (274 of 401), respectively (P < 0.001). Regarding the rationale for differential diagnoses, GPT-4V (T1) accurately identified both the specific lesion location and imaging findings in 30.7% (123 of 401) and 12.9% (16 of 124) of cases without textual clinical history. Hallucinations occurred in 4.5% (18 of 401), and only 29.4% (118 of 401) of the LLM-generated analyses were deemed acceptable. GPT-4V (T1) demonstrated high accuracy in identifying the imaging modality (97.4% [800 of 821]) and scanned body parts (92.2% [756 of 820]). Conclusion LLMs remarkably underperformed compared with neuroradiologists and showed unsatisfactory reasoning for their differential diagnoses, with performance declining further in cases without textual input of clinical history. These findings highlight the limitations of current multimodal LLMs in neuroradiological interpretation and their reliance on text input.

Background: and Purpose Criteria for amyloid-related imaging abnormalities in anti-amyloid therapy are based on T2* gradient-recalled echo (GRE), but susceptibility-weighted imaging (SWI) is widely used, creating uncertainty. This study quantitatively compared the detectability of SWI and GRE for cerebral microbleeds and established evidence supporting distinct microbleed criteria for each. Methods: A systematic review and meta-analysis were conducted following PRISMA guidelines. PubMed and Embase were searched for studies directly comparing SWI and GRE up to August 8, 2024. Study quality was assessed with QUADAS-2. The pooled proportion of microbleed detection and detection ratio were calculated. Subgroup analyses were performed based on magnetic field strength (1.5 T vs. 3 T) and SWI slice thickness (<2 mm vs. ≥2 mm), equipment vendor, and study quality. Results: Thirteen studies were included. SWI detected cerebral microbleeds approximately 1.6times more effectively than GRE. At 3.0 T and 1.5 T, SWI exhibited 1.7-fold and 1.5-fold greater detectability, respectively. SWI with thinner slices (<2 mm) showed a 1.9-fold improvement, while thicker slices (≥2 mm) showed a 1.3-fold improvement. Subgroup analyses revealed no significant differences between vendors (0.61 vs. 0.60, p=0.89), or by study quality (0.61 vs. 0.59,p=0.89). Conclusions SWI detects cerebral microbleeds about 1.6 times more effectively than GRE, highlighting important differences between the two techniques. Cautious exploration of adjusted thresholds may be needed, and prospective validation in therapy-specific cohorts will be essential before clinical application.

Introduction: Polypharmacy and potentially inappropriate prescribing (PIP) are increasingly recognised as important contributors to medication -related harm among older adults. However, published national data on the prevalence of polypharmacy and PIP in Brunei Darussalam are unavailable. The objectives of this study was to determine the prevalence of polypharmacy and PIP among adults aged 65 years and above in Brunei Darussalam using Screening Tool of Older Persons ’ potentially inappropriate Prescriptions (STOPP) version 2 criteria. Materials and Methods: A retrospective cross -sectional study was conducted using data from the Brunei Healthcare Information Management System (Bru -HIMS). A stratified proportional random sample of 2,000 older adults with documented medical encounters in 2016 was selected. Polypharmacy was defined as the concurrent use of five or more medications. PIP was assessed using STOPP version 2 criteria. Descriptive statistics were used to summarise prescribing patterns. Results: Of the 2000 older adults included (mean age 73.0 ± 6.6 years), 1,181 (59.1%) had polypharmacy. Among those with polypharmacy, 1,057 (89.5%) could be assessed using STOPP criteria, and 206 (19.5%) had at least one PIP. The most frequently identified PIPs involved prolonged use of proton pump inhibitors for uncomplicated peptic ulcer disease at full therapeutic dosage for more than eight weeks, where dose reduction, discontinuation, or transition to maintenance therapy (e.g. H2 receptor antagonists) is indicated, prescribing without evidence -based indication, duplication of drug classes, use of ACE inhibitors or angiotensin receptor blockers in patients with hyperkalaemia, and use of first -generation antihistamines. Conclusion: Polypharmacy and PIP are common among older adults in Brunei Darussalam. The findings highlight the need for systematic medication review and deprescribing strategies to optimise pharmacotherapy and improve medication safety in older populations

Introduction: Polypharmacy and potentially inappropriate prescribing (PIP) are increasingly recognised as important contributors to medication -related harm among older adults. However, published national data on the prevalence of polypharmacy and PIP in Brunei Darussalam are unavailable. The objectives of this study was to determine the prevalence of polypharmacy and PIP among adults aged 65 years and above in Brunei Darussalam using Screening Tool of Older Persons ’ potentially inappropriate Prescriptions (STOPP) version 2 criteria. Materials and Methods: A retrospective cross -sectional study was conducted using data from the Brunei Healthcare Information Management System (Bru -HIMS). A stratified proportional random sample of 2,000 older adults with documented medical encounters in 2016 was selected. Polypharmacy was defined as the concurrent use of five or more medications. PIP was assessed using STOPP version 2 criteria. Descriptive statistics were used to summarise prescribing patterns. Results: Of the 2000 older adults included (mean age 73.0 ± 6.6 years), 1,181 (59.1%) had polypharmacy. Among those with polypharmacy, 1,057 (89.5%) could be assessed using STOPP criteria, and 206 (19.5%) had at least one PIP. The most frequently identified PIPs involved prolonged use of proton pump inhibitors for uncomplicated peptic ulcer disease at full therapeutic dosage for more than eight weeks, where dose reduction, discontinuation, or transition to maintenance therapy (e.g. H2 receptor antagonists) is indicated, prescribing without evidence -based indication, duplication of drug classes, use of ACE inhibitors or angiotensin receptor blockers in patients with hyperkalaemia, and use of first -generation antihistamines. Conclusion: Polypharmacy and PIP are common among older adults in Brunei Darussalam. The findings highlight the need for systematic medication review and deprescribing strategies to optimise pharmacotherapy and improve medication safety in older populations

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.

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 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