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.

Objective To construct a lung cancer surgery-oriented disease-specific database covering the entire perioperative care pathway, thereby improving the quality and usability of key surgical data elements. Methods Real-world clinical data were extracted from a single-center thoracic surgery department. A standardized data model was established based on the open electronic health record (openEHR) standard. Large language model (LLM), optical character recognition (OCR), and artificial intelligence (AI)-driven techniques were employed to extract, structure, and perform quality control on unstructured clinical narratives, imaging reports, and radiological data, with a focus on capturing surgically relevant perioperative indicator. Results A multimodal database comprising 19 917 patients was established, including 7 930 males and 11 987 females, with ages ranging from 15 to 97 (61.7±9.7) years. The database includes 582 structured data variables, textual report data corresponding to 69 clinical indicators, 13 000 pulmonary function test PDF reports, and chest CT imaging data from 16 884 patients. This database comprehensively covers major information relevant to surgical diagnosis and treatment of lung cancer, significantly improving the completeness and granularity of surgical detail data. Large language models (LLMs) and optical character recognition (OCR) technologies enhanced the efficiency of converting unstructured data into structured formats, while a multi-level manual verification process ensured data accuracy and traceability. The database supports real-world research including comparisons of surgical procedures, prediction of postoperative complications, prognosis assessment, and multimodal data association analyses.

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.

Objective To construct and evaluate a prognostic model for clear cell renal cell carcinoma(ccRCC)based on preoperative lipid ratios, cM0(i+)staging, and other clinical characteristics, so as to provide a precise tool for clinical prognosis assessment. Methods A retrospective analysis was conducted on the clinical data of 215 ccRCC patients treated in our hospital during May 2014 and May 2023. Lipid ratios were calculated using preoperative lipid data, and patients were divided into cM0(i+)stage or cM0 stage according to postoperative circulating tumor cells(CTCs)test results. The optimal lipid ratio and cutoff value were selected using receiver operating characteristic(ROC)curves and the X-tile method. Patients were subdivided into three groups based on the level of the low-density lipoprotein cholesterol to non-low-density lipoprotein cholesterol ratio(LNLR)and cM0(i+)staging:Group A [LNLR＞1.68 and diagnosed with cM0(i+)], Group B [LNLR＞1.68 or diagnosed with cM0(i+)], and Group C(LNLR ≤1.68 and diagnosed with cM0). Kaplan-Meier survival analysis was used to plot survival curves for patients in different groups. The log-rank test was employed to compare differences in recurrence-free survival(RFS)among the subgroups. Multivariate Cox regression analysis was conducted to identify the independent risk factors influencing RFS, and a nomogram prediction model was constructed based on these results. The predictive performance of the model was validated using ROC curves, calibration curves, and decision curves. Results ROC curves were plotted for lipid markers, and LNLR was identified as the most predictive for RFS. Its optimal cutoff point was 1. 68. Patients in Group A experienced a significantly shorter postoperative RFS. Multivariate Cox regression analysis identified preoperative LNLR, cM0(i+)staging, pathological grade, and stage as independent risk factors for RFS. A nomogram model was constructed based on these risk factors. The area under the ROC curve(AUC)for 1-, 3-, and 5-year RFS was 0.896(95% CI:0.8121-0.9627), 0.890(95%CI:0.7879-0.9641), and 0.870(95%CI:0.7697-0.9526), indicating good discriminatory ability and predictive performance. Calibration plots demonstrated good agreement between predicted and actual outcomes. Clinical decision curve analysis showed high clinical net benefit. Conclusion Preoperative LNLR level is an independent risk factor for RFS in ccRCC patients. The prognostic prediction model based on LNLR, cM0(i+)staging, patient pathological grade, and staging demonstrates good predictive performance for RFS and holds potential clinical application value.

