Stroke is the leading cause of death and disability among adults in China， and its common complications include digestive system abnormalities， cognitive impairment， depression， stroke-associated pneumonia， and hemiplegia. The combination of traditional Chinese and Western medicine has great potential in treating post-stroke complications. Xinglou Chengqitang （XLCQT） is a representative prescription of alleviating the disease in the upper part by treating the lower part. It has definite therapeutic effect and high safety. Clinically， XLCQT is often used to treat stroke and its complications. However， the quantity and quality of clinical trials of XLCQT in treating post-stroke complications need to be improved. Additionally， since the basic research is weak， the material basis and multi-target mechanism for the efficacy of this prescription are unknown. This article reviews XLCQT in terms of the pharmacodynamic basis， medicinal properties， safety evaluation， and progress in clinical research and mechanisms in treating post-stroke complications. This article summarizes 22 key active ingredients of XLCQT in treating acute stroke complicated with syndrome of phlegm heat and fu-organ excess. Among these key active ingredients， resveratrol， kaempferol， luteolin， chrysoeriol， apigenin， （+）-catechin， and adenosine have good pharmacokinetic properties and high bioavailability. The mechanisms of XLCQT in treating post-stroke complications are complex， including inflammatory response， brain-gut axis， hypothalamic-pituitary-adrenal （HPA） axis， intestinal flora， neurotrophic factors， autophagy， oxidative stress， and free radical damage. This review helps to deeply understand the pharmacodynamic basis and mechanisms of XLCQT in treating post-stroke complications and provides a theoretical basis for the clinical application of XLCQT against post-stroke complications and the development of drugs.

Objective To investigate the expression profile, prognostic value, gene co-expression network, and immunomodulatory role of BRF1 in a pan-cancer context, and to explore its biological functions and molecular regulatory mechanisms in esophageal squamous cell carcinoma (ESCC). Methods The pan-cancer dataset from The Cancer Genome Atlas (TCGA) was utilized to analyze the differential expression of BRF1 in tumor versus normal tissues, its association with patient survival, pathway enrichment for co-expressed genes, and immune features (including immune checkpoints, cytokines, and immune cell infiltration). The expression profile of BRF1 in ESCC was validated using the Gene Expression Omnibus (GEO) database. In vitro, BRF1 was knocked down in ESCC cells using siRNA. Cell proliferation and migration were assessed by MTT and Transwell assays, respectively. The expression levels of proliferation- and migration-related proteins were detected by Western blotting. The correlation between BRF1 and ferroptosis was analyzed using TCGA data. Results BRF1 was significantly upregulated in over 20 types of cancer, and its high expression was associated with poor prognosis in patients with adrenocortical carcinoma and prostate adenocarcinoma. BRF1 was found to positively regulate the T-cell-mediated cell death pathway in esophageal adenocarcinoma and was associated with the circadian rhythm regulation pathway in pancreatic adenocarcinoma. The correlation of BRF1 with immune checkpoints, cytokine networks, and immune cell infiltration was found to be cancer type-specific. In vitro experiments demonstrated that knocking down BRF1 significantly inhibited the proliferation of ESCC cells, accompanied by the downregulation of the proliferation marker PCNA. Cell migration was also significantly impaired, with decreased expression of Vimentin and MMPs and increased expression of E-cadherin. Furthermore, the expression of BRF1 was positively correlated with that of ferroptosis-antagonizing genes, such as GPX4, HSPA5, and SLC7A11. Conclusion BRF1 plays complex roles in pan-cancer, participating in the regulation of tumorigenesis, progression, and immune infiltration. BRF1 promotes the proliferation and migration of ESCC cells, a mechanism potentially associated with the regulation of ferroptosis resistance. These findings suggest that BRF1 could be a potential therapeutic target for ESCC.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

