Purpose: The standardized uptake value (SUV) is a key quantitative index in nuclear medicine imaging; however, variations in region‐of‐interest (ROI) determination exist across institutions. This study aims to standardize SUV evaluation by introducing a deep learning‐based quantitative analysis method that enhances diagnostic and prognostic accuracy. Methods: We used the Swin UNETR model to automatically segment key organs (breast, liver, spleen, and bone marrow) critical for breast cancer prognosis. Tumor segmentation was performed iteratively based on predefined SUV thresholds, and prognostic information was extracted from the liver, spleen, and bone marrow (reticuloendothelial system). The artificial intelligence training process employed 3 datasets: a test dataset (40 patients), a validation dataset (10 patients), and an independent test dataset (10 patients). To validate our approach, we compared the SUV values obtained using our method with those produced by commercial software. Results: In a dataset of 10 patients, our method achieved an auto‐segmentation accuracy of 0.9311 for all target organs. Comparison of maximum SUV and mean SUV values from our automated segmentation with those from traditional single‐ROI methods revealed differences of 0.19 and 0.16, respectively, demonstrating improved reliability and accuracy in whole‐organ SUV analysis. Conclusion This study successfully standardized SUV calculation in nuclear medicine imaging through deep learning‐based automated organ segmentation and SUV analysis, significantly enhancing accuracy in predicting breast cancer prognosis.

Purpose: The standardized uptake value (SUV) is a key quantitative index in nuclear medicine imaging; however, variations in region‐of‐interest (ROI) determination exist across institutions. This study aims to standardize SUV evaluation by introducing a deep learning‐based quantitative analysis method that enhances diagnostic and prognostic accuracy. Methods: We used the Swin UNETR model to automatically segment key organs (breast, liver, spleen, and bone marrow) critical for breast cancer prognosis. Tumor segmentation was performed iteratively based on predefined SUV thresholds, and prognostic information was extracted from the liver, spleen, and bone marrow (reticuloendothelial system). The artificial intelligence training process employed 3 datasets: a test dataset (40 patients), a validation dataset (10 patients), and an independent test dataset (10 patients). To validate our approach, we compared the SUV values obtained using our method with those produced by commercial software. Results: In a dataset of 10 patients, our method achieved an auto‐segmentation accuracy of 0.9311 for all target organs. Comparison of maximum SUV and mean SUV values from our automated segmentation with those from traditional single‐ROI methods revealed differences of 0.19 and 0.16, respectively, demonstrating improved reliability and accuracy in whole‐organ SUV analysis. Conclusion This study successfully standardized SUV calculation in nuclear medicine imaging through deep learning‐based automated organ segmentation and SUV analysis, significantly enhancing accuracy in predicting breast cancer prognosis.

With the completion of the "Human Genome Project" and the smooth progress of the "Herbal Genome Project", the research wave of RNAomics is gradually advancing, opening the research gateway for the modernization of traditional Chinese medicine (TCM) and initiating the post-genome era of medicinal plant RNA research. Therefore, this article proposes for the first time the concept of HerbRNomes, which involves constructing databases of medicinal plant, medicinal fungus, and medicinal animal RNA at different stages, from different origins, and in different organs. This research aims to explore the role of HerbRNA in self-genetic information transmission, functional regulation, as well as cross-species regulation functional mechanisms and key technologies. It also investigates application scenarios, providing a theoretical basis and research ideas for the resistance of TCM or medicinal plants to adversity and stress, molecular assistant breeding, and the development of small nucleic acid drugs. This article reviews recent research progress in elucidating the molecular mechanisms of the transmission and expression of genetic information, self-regulation and cross-species regulation of herbs at the RNA level, along with key technologies. It proposes a development strategy for small nucleic acid drugs based on HerbRNomes, providing theoretical support and guidance for the modernization of TCM based on HerbRNomes research.