Objective: To evaluate the effects of a home-school collaborative intervention on improving sugar sweetened beverages (SSBs) consumption behaviors among primary school students in grades 3 to 5 in Chengdu, so as to provide evidence for SSBs prevention and control strategies among primary school students. Methods: Using a stratified cluster random sampling method, a total of 2 006 students in grades 3 to 5 from 4 primary schools in Chengdu were enrolled from October to December 2024, including an intervention group ( n =769) and a control group ( n =1 237). The control group received routine psychological counseling, while the intervention group received a comprehensive home-school collaborative intervention integrating time perspective cultivation and family functioning improvement. A two level linear mixed effects model was used to analyze the net intervention effect, and the EBICglasso algorithm was used to construct variable association networks to identify core intervention targets. Results: After adjusting for confounders and clustering effects, the intervention significantly reduced the weekly frequency of SSBs consumption (decreased from baseline by 1.29 in the intervention group, 95% CI =-1.84 to -0.73; by 0.84 in the control group, 95% CI =-1.21 to -0.46; interaction P =0.01, Cohen s d=-0.16), improved SSBs consumption behavioral intention (decreased from baseline by 0.62 in the intervention group, 95% CI =-1.26 to -0.02; by 0.14 in the control group, 95% CI =-0.57 to 0.29; interaction P =0.02, Cohen s d =-0.15) and increased family functioning level (increased from baseline by 0.34 in the intervention group, 95% CI =-0.16 to 0.85; decreased by 0.08 in the control group, 95% CI =-0.42 to 0.25; interaction P =0.01, Cohen s d =0.17). A permutation test with 1 000 iterations showed no statistically significant difference in global network strength between the two groups ( P =0.19), whereas the differences in overall network structure and maximum edge weight were both statistically significant (all P <0.05), with present hedonistic time perspective, SSBs consumption attitude and behavioral intention identified as the core intervention targets. Conclusion The comprehensive home-school collaborative intervention effectively reduces SSB consumption frequency among primary school students and improves behavioral intention and family functioning.

OBJECTIVE To provide a reference for the optimization of prescription-review software and the selection of auxiliary prescription-review tools for medical institutions.METHODS A total of 559 eligible prescriptions were collected from the Meikang Prescription Automatic Screening System of a tertiary grade-A hospital from January 2025 to March 2026. Judged according to the composite reference standard （CRS） established in this study， there were 206 positive prescriptions [with drug-drug interaction （DDI）] and 353 negative prescriptions （without DDI）. DeepSeek， Doubao， Wenxin yiyan， Kimi and Tongyi qianwen were separately adopted to identify DDI in the 559 prescriptions， and binary judgment was performed on their output texts related to DDI. Taking the results judged by CRS as the gold standard， the confusion matrix was constructed and model performance indicators were calculated. The differences in sensitivity between any two large language models were compared， the relative sensitivity （RS） and its 95% confidence interval （CI） were calculated， and the results were visualized by a forest plot.RESULTS Wenxin yiyan achieved the highest sensitivity （74.8%） with the lowest risk of missed reports， whereas its specificity was only 40.8%. It had the lowest accuracy （53.3%） and presented a high risk of false positive reports. Tongyi qianwen obtained the highest accuracy （65.3%） and the optimal F1-score （0.587）. DeepSeek showed moderate performance in both sensitivity and specificity. Doubao and Kimi exhibited relatively low sensitivity （38.8% and 45.1%， respectively）， accompanied by high risks of missed reports. Pairwise comparison revealed that the sensitivity of Wenxin yiyan was significantly higher than that of DeepSeek [ χ 2 ＝4.84， P ＝0.028； RS＝1.124， 95%CI （1.018， 1.230）]. The sensitivity of Tongyi qianwen was significantly higher than that of Doubao [ χ 2 ＝4.77， P ＝0.029； RS＝1.239， 95%CI （1.035， 1.443）]. DeepSeek had significantly higher sensitivity than Doubao [ χ 2 ＝37.65， P ＜0.001； RS＝1.715， 95%CI （1.491， 1.939）]. No statistically significant differences in sensitivity were observed between Kimi versus Doubao and between Tongyi qianwen versus Kimi （ P ＞0.05）.CONCLUSIONS Wenxin yiyan is applicable for preliminary-screening scenarios with low tolerance for missed reports and requires combined manual review. Tongyi qianwen is suitable for auxiliary outpatient prescription review. DeepSeek can act as a transitional solution for medical institutions without dedicated prescription-review software. Doubao and Kimi are not recommended for independent application in prescription DDI audit.

