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.

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.

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.

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.

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.

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.

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.

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.