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 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.

Objective:To develop a Pain Management Nursing Competency Evaluation Index System for surgical nurses, providing a framework for assessing their pain management competency and guiding in-service training programs.Methods:From December 2023 to July 2024, a research team employed literature analysis, semi-structured interviews, expert consultations, and the analytic hierarchy process (AHP) to determine the content and weight of the competency evaluation index system. A cross-sectional survey was subsequently conducted using convenience sampling on 788 surgical nurses to test the internal consistency, test-retest reliability, and content validity of the index system.Results:The response rates for the two rounds of expert consultations were both 100% (46/46), with an expert authority coefficient of 0.921; the Kendall′s coefficients of expert opinions for the primary indicators were 0.106 and 0.245, respectively. The final index system included seven primary indicators (routine pain assessment, assessment and management of active and unexpected pain, pharmacological pain management, patient-controlled analgesia nursing, non-pharmacological pain management, pain related education for patients and their families, and professional development), 23 secondary indicators, and 78 tertiary indicators. The overall Cronbach α was 0.991, the test-retest reliability was 0.493, the item-level content validity index ranged from 0.96 to 1.00, and the scale-level content validity index was 0.98.Conclusions:The pain management nursing competency evaluation index system for surgical nurses is scientifically valid, reliable, and practical. It offers a solid foundation for evaluating the pain management competencies of surgical nurses and designing targeted in-service training programs.

Objective:To explore the relationship between life events, anxiety, depression, and behavior problems in adolescents.Methods:From September to October 2022, the cluster sampling method was used to select 5 341 adolescents from 4 middle schools in Xinxiang urban area.The subjects and their parents were investigated by the adolescent self-rating life events check list (ASLEC), generalized anxiety disorder scale (GAD-7), patient health questionnaire (PHQ-9), and child behavior checklist (CBCL). SPSS 27.0 software was used for Spearman correlation analysis, and AMOS 28.0 software was used to construct the structural equation model.Results:The scores of anxiety, depression, and behavioral problems were 1 (0, 4), 1 (0, 4), and 3 (0, 10). The total score of life events was 5 (1, 13), and the dimensions scored as follows: interpersonal conflict 1 (0, 4), academic pressure 2 (0, 5), punishment 0 (0, 2), loss 0 (0, 0), health and adaptation problem 0 (0, 1), and others 0 (0, 2). There were positive correlations between life events and its dimensions, depression, anxiety and behavioral problems ( r=0.28-0.69, all P<0.01). In the overall population, anxiety and depression played parallel mediating roles in the impact of life events on behavior problems. Life events could positively predict anxiety ( β=0.68, P<0.01), and anxiety could positively predict behavior problems ( β=0.04, P=0.02). Life events could positively predict depression ( β=0.77, P<0.01), and depression could positively predict behavior problems ( β=0.18, P<0.01). The standardized total effect size of the impact of life events on behavioral problems was 0.622 (95% CI=0.564-0.675). The standardized direct effect size and indirect effect size were 0.460 (95% CI=0.374-0.539) and 0.162 (95% CI=0.108-0.218), accounting for 74.0% and 26.0%of the total effect, respectively. After stratification by gender, the results for male adolescents were consistent with the overall population, while the mediating effect of anxiety was not significant in the female adolescents. Conclusion:Life events can lead to anxiety and depression in adolescents, thereby increasing the risk of behavior problems.

OBJECTIVE To design and synthesize rifamycin S derivatives and load them into milk exosomes to evaluate their in vitro antimicrobial activity.METHODS Rifamycin S derivatives were synthe-sized and characterized by mass spectrometry and NMR.Using the dilution assay method,the inhibitory activity of each rifamycin S derivatives molecule against Staphylococcus aureus and Pseudomonas aerugi-nosa was determined,and the IC50 was calculated.Derivatives molecules with excellent antimicrobial activity were selected and loaded into milk exosomes using the ultrasonication method,resulting in the preparation of milk exosome-loaded rifamycin S derivatives.The antimicrobial activity against Staphylo-coccus aureus was determined using the dilution assay method.The inhibitory effect of the exosome-loaded rifamycin S derivatives on Staphylococcus aureus residing within macrophages was detected using the plate colony counting method.RESULTS Three rifamycin S derivatives were successfully designed and synthesized,which demonstrated superior antimicrobial activity against Staphylococcus aureus(the parent compound's antimicrobial activity is merely from 1/20 to 1/80 of that of the three rifamycin S derivatives)and Pseudomonas aeruginosa(the parent compound's antimicrobial activity is only 1/14 and 1/9 of that of compound 1 and compound 3)compared to the parent compound.The loading of milk exosomes with the rifamycin S derivatives compound 3 was successfully achieved,with a loading efficiency of 10.9％.The antimicrobial activity of the compound after exosome loading was significantly enhanced against Staphylococcus aureus in vitro and against Staphylococcus aureus residing within macrophages(P＜0.01).CONCLUSION The designed and synthesized derivatives of rifamycin S possess stronger anti-microbial activity,and their antibacterial efficacy against both extracellular and intracellular bacteria can be further enhanced after loading into exosomes.

Objective:To explore the clinical phenotype and genetic characteristics of a child with hereditary Spastic paraplegia type 52 (SPG52) due to variant of AP4S1 gene. Methods:A child diagnosed with SPG52 at the Department of Pediatrics of the First Affiliated Hospital of Anhui Medical University in May 2010 was selected as the study subject. Whole-exome sequencing (WES) was carried out for the child and his parents. Candidate variants were confirmed by Sanger sequencing. Pathogenicity of the candidate variant was interpreted according to the guidelines from the American College of Medical Genetics and Genomics (ACMG). The study protocol was approved by the Ethics Committee of the Hospital (Ethics No.: PJ2024-04-56).Results:The child had presented with global developmental delay from infancy, and featured progressive lower limb spasticity, contractures, talipes equinovarus, and muscle weakness, but with no significant facial dysmorphism. His first febrile seizure occurred before one year of age, followed by several afebrile seizures. The seizures had remitted after 3 to 4 years of antiepileptic therapy, and electroencephalography was normal. However, he had severe intellectual disability, and MRI revealed reduced white matter. WES identified a homozygous AP4S1 c. 289C>T (p.Arg97*) variant in the child, for which both of his parents were heterozygous carriers. The variant was rated as pathogenic based on the ACMG guidelines. Literature review has identified 8 publications on SPG52, involving 18 patients from 12 pedigrees. Combined with our case, 14 had carried homozygous variants of the AP4S1 gene, 3 had compound heterozygous variants, and 2 had heterozygous variants, involving 12 distinct variant sites. The cohort included 7 males and 12 females. All patients exhibited progressive lower limb spasticity and weakness as the primary feature, with certain loss of independent ambulation. Most patients had intellectual disability, some had distinctive facial features, though febrile seizures or epilepsy were common. Electroencephalography often showed increased slow-wave activity. Brain MRI frequently demonstrated ventriculomegaly, a thin corpus callosum, and reduced white matter. Conclusion:The homozygous c. 289C>T (p.Arg97*) variant of the AP4S1 gene probably underlay the pathogenesis of SPG52 in this child. Above discovery has expanded the mutational spectrum of AP4S1 and provided valuable insights for the genetic diagnosis, counseling, and clinical management of SPG52.