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.

ObjectiveColorectal cancer (CRC) is a highly heterogeneous malignancy, and traditional Chinese medicine (TCM) syndrome differentiation is widely used in its clinical management. However, the biological basis underlying different TCM syndromes remains insufficiently understood. This study aimed to investigate the proteomic differences between qi-blood deficiency syndrome (QBDS) and damp-heat syndrome (DHS) in CRC patients and to establish a “syndrome-differential protein-herbal component” network for exploring potential molecular mechanisms and therapeutic targets associated with syndrome-specific treatment strategies. MethodsTumor tissue samples were collected from 10 patients with pathologically confirmed CRC, including 5 patients diagnosed with QBDS and 5 with DHS according to standardized TCM syndrome differentiation criteria. Tandem mass tag (TMT)-based quantitative proteomics was employed to identify differentially expressed proteins (DEPs) between the two syndrome groups. Functional enrichment analyses, including Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, were performed to characterize the biological functions and signaling pathways associated with the identified DEPs. Least absolute shrinkage and selection operator (LASSO) regression analysis was further applied to screen key syndrome-related proteins and construct a core protein prediction model. Subsequently, molecular docking was conducted to evaluate the binding affinities between representative herbal compounds, astragaloside IV and berberine, and the identified core proteins. Finally, cell counting kit-8 (CCK-8) assays were performed in HCT 116 and HT-29 colorectal cancer cell lines to validate the anti-proliferative activities of these compounds in vitro. ResultsA total of 44 DEPs were identified between QBDS and DHS tissues, including 21 upregulated and 23 downregulated proteins in the QBDS group relative to the DHS group. Functional enrichment analyses revealed that these proteins were primarily associated with antiviral immune responses, regulation of inflammatory signaling, NF‑κB signaling pathways, cholesterol metabolism, and other biological processes relevant to tumor progression and host immune regulation. LASSO regression analysis identified eight core proteins with potential syndrome-specific significance. Among them, ST7, GPCPD1, and PODXL were closely associated with QBDS, whereas FAT1, GRB7, KRT7, MAFF, and MMP8 were associated with DHS. Molecular docking demonstrated favorable interactions between astragaloside IV and QBDS-related proteins, with binding energies lower than -5 kcal/mol, indicating stable binding activity. Berberine exhibited even lower binding energies and stronger binding specificity toward DHS-related core proteins. In vitro experiments further confirmed that both compounds inhibited CRC cell proliferation, while berberine displayed significantly greater anti-proliferative efficacy. The lowest 48 h IC₅₀ value of berberine reached 15.21 mg/L in CRC cells, indicating a stronger growth-inhibitory effect than astragaloside IV. ConclusionCRC patients with QBDS and DHS exhibit distinct proteomic characteristics, suggesting that different TCM syndromes possess unique molecular signatures. The identified core proteins may serve as potential biomarkers for syndrome differentiation, while astragaloside IV and berberine may exert therapeutic effects through targeting syndrome-specific molecular networks. These findings provide preliminary proteomic evidence supporting syndrome-guided precision integrative medicine in CRC and offer new insights into the biological basis of TCM syndrome differentiation and individualized therapeutic strategies.

Objective:To investigate the association between serum 25-hydroxyvitamin D [25(OH)D] levels and 24-h urinary calcium excretion (24-h UCaE) and hypercalciuria in Chinese adults.Methods:This cross-sectional study was conducted from March 2022 to March 2023 in nine cities in China and included 1 239 residents. Demographic characteristics were collected through questionnaires and physical examinations, fasting blood samples were assessed for bone metabolism indicators, and 24-h urine samples were used to determine the 24-h UCaE. Multiple linear regression analysis was used to explore the relationship between serum 25(OH)D and 24-h UCaE and bone metabolism indexes. The relationship between serum 25(OH)D and hypercalciuria was analyzed using a multiple logistic regression model combined with restricted cubic spline modeling.Results:The mean participant age was (47.9±18.1) years, of which 453 (36.6%) were male. The percentages of vitamin D sufficiency, insufficiency, and deficiency were 7.6% (94/1 239), 29.0% (359/1 239), and 63.4% (786/1 239), respectively. The multiple linear regression model showed that after adjusting for the covariates the 24-h UCaE gradually increased with higher levels of 25(OH)D ( P overall <0.001, P nonlinear <0.001). The logistic regression analysis revealed that compared with the vitamin D deficient group, the OR for the prevalence of hypercalciuria in the vitamin D sufficient and vitamin D insufficient groups were 3.290 (95% CI 1.745 to 6.202) and 3.742 (95% CI 2.458 to 5.697), respectively. The results of the restricted cubic spline modeling showed a positive nonlinear relationship between 25(OH)D and the prevalence of hypercalciuria ( P overall <0.001, P nonlinear <0.001). The prevalence of hypercalciuria increased when 25(OH)D was >17.00 μg/L and peaked at 26.71 μg/L, after which there was a decreasing trend in the prevalence of hypercalciuria with increasing 25(OH)D. Conclusion:Associations between serum 25(OH)D levels and urinary calcium excretion and the prevalence of hypercalciuria were observed in the Chinese adult population.