With the widespread adoption of low-dose CT screening and the extensive application of high-resolution CT, the detection rate of sub-centimeter lung nodules has significantly increased. How to scientifically manage these nodules while avoiding overtreatment and diagnostic delays has become an important clinical issue. Among them, lung nodules with a consolidation tumor ratio less than 0.25, dominated by ground-glass shadows, are particularly worthy of attention. The therapeutic challenge for this group is how to achieve precise and complete resection of nodules during surgery while maximizing the preservation of the patient's lung function. The "watershed topography map" is a new technology based on big data and artificial intelligence algorithms. This method uses Dicom data from conventional dose CT scans, combined with microscopic (22-24 levels) capillary network anatomical watershed features, to generate high-precision simulated natural segmentation planes of lung sub-segments through specific textures and forms. This technology forms fluorescent watershed boundaries on the lung surface, which highly fit the actual lung anatomical structure. By analyzing the adjacent relationship between the nodule and the watershed boundary, real-time, visually accurate positioning of the nodule can be achieved. This innovative technology provides a new solution for the intraoperative positioning and resection of lung nodules. This consensus was led by four major domestic societies, jointly with expert teams in related fields, oriented to clinical practical needs, referring to domestic and foreign guidelines and consensus, and finally formed after multiple rounds of consultation, discussion, and voting. The main content covers the theoretical basis of the "watershed topography map" technology, indications, operation procedures, surgical planning details, and postoperative evaluation standards, aiming to provide scientific guidance and exploration directions for clinical peers who are currently or plan to carry out lung nodule resection using the fluorescent microscope watershed analysis method.

OBJECTIVE To construct ensemble learning models for predicting high-use days of budesonide based on meteorological factors， thereby providing reference for hospital pharmacy management. METHODS Meteorological data for 2024 and outpatient budesonide usage data from the jurisdiction of Sanming Hospital of Integrated Traditional Chinese and Western Medicine were collected. High-use days were defined as the 75th percentile of outpatient budesonide usage， and a corresponding dataset was established. The prediction task was formulated as a classification problem， and three ensemble learning models were developed： Random Forest， Extreme Gradient Boosting （XGBoost）， and Histogram-based Gradient Boosting Classifier. Model performance was evaluated using accuracy， precision， recall， F1-score， and log-loss. Model interpretability was analyzed using Shapley Additive Explanations （SHAP）. RESULTS The Histogram-based Gradient Boosting Classifier achieved the best performance （accuracy＝0.75， F1-score＝0.48）， followed by XGBoost （accuracy＝0.74， F1-score＝0.43） and Random Forest （accuracy＝0.72， F1-score＝0.22）. SHAP results suggested that the prediction results of the last two models have the highest correction. CONCLUSIONS Ensemble learning models can effectively predict high-use days of budesonide， with the Histogram- based Gradient Boosting Classifier demonstrating the best predictive performance. Low temperature， high humidity， and low atmospheric pressure show significant positive impacts on the prediction of daily budesonide usage.

OBJECTIVE: To develop an artificial intelligence-assisted system for the automatic detection of the features of common 21 auricular points based on the YOLOv8 neural network. METHODS: A total of 660 human auricular images from three research centers were collected from June 2019 to February 2024. The rectangle boxes and features of images were annotated using the LabelMe5.3.1 tool and converted them into a format compatible with the YOLO model. Using these data, transfer learning and fine-tuning training were conducted on different scales of pretrained YOLO neural network models. The model's performance was evaluated on validation and test sets, including the mean average precision (mAP) at various thresholds, recall rate (recall), frames per second (FPS) and confusion matrices. Finally, the model was deployed on a local computer, and the real-time detection of human auricular images was conducted using a camera. RESULTS: Five different versions of the YOLOv8 key-point detection model were developed, including YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x. On the validation set, YOLOv8n showed the best performance in terms of speed (225.736 frames per second) and precision (0.998). On the external test set, YOLOv8n achieved the accuracy of 0.991, the sensitivity of 1.0, and the F1 score of 0.995. The localization performance of auricular point features showed the average accuracy of 0.990, the precision of 0.995, and the recall of 0.997 under 50% intersection ration (mAP⁵⁰). CONCLUSION The key-point detection model of 21 common auricular points based on YOLOv8n exhibits the excellent predictive performance, which is capable of rapidly and automatically locating and classifying auricular points.
Humans ; Neural Networks, Computer ; Artificial Intelligence ; Acupuncture Points

Objective: To investigate the association between emotional intelligence and sleep problems at the symptom level among junior and senior high school students, so as to provide new insights for interventions targeting junior and senior high school students sleep disorders. Methods: From November 2023 to May 2024, a stratified cluster random sampling method was employed to select 3 531 first year junior high school and first year senior high school students from 6 schools in Guangyuan City and Liangshan Yi Autonomous Prefecture in Sichuan Province, as well as Lhasa City in Tibet Autonomous Region. The Insomnia Severity Index Scale and the Wong and Law Emotional Intelligence Scale(WLEIS) were used to assess sleep problems and emotional intelligence. A network analysis was performed to explore the relationship between emotional intelligence and sleep disorders, and a gender based network comparison analysis was conducted. Results: The reported rate of sleep problems among junior and senior high school students was 47.3%, with severe sleep problems of 2.2%. Difficulty maintaining sleep, worry about sleep, and emotional application were the core symptoms in the network (node strength values: 1.11, 0.98, and 0.82, respectively). Dissatisfaction with sleep and emotional application served as bridge symptoms connecting emotional intelligence and sleep problems (bridge strength values: 1.77 and 1.59, respectively). The edge weights of the emotional intelligence and sleep problems network differed significantly between genders (maximum difference in edge weight values was 0.13， P <0.05). Conclusions Emotional application ability and dissatisfaction with sleep are the key nodes in the network connecting emotional intelligence and sleep problems. Targeted efforts to enhance emotional application ability may effectively reduce the risk of sleep problems among junior and senior high school students.

Objective : To investigate the role of enhancer of zeste homolog 2(EZH2)-trimethylated lysine 27 of histone H3(H3K27me3) axis in the dedifferentiation of thyroid cancer and its clinical value as a potential target for the treatment of anaplastic thyroid cancer(ATC). Methods : Immunohistochemical SP method was used to detect the expression of EZH2, H3K27me3, paired box gene 8(PAX8), thyroglobulin(TG) and thyroid transcription factor 1(TTF1) in ATC and papillary thyroid carcinoma(PTC) and their adjacent tissues. The relationship between EZH2 and thyroid differentiation markers(PAX8, TTF1, TG) was further analyzed by gene expression omnibus(GEO) database. ATC cell lines 8305C and BHT-101 were culturedin vitro. Real-time reverse transcription PCR(RT-qPCR) was used to detect the expression of thyroid differentiation markers(TTF1, PAX8) mRNA in ATC cell lines treated with EZH2 inhibitor(GSK126), and evaluate the potential therapeutic effect of GSK126in vitro. The effects of GSK126 and BRAF inhibitor vemurafenib on the proliferation of ATC cell lines were observed by cell proliferation assay. Results : The expression of EZH2 in ATC tissues was significantly higher than that in papillary thyroid carcinoma and adjacent tissues(P<0.05). The expression of H3K37me3 in ATC tissues was significantly lower than that in PTC tissues(P<0.05). EZH2 was negatively correlated with PAX8 and TG expression levels, but not with TTF1 expression level.In vitroexperiments, GSK126 could reverse the expression of thyroid differentiation markers PAX8 and TTF1 in ATC cell lines. GSK126 combined with BRAF inhibitor vemurafenib could significantly inhibit the growth of ATC cell lines. Conclusion The EZH2-H3K27me3 axis plays an important role in regulating thyroid specific markers, and the inhibition of EZH2 by small molecular compounds is a promising target for ATC treatment in the future.