ObjectiveTo investigate the prevalence of coronary microvascular dysfunction （CMD） during the perioperative period of elective percutaneous coronary intervention （PCI） and the incidence of deterioration in coronary microvascular function using angiography-derived microcirculatory resistance （AMR）， and to explore the influencing factors associated with PCI-related coronary microvascular injury. MethodsPatients who underwent elective PCI on the left anterior descending coronary artery at Beijing Hospital of Traditional Chinese Medicine， Capital Medical University， from January 1， 2021 to December 31， 2024 were retrospectively enrolled. AMR values of the target vessel before and after PCI were calculated to evaluate the prevalence of postoperative CMD and the incidence of microvascular functional deterioration. Multivariate analysis was performed to explore the influencing factors associated with coronary microvascular injury during the periprocedural period of PCI. ResultsA total of 204 patients were finally included. Postoperative AMR of the target vessel were significantly higher than preoperative values （P＜0.001）. The prevalence of CMD was 31.86% （65/204）， and the incidence of microvascular functional deterioration was 69.61% （142/204）. In-stent restenosis （P = 0.004） and post-balloon dilation （P = 0.002） were risk factors for CMD. Traditional Chinese medicine （TCM） syndrome elements phlegm-turbidity （P = 0.004） and blood stasis （P = 0.013）， a higher diameter stenosis rate of the target lesion （P = 0.006）， larger stent/balloon diameter （P = 0.009）， and a greater number of balloon inflations （P = 0.029） were risk factors for microvascular functional deterioration. Preoperative use of Chinese herbal decoctions with actions of resolving phlegm and activating blood （P = 0.014） was a protective factor against microvascular functional deterioration， while intraoperative use of Kuanxiong Aerosol （宽胸气雾剂） was a protective factor against both CMD （P = 0.007） and microvascular functional deterioration （P = 0.030）. ConclusionCMD and microvascular functional deterioration are relatively common during the perio-perative period of elective PCI. Target lesion characteristics， PCI procedural features， and the TCM syndrome elements of phlegm-turbidity and blood stasis are associated with an increased risk of coronary microvascular injury during the perioperative period， whereas TCM treatment may reduce this risk.

Objective By combining biological detection and imaging evaluation, a clinical prediction model is constructed based on a large cohort to improve the accuracy of distinguishing between benign and malignant pulmonary nodules. Methods A retrospective analysis was conducted on the clinical data of the 32 627 patients with pulmonary nodules who underwent chest CT and testing for 7 types of lung cancer-related serum autoantibodies (7-AABs) at our hospital from January 2020 to April 2024. The univariate and multivariate logistic regression models were performed to screen independent risk factors for benign and malignant pulmonary nodules, based on which a nomogram model was established. The performance of the model was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results A total of 1 017 patients with pulmonary nodules were included in the study. The training set consisted of 712 patients, including 291 males and 421 females, with a mean age of (58±12) years. The validation set included 305 patients, comprising 129 males and 176 females, with a mean age of (58±13) years. Univariate ROC curve analysis indicated that the combination of CT and 7-AABs testing achieved the highest area under the curve (AUC) value (0.794), surpassing the diagnostic efficacy of CT alone (AUC=0.667) or 7-AABs alone (AUC=0.514). Multivariate logistic regression analysis showed that radiological nodule diameter, nodule nature, and CT combined with 7-AABs detection were independent predictors, which were used to construct a nomogram prediction model. The AUC values for this model were 0.826 and 0.862 in the training and validation sets, respectively, demonstrating excellent performance in DCA. Conclusion The combination of 7-AABs with CT significantly enhances the accuracy of distinguishing between benign and malignant pulmonary nodules. The developed predictive model provides strong support for clinical decision-making and contributes to achieving precise diagnosis and treatment of pulmonary nodules.

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.