Lean six sigma (LSS) emphasizes patient demand-oriented, and continuously optimizing prolesses to achieve efficiency and standardization in medical services. Starting in 2021, a specialized children′s hospital (comprising one main campus and three branch campuses) introduced the LSS management method. Through define, measure, analyze, improve, and control, the hospital utilized tools such as questionnaire surveys, SIPOC models, and fishbone diagrams to identify pain points in the building of multi-campus child-friendly hospital and develop improvement measures.To address the main issues of insufficient cross-campus collaboration, significant disparities in service quality among different campuses, unclear functional positioning of each campus, and inadequate integration with social security mechanisms, the hospital implemented a " homogeneity-differentiation-coordination" management mode. The hospital implemented unified diagnosis and treatment standards and clinical pathways, carried out remote consultations and expert rotations, completed mutual recognition of examination and testing results, optimized child friendly labeling and child friendly environment, and built a " hospital-to-hospitals, hospital to medical schools, and hospital to community health centers" linkage platform, etc., to promote the standardization of diagnosis and treatment processes, clear functional positioning of the hospital area, and efficient resource allocation. Through practice, the outpatient appointment rate and patient satisfaction rate in the main hospital had increased from 86.72% and 98.64% in January December 2021 to 91.87% and 99.72% in January December 2024, respectively; The patient waiting time had been shortened from 26.54 minutes to 21.94 minutes, and the efficiency of medical treatment and service experience had been significantly improved. As of 2024, mutual recognition of 214 inspection and testing items had been achieved cross hospital campuses, forming a collaborative pattern of " main hospital leading, campuses support, and resource complementarity", and significantly improving the level of collaboration and child friendly connotation among multi-campus. This practice explored the integration path of multi-campus collaborative governance and child-friendly services, which could provide reference and inspiration for the similar hospitals.

Lung cancer is one of the most prevalent malignancies globally,with the highest incidence and mortality rates.Non-small cell lung cancer(NSCLC)accounts for 85%of all lung cancer cases.In China,both the incidence and mortality of lung cancer rank first among all ma-lignancies.Interstitial lung abnormalities(ILA)are non-dependent abnormalities incidentally detected on chest computed tomography(CT)scans.Some NSCLC patients may present with concurrent ILA at diagnosis,and ILA may significantly impact survival outcomes and treatment-related complications in NSCLC patients.However,the relationship between ILA and NSCLC remains under investigation.The integration of artificial intelligence(AI)technologies with multimodal data to develop novel and efficient comprehensive assessment models for ILA repres-ents a promising research frontier.This review aims to provide a comprehensive analysis of the correlation between ILA and NSCLC,the pro-gnostic implications of ILA in NSCLC patients,and the current advancements in AI-based technologies for ILA evaluation.The findings may of-fer valuable insights to guide future research directions on ILA in China.

Laccase is a type of polyphenol oxidase that can catalyze the oxidation of various substances,including phenols,aromatic amines,and catecholamines.It has been widely utilized in pollutant degradation and analytical applications.However,the high cost of preparation of natural laccase and its susceptibility to environmental factors,which can lead to denaturation and inactivation,limit its practical applications.Nanozymes,which are nanomaterials that exhibit enzyme-like properties,offer advantages such as easy preparation,adjustable activity,and exceptional stability.Currently,many types of nanozymes have been developed.Inspired by the coordination of Cu2+with amino acids in the active site of natural laccase,researchers have employed coordination synthetic strategies to prepare laccase-like nanozymes.The metal nodes in these laccase-like nanozymes include copper,manganese,and cerium,while the ligands involve a variety of chemicals like nucleotides,amino acids,polypeptides,and aromatic acids.By manipulating factors such as the metal-to-ligand ratio,reducing capacity of ligands,buffer solutions,chloride ions,bromine ions,the catalytic activity of laccase-like nanozymes can be finely tuned.In this paper,laccase-like nanozymes developed through coordination strategies were categorized and summarized,along with review of their analytical applications in detection of phenolic compounds,disease biomarkers,antibiotics,pesticides,sulfur-containing pollutants,and time-temperature indicators.Furthermore,the challenges currently faced in the research of laccase-like nanozymes and future research directions were discussed.

Forensic medicine has been designated as a first-level discipline,presenting new opportunities and challenges for the development of forensic medicine.Since the 1980s,the establishment of foren-sic medicine discipline and the cultivation of high-level forensic talents have become hot topics in the development of forensic medicine in China.Since the 13th Five-Year Plan,the forensic team of Shanxi Medical University has been aiming at the forefront,proposing the development goals of"Five First-class"and the discipline development path"Six Major Achievements".It has selected benchmark disci-plines,identified gaps in disciplinary development,unified thoughts,formulated completion timelines,concentrated superior resources,assigned tasks to individuals,and created an"Organized Fill-in-the-Blank Format"forensic medicine discipline construction model with the characteristics of the new era.The construction model of forensic medicine has achieved good results in the goals,discipline frame-work,scientific research,talent cultivation,discipline team and platform construction,forming a rela-tively complete discipline construction and management system,and accumulating valuable experience for the construction of first-level discipline and high-level talent cultivation of forensic medicine.

Objective To evaluate the reporting quality of randomized controlled trials on acupuncture cessation by using CONSORT statement and STRICTA checklist.Methods According to inclusion and exclusion criteria,9 Chinese and English databases such as CNKI and PubMed were searched by computer,and the retrieved literatures were evaluated according to the CONSORT statement 2010 and STRICTA checklist 2010.Results After exclusion,34 Chinese and English literatures were included,including 27 in Chinese and 7 in English.In a risk review using ROB2,the Cochrane Collaboration's bias risk assessment tool,29 reviews were judged to be high risk overall,2 to be at some risk,and 3 to be at low risk.In the CONSORT statement evaluation,the reporting rate of 23 items was less than 76.4%,including the reporting rate of 17 items was less than 47%.In the STRICTA checklist evaluation,the reporting rate of 9 items was less than 76.4%,including the reporting rate of 4 items was less than 29.4%.Conclusion There are still shortcomings in the methodological design and item reporting standards of acupuncture smoking cessation RCT.It is suggested that researchers should strictly follow the CONSORT declaration and STRICTA list in designing RCT schemes and reporting RCT results in the future.

Under the background of rapid development of artificial intelligence(AI),this paper systematically proposes AI-based education(AIBE).It empowers the teaching process,learning process,research process,and teaching management with AI,and constructs an AI-based educational paradigm,including AI-based teaching(AIBT),AI-based learning(AIBL),AI-based re-search(AIBR),and AI-based management(AIBM).Taking the AI course of immunology teaching as an example,this paper deeply analyzes the practices and explorations of implementing AIBT,AIBL,AIBR and AIBM based on the AI course,so as to accelerate the promotion of the transformation of the fourth generation of medical education.

OBJECTIVE: Hepatocellular carcinoma (HCC) is sensitive to ferroptosis, a new form of programmed cell death that occurs in most tumor types. However, the mechanism through which ferroptosis modulates HCC remains unclear. This study aimed to investigate the oncogenic role and prognostic value of FANCD2 and provide novel insights into the prognostic assessment and prediction of immunotherapy. METHODS: Using clinicopathological parameters and bioinformatic techniques, we comprehensively examined the expression of FANCD2 macroscopically and microcosmically. We conducted univariate and multivariate Cox regression analyses to identify the prognostic value of FANCD2 in HCC and elucidated the detailed molecular mechanisms underlying the involvement of FANCD2 in oncogenesis by promoting iron-related death. RESULTS: FANCD2 was significantly upregulated in digestive system cancers with abundant immune infiltration. As an independent risk factor for HCC, a high FANCD2 expression level was associated with poor clinical outcomes and response to immune checkpoint blockade. Gene set enrichment analysis revealed that FANCD2 was mainly involved in the cell cycle and CYP450 metabolism. CONCLUSION To the best of our knowledge, this is the first study to comprehensively elucidate the oncogenic role of FANCD2. FANCD2 has a tumor-promoting aspect in the digestive system and acts as an independent risk factor in HCC; hence, it has recognized value for predicting tumor aggressiveness and prognosis and may be a potential biomarker for poor responsiveness to immunotherapy.
Humans ; Carcinoma, Hepatocellular/diagnosis* ; Liver Neoplasms/diagnosis* ; Immunotherapy ; Fanconi Anemia Complementation Group D2 Protein/metabolism* ; Prognosis ; Male ; Female ; Middle Aged ; Biomarkers, Tumor/metabolism*