The rapid development of artificial intelligence （AI） technology provides new opportunities for the modernisation of traditional Chinese medicine （TCM） diagnosis. By analysing the foundation， research progress and difficulties of the combination of AI and TCM diagnosis， it is concluded that AI has made remarkable development in intelligence-driven modernization of TCM tongue diagnosis， pulse diagnosis， listening and smelling diagnosis and text processing， and there are useful explorations in the field of constructing data-driven TCM diagnostic model and multidisciplinary integration of TCM diagnostic models. However， the current integration of AI technology in TCM diagnosis still faces many challenges， such as the scarcity and uneven quality of clinical data， the limited ability of AI algorithms to express TCM thinking model of syndrome differentiation and empirical knowledge， and the possible existence of ethical and privacy issues. By systematically sorting out the current research status and development direction of AI-empowered TCM diagnostics， it is proposed to promote the application of AI technology in TCM diagnostics in four aspects， namely， strengthening the construction of TCM big data and talent cultivation， encouraging cross-disciplinary cooperation， improving the legal and ethical framework， and promoting the popularity of the technology in primary care， so as to enhance the modernisation of TCM diagnostics.

Objective:To analyze the expression of tumor-associated transmembrane protein 61 (TMEM61) in cholangiocarcinoma tissues and its influence on prognosis and immune infiltration, as well as the effect on the proliferation of cholangiocarcinoma cells.Methods:In the cholangiocarcinoma gene chip dataset (TCGA-CHOL), differentially expressed genes between cholangiocarcinoma tissues and normal bile duct tissues were screened, and the upregulated TMEM61 gene was selected for further analysis. Based on the TMEM61 expression, cholangiocarcinoma patients higher than the median value were classified as the high-expression group ( n=17), and those lower than the median value were classified as the low-expression group ( n=18). The Kaplan-Meier survival curve was plotted. Functional and pathway enrichment analyses were conducted on differentially expressed genes related to TMEM61, and the correlations between TMEM61 expression and immune cells and immune molecules were respectively analyzed. The expression level of TMEM61 in cholangiocarcinoma tissues was analyzed by immunohistochemistry; The effect of TMEM61 expression on the proliferation of cholangiocarcinoma cells was detected by Western blotting, CCK-8, clone formation assay, etc. Results:Compared with normal tissues, the expression of TMEM61 mRNA in cholangiocarcinoma tissues was significantly upregulated ( t=18.31, P<0.001). The overall survival rate of patients in the high-expression group of TMEM61 was significantly lower than that in the low-expression group, and the difference was statistically significant ( χ2=7.23, P=0.007). The differentially expressed genes related to TMEM61 were involved in cell proliferation, cell cycle and DNA replication, etc. Compared with normal tissues, regulatory T cells ( t=10.21, P<0.001) and M0-type macrophages ( t=5.89, P=0.008) were significantly increased in cholangiocarcinoma tissues. Plasma cells ( t=7.34, P=0.002), γδT cells ( t=9.87, P<0.001), and M2-type macrophages ( t=11.53, P<0.001) were significantly decreased in cholangiocarcinoma tissues. The expression of TMEM61 was correlated with neurociliary protein 1, tumor necrosis factor ligand superfamily member 15 and B7 homologous protein 3 (all P<0.05). The proportion of positive staining area of TMEM61 protein in normal tissues was (10.15±2.27) %, and that in cholangiocarcinoma tissues was (69.43±11.66) %. The difference was statistically significant ( t=14.97, P<0.001). Inhibition of TMEM61 expression led to a decrease in the number of cholangiocarcinoma cell clones and proliferation activity, and the differences were statistically significant (both P<0.01). Conclusion:The expression of TMEM61 is elevated in cholangiocarcinoma tissues and is associated with poor prognosis. The abnormally high expression of TMEM61 affects the infiltration of immune cells and promotes the proliferation of cholangiocarcinoma cells. TMEM61 is expected to become a potential biomarker for the prognosis assessment of cholangiocarcinoma.