The disease-syndrome combination animal model in traditional Chinese medicine （TCM） andrology serves as an important bridge linking TCM theory with modern medical research， providing a key experimental platform for elucidating the 'syndrome-disease' correlation mechanism in male-specific diseases and for screening effective prescriptions. This article reviews recent progress in animal model research on common TCM andrological diseases， including prostatic diseases， sexual dysfunction， and male infertility， with a focus on analyzing the application， advantages， and disadvantages of various modeling strategies， such as immune induction， hormonal intervention， and multi-factor combination across different syndrome types. However， despite breakthroughs in model construction techniques， current research still faces several challenges， including insufficient standardization of syndrome differentiation and difficulties in quantifying TCM-specific indicators. Future studies need to optimize model evaluation systems by integrating modern technologies， in order to promote the standardization and internationalization of TCM andrology research.

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 propose a framework for risk assessment and stepwise management of pulmonary nodules based on existing guidelines and key studies, and to clarify the boundaries for triage, workup escalation, and referral in different detection scenarios. Methods A problem-oriented evidence synthesis approach was used, prioritizing Chinese expert consensus, Fleischner Society Guidelines, British Thoracic Society (BTS) Guidelines, and Lung-RADS. This was supplemented by studies on risk models, imaging measurements, liquid biopsy, tissue sampling, and surgical treatments. This paper is not presented as a formal consensus or a validated clinical pathway. Results A three-tiered framework consisting of basic non-invasive assessment, advanced assessment, and invasive diagnosis was established. It specified the applicable populations, boundaries for the use of risk models, imaging review, management of conflicting results, evaluation of histopathological consistency, and post-intervention follow-up. Liquid biopsy, genetic testing, and artificial intelligence were defined as investigational or adjunctive tools. Conclusion This framework can serve as a preliminary reference for multidisciplinary teams in developing local protocols. It cannot replace applicable guidelines, pathological diagnosis, shared decision-making with patients, or real-world validation.

ObjectiveTo identify the direct target proteins for the anti-obesity effect of resveratrol （Res） via chemical proteomic analysis and systematically explore the molecular mechanisms by which Res inhibits lipid accumulation in adipocytes from mitochondrial function regulation， adipocyte browning， and inflammation inhibition， thus providing new targets and experimental evidence for the prevention and treatment of obesity and related metabolic diseases. MethodsThe cell counting kit-8 （CCK-8） assay was adopted to detect the safe and effective doses （7.5， 15， 30 μmol·L^-1） of Res for treating 3T3-L1 cells based on the cell survival rate. After being induced to differentiate into mature adipocytes， the cells were assigned into the blank ［treated with dimethyl sulfoxide （DMSO）］ group and the Res （7.5， 15， 30 μmol·L^-1） groups and treated for 48 h. Oil red O staining and total triglyceride （TG） content were assessed to measure lipid accumulation in adipocytes. Target protein identification of Res was conducted through IAA-P-based chemical proteomics combined with high-resolution mass spectrometry. Target proteins of Res were validated by cellular thermal shift assay （CETSA） combined with Western blot analysis. Furthermore， the expression of browning markers peroxisome proliferator-activated receptor γ coactivator-1α （PGC-1α） and mitochondrial uncoupling protein 1 （UCP1）， as well as the target proteins NADH-ubiquinone oxidoreductase core subunit S8 （NDUFS8） and high-mobility group protein B1 （HMGB1） were determined by Western blot. The ATP assay kit was used to measure the ATP concentration. Mitochondrial biogenesis was characterized with MitoTracker Green， on the basis of which the regulatory effect of Res on mitochondrial function was analyzed. The level of the inflammatory cytokine tumor necrosis factor-α （TNF-α） was measured by enzyme-linked immunosorbent assay （ELISA）， on the basis of which the anti-inflammatory effect of Res on adipocytes was evaluated. ResultsCompared with the control group， Res demonstrated safety and efficacy within the range of 7.5-30 μmol·L^-1， without significantly affecting cell viability. Compared with the control group， different concentrations of Res reduced lipid accumulation in 3T3-L1 adipocytes （P<0.01）， promoted the expression of NDUFS8 （P<0.01）， increased the ATP concentration and the protein levels of UCP1 and PGC-1α， elevated the level of TNF-α （P<0.05， P<0.01）. ConclusionNDUFS8 and HMGB1were identified as the target proteins of Res in mitigating obesity. Res promotes mitochondrial biogenesis to induce the browning of 3T3-L1 adipocytes via targeting NDUFS8 and it inhibits the TNF-α-mediated inflammatory signaling via targeting HMGB1， thus inhibiting lipid accumulation and ameliorating obesity.

Pediatric nuclear medicine achieve precise functional and metabolic assessments with renal dynamic imaging,bone scintigraphy and 18F-FDG PET/CT,playing irreplaceable role for diagnosis and treatment of pediatric diseases.The emergence of novel molecular imaging probes,such as 68 Ga-DOT AT ATE,18F-DOPA and 18F-MFBG,expand clinical application field of pediatric nuclear medicine,while radionuclide therapy using 131I,131I-MIBG and 177 Lu-DOT AT ATE offer targeted options for pediatric thyroid cancer and neuroendocrine tumors.The current status,challenges and future prospect of pediatric nuclear medicine were reviewed in this article.

Significant advancements have been achieved in both basic and clinical research pertaining to thyroid cancer,leading to substantial updates in clinical diagnostic and therapeutic protocols and guidelines for various thyroid cancer subtypes.The advent and widespread application of next-generation sequencing technology in clinical practice have brought protein kinase fusion-driven thyroid cancer into sharper focus.This particular subtype accounts for over 10％of papillary thyroid carcinomas and frequently exhibits more aggressive histological characteristics and biological behaviors.The identification of kinase fusion-related thyroid cancer and the implementation of optimal detection strate-gies pose notable challenges for pathologists in China.This article provides a comprehensive review of the molecular mechanisms underlying phosphorylation signal transduction driven by kinase fusion,the pathological characteristics and detection methodologies of kinase fusion-related thyroid cancer,as well as the progress in research on related targeted therapies.The aim is to enhance the understanding of kinase fusion-related thyroid cancer,with the aspiration of facili-tating personalized and optimal diagnostic and therapeutic strategies in daily clinical practice,ultimately maximizing pa-tient outcomes.

Objective To provide a reference for medication therapy management(MTM)of diabetic patients complicated with abnormal blood pressure fluctuation.Methods A 71-year-old female diabetic patient with combined hypertension and abnormal blood pressure fluctuations was referred by the doctor to the pharmacy clinic.The pharmacists provided MTM services through pharmacy inquiry,medication evaluation,medication reconciliation,drug use education,and pharmacy follow-up.They reconciliated the medication regimen based on plasma glucose levels and dynamic blood pressure rhythms.Results Through twelve times MTM services for fourteen weeks,the atherosclerotic cardiovascular disease(ASCVD)risk factors,including morning peak blood pressure,fasting plasma glucose(FPG),plasma glucose two hours post breakfast(P2h PG)and glycosylated hemoglobin(HbA1c),reduced from 163/115 mmHg,7.54 mmol·L-1,12.87 mmol·L-1 and 7.2%before MTM to 137/84 mmHg,6.42 mmol·L-1,8.79 mmol·L-1 and 6.4%after MTM services,and the trough blood pressure post breakfast raised from 86/64mmHg to 115/76 mmHg.The patient's plasma glucose and blood pressure were effectively managed and controlled,and the abnormal non-spoon-shaped hypertension rhythm changed to the normal spoon-shaped diurnal blood pressure rhythm,and the patient's dizziness symptoms disappeared after breakfast.Conclusion The hospital develops pharmacy clinics,where pharmacists provide MTM services for patients with chronic diseases such as hypertension and diabetes,promote clinical rational drug use,and improve the level of patient health management.

Pediatric nuclear medicine achieve precise functional and metabolic assessments with renal dynamic imaging,bone scintigraphy and 18F-FDG PET/CT,playing irreplaceable role for diagnosis and treatment of pediatric diseases.The emergence of novel molecular imaging probes,such as 68 Ga-DOT AT ATE,18F-DOPA and 18F-MFBG,expand clinical application field of pediatric nuclear medicine,while radionuclide therapy using 131I,131I-MIBG and 177 Lu-DOT AT ATE offer targeted options for pediatric thyroid cancer and neuroendocrine tumors.The current status,challenges and future prospect of pediatric nuclear medicine were reviewed in this article.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.