OBJECTIVE To explore the application effect of ticagrelor combined with aspirin antiplatelet therapy in high-risk non-disabling ischemic cerebrovascular events （HR-NICE）. METHODS A total of 232 patients with HR-NICE treated at the First Affiliated Hospital of Nanyang Medical College from January 1， 2023 to January 1， 2025 were retrospectively selected. According to different antiplatelet regimens， the patients were divided into three groups： group A （78 cases） received clopidogrel 300 mg as a loading dose on the first day， followed by clopidogrel 75 mg， qd combined with aspirin 100 mg， qd； group B （69 cases） received ticagrelor 120 mg as a loading dose on the first day， followed by ticagrelor 60 mg， bid combined with aspirin 100 mg， qd； group C （85 cases） received ticagrelor 180 mg as a loading dose on the first day， followed by ticagrelor 90 mg， bid combined with aspirin 100 mg， qd. All groups received dual-antiplatelet therapy for 21 days and were then switched to aspirin monotherapy. The primary outcome [incidence of early neurological deterioration （END）] and secondary outcomes， including incidence of ischemic events within 90 days post-discharge， favorable prognosis at 30/90 days， and incidence of bleeding events within 90 days， were compared among the three groups. Logistic regression was used to analyze the relationship between different antiplatelet regimens and END. Interaction terms between treatment regimen and demographic and clinical baseline characteristics were further included to evaluate the interaction effect between treatment regimen and END risk in different subgroups. Propensity score matching （PSM） was performed for sensitivity analysis. RESULTS For the primary outcome， the incidence of END in group C was significantly lower than that in group A （ P ＜0.05）. For secondary outcomes， there were no statistically significant differences among the three groups in the incidence of ischemic events within 90 days post-discharge， favorable prognosis at 30/90 days （ P ＞0.05）. The incidence of minor bleeding events in groups B and C was significantly higher than that in group A （ P ＜0.05）， while no significant differences were observed among the three groups in moderate or severe bleeding events （ P ＞0.05）. Interaction analysis showed that ischemic cerebrovascular disease， Age-Blood Pressure-Clinical Feature-Duration-Diabetes Score （ABCD2）， REACH risk score， and Essen Stroke Risk Score （ESSEN） had significant interactions with treatment regimen （ P ＜0.05）. Among patients with ischemic cerebrovascular disease， ABCD2≥6 points， REACH risk score≥6 points， or ESSEN≥3 points， regimens B or C were associated with a lower risk of END compared with regimen A. The PSM results were generally consistent with the analysis before matching. CONCLUSIONS Ticagrelor， especially the 90 mg bid regimen， plus aspirin， was associated with a lower risk of END in patients with HR-NICE， but increased the risk of minor bleeding events. Patients with ischemic cerebrovascular disease and higher ABCD2， REACH risk score， or ESSEN may be potential beneficiaries of this combination regimen.

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

Large language models (LLMs) such as ChatGPT, Claude, Llama, and Qwen are emerging as transformative technologies for the diagnosis and treatment of various diseases. With their exceptional long-context reasoning capabilities, LLMs are proficient in clinically relevant tasks, particularly in medical text analysis and interactive dialogue. They can enhance diagnostic accuracy by processing vast amounts of patient data and medical literature and have demonstrated their utility in diagnosing common diseases and facilitating the identification of rare diseases by recognizing subtle patterns in symptoms and test results. Building on their image-recognition abilities, multimodal LLMs (MLLMs) show promising potential for diagnosis based on radiography, chest computed tomography (CT), electrocardiography (ECG), and common pathological images. These models can also assist in treatment planning by suggesting evidence-based interventions and improving clinical decision support systems through integrated analysis of patient records. Despite these promising developments, significant challenges persist regarding the use of LLMs in medicine, including concerns regarding algorithmic bias, the potential for hallucinations, and the need for rigorous clinical validation. Ethical considerations also underscore the importance of maintaining the function of supervision in clinical practice. This paper highlights the rapid advancements in research on the diagnostic and therapeutic applications of LLMs across different medical disciplines and emphasizes the importance of policymaking, ethical supervision, and multidisciplinary collaboration in promoting more effective and safer clinical applications of LLMs. Future directions include the integration of proprietary clinical knowledge, the investigation of open-source and customized models, and the evaluation of real-time effects in clinical diagnosis and treatment practices.
Humans ; Large Language Models ; Tomography, X-Ray Computed

Based on the pharmacokinetics theory, this study investigated the pharmacokinetic characteristics of albiflorin, paeoniflorin, wogonoside, and wogonin in normal and febrile rats and summarized absorption and elimination rules of Dayuanyin in them to provide reference for further development and clinical application of Dayuanyin. Blood samples were taken from the fundus venous plexus of normal and model rats after intragastric administration of Dayuanyin at different time points. The concentration of each substance in blood was determined by ultra performance liquid chromatography-triple quadrupole mass spectrometry(UPLC-MS/MS) technique at different time points. DAS 2.0, a piece of pharmacokinetics software, was used to calculate the pharmacokinetic parameters of each component. The results show that the 4 components had good linear relationship in their respective ranges, and the results of methodological investigation met the requirements. The pharmacokinetic parameters of C_(max), T_(max), t_(1/2), AUC_(0-t), AUC_(0-∞), and MRT_(0-t) were calculated by the DAS 2.0 non-compartmental model. Compared with those in the normal group, C_(max) and AUC_(0-t) of the 4 components in the model group were significantly increased. There were significant differences in the pharmacokinetic characteristics between the normal and model groups, suggesting that the absorption and elimination of Dayuanyin may be affected by the changes of internal environment of the body in different physiological states.
Animals ; Rats ; Drugs, Chinese Herbal/administration & dosage* ; Male ; Rats, Sprague-Dawley ; Fever/metabolism* ; Tandem Mass Spectrometry ; Chromatography, High Pressure Liquid ; Glucosides/pharmacokinetics* ; Monoterpenes

To elucidate the mechanism by which steaming affects the quality of Curcuma kwangsiensis root tubers, methods such as LSCM, RVA, dual-wavelength spectrophotometry, LF-NMR, and LC-MS were employed to qualitatively and quantitatively detect changes in starch gelatinization characteristics, water distribution, and material composition of C. kwangsiensis root tubers under different steaming durations. Based on multivariate statistical analysis, the correlation between differences in gelatinization parameters, water distribution, and terpenoid material composition was investigated. The results indicate that steaming affects both starch gelatinization and water distribution in C. kwangsiensis. During the steaming process, transformations occur between amylose and amylopectin, as well as between semi-bound water and free water. After 60 min of steaming, starch gelatinization and water distribution reached an equilibrium state. The content of amylopectin, the amylose-to-amylopectin ratio, and parameters such as gelatinization temperature, viscosity, breakdown value, and setback value were significantly correlated(P≤0.05). Additionally, the amylose-to-amylopectin ratio was significantly correlated with total free water and total water content(P≤0.05). Steaming induced differences in the material composition of C. kwangsiensis root tubers. Clustering of primary metabolites in the OPLS-DA model was distinct, while secondary metabolites were classified into 9 clusters using the K-means clustering algorithm. Differential terpenoid metabolites such as(-)-α-curcumene were significantly correlated with zerumbone, retinal, and all-trans-retinoic acid(P<0.05). Curcumenol was significantly correlated with isoalantolactone and ursolic acid(P<0.05), while all-trans-retinoic acid was significantly correlated with both zerumbone and retinal(P<0.05). Alpha-tocotrienol exhibited a significant correlation with retinal and all-trans-retinoic acid(P<0.05). Amylose was extremely significantly correlated with(-)-α-curcumene, curcumenol, zerumbone, retinal, all-trans-retinoic acid, and α-tocotrienol(P<0.05). Amylopectin was significantly correlated with zerumbone(P<0.05) and extremely significantly correlated with(-)-α-curcumene, curcumenol, zerumbone, retinal, all-trans-retinoic acid, and 9-cis-retinoic acid(P<0.01). The results provide scientific evidence for elucidating the mechanism of quality formation of steamed C. kwangsiensis root tubers as a medicinal material.
Curcuma/chemistry* ; Starch/chemistry* ; Multivariate Analysis ; Water/chemistry* ; Terpenes/analysis* ; Plant Roots/chemistry* ; Plant Tubers/chemistry* ; Drugs, Chinese Herbal/chemistry*

Based on the ultra performance liquid chromatography-quadrupole Exactive Orbitrap mass spectrometry(UPLC-Q-Exactive Orbitrap-MS) technology, combined with related literature, databases, and reference material information, this study qualitatively analyzed the components of Dayuanyin in the tissue of rats after gavage and employed molecular docking technology to predict the rationality of the mechanism behind the antipyretic effect of the in vivo components in Dayuanyin. A total of 21, 26, 20, 21, 14, and 31 prototype components and 3, 16, 3, 7, 5, and 24 metabolites were identified from the heart, liver, spleen, lung, kidney, and hypothalamus of the rats, respectively, and the binding ability of key components and targets was further verified by molecular docking. The results showed that all components had good binding ability with targets. The established UPLC-Q-Exactive Orbitrap-MS could effectively and quickly identify the Dayuanyin components distributed in tissue and preliminarily identify their metabolites. Many components were identified in the hypothalamus, which suggested that the components delivered to the brain should be focused on in the study on Dayuanyin in the treatment of febrile diseases. The molecular docking technology was used to predict the rationality of the mechanism behind its antipyretic effect, which lays the foundation for the clarification of the material basis and action mechanism of Dayuanyin, the development of new preparations, and the prediction of quality markers.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Rats ; Molecular Docking Simulation ; Male ; Antipyretics/metabolism* ; Rats, Sprague-Dawley ; Tissue Distribution ; Mass Spectrometry ; Chromatography, High Pressure Liquid ; Hypothalamus/metabolism*

As global greenhouse gases continue rising, the urgency of more ambitious action is clearer than ever before. China is the world's biggest emitter of greenhouse gases and one of the countries affected most by climate change. The evidence about the impacts of climate change on the environment and human health may encourage China to take more decisive action to mitigate greenhouse gas emissions and adapt to climate impacts. This article aimed to review the evidence of environmental damages and health risks posed by climate change and to provide a new science-based perspective for the delivery of sustainable development goals. Over recent decades, China has experienced a strong warming pattern with a growing frequency of extreme weather events, and the impacts of climate change on China's environment and human health have been consistently observed, with increasing O ₃ air pollution, decreases in water resources and availability, land degradation, and increased risks for both communicable and non-communicable diseases. Therefore, China's climate policy should target the key factors driving climate change and scale up strategic measures to curb carbon emissions and adapt to inevitable increasing climate impacts. It provides new insights for not only China but also other countries, particularly developing and emerging economies, to ensure climate and environmental sustainability whilst pursuing economic growth.
Climate Change ; China ; Humans ; Greenhouse Gases ; Air Pollution ; Sustainable Development ; Environment