Purpose: The standardized uptake value (SUV) is a key quantitative index in nuclear medicine imaging; however, variations in region‐of‐interest (ROI) determination exist across institutions. This study aims to standardize SUV evaluation by introducing a deep learning‐based quantitative analysis method that enhances diagnostic and prognostic accuracy. Methods: We used the Swin UNETR model to automatically segment key organs (breast, liver, spleen, and bone marrow) critical for breast cancer prognosis. Tumor segmentation was performed iteratively based on predefined SUV thresholds, and prognostic information was extracted from the liver, spleen, and bone marrow (reticuloendothelial system). The artificial intelligence training process employed 3 datasets: a test dataset (40 patients), a validation dataset (10 patients), and an independent test dataset (10 patients). To validate our approach, we compared the SUV values obtained using our method with those produced by commercial software. Results: In a dataset of 10 patients, our method achieved an auto‐segmentation accuracy of 0.9311 for all target organs. Comparison of maximum SUV and mean SUV values from our automated segmentation with those from traditional single‐ROI methods revealed differences of 0.19 and 0.16, respectively, demonstrating improved reliability and accuracy in whole‐organ SUV analysis. Conclusion This study successfully standardized SUV calculation in nuclear medicine imaging through deep learning‐based automated organ segmentation and SUV analysis, significantly enhancing accuracy in predicting breast cancer prognosis.

Purpose: The standardized uptake value (SUV) is a key quantitative index in nuclear medicine imaging; however, variations in region‐of‐interest (ROI) determination exist across institutions. This study aims to standardize SUV evaluation by introducing a deep learning‐based quantitative analysis method that enhances diagnostic and prognostic accuracy. Methods: We used the Swin UNETR model to automatically segment key organs (breast, liver, spleen, and bone marrow) critical for breast cancer prognosis. Tumor segmentation was performed iteratively based on predefined SUV thresholds, and prognostic information was extracted from the liver, spleen, and bone marrow (reticuloendothelial system). The artificial intelligence training process employed 3 datasets: a test dataset (40 patients), a validation dataset (10 patients), and an independent test dataset (10 patients). To validate our approach, we compared the SUV values obtained using our method with those produced by commercial software. Results: In a dataset of 10 patients, our method achieved an auto‐segmentation accuracy of 0.9311 for all target organs. Comparison of maximum SUV and mean SUV values from our automated segmentation with those from traditional single‐ROI methods revealed differences of 0.19 and 0.16, respectively, demonstrating improved reliability and accuracy in whole‐organ SUV analysis. Conclusion This study successfully standardized SUV calculation in nuclear medicine imaging through deep learning‐based automated organ segmentation and SUV analysis, significantly enhancing accuracy in predicting breast cancer prognosis.

Purpose: The standardized uptake value (SUV) is a key quantitative index in nuclear medicine imaging; however, variations in region‐of‐interest (ROI) determination exist across institutions. This study aims to standardize SUV evaluation by introducing a deep learning‐based quantitative analysis method that enhances diagnostic and prognostic accuracy. Methods: We used the Swin UNETR model to automatically segment key organs (breast, liver, spleen, and bone marrow) critical for breast cancer prognosis. Tumor segmentation was performed iteratively based on predefined SUV thresholds, and prognostic information was extracted from the liver, spleen, and bone marrow (reticuloendothelial system). The artificial intelligence training process employed 3 datasets: a test dataset (40 patients), a validation dataset (10 patients), and an independent test dataset (10 patients). To validate our approach, we compared the SUV values obtained using our method with those produced by commercial software. Results: In a dataset of 10 patients, our method achieved an auto‐segmentation accuracy of 0.9311 for all target organs. Comparison of maximum SUV and mean SUV values from our automated segmentation with those from traditional single‐ROI methods revealed differences of 0.19 and 0.16, respectively, demonstrating improved reliability and accuracy in whole‐organ SUV analysis. Conclusion This study successfully standardized SUV calculation in nuclear medicine imaging through deep learning‐based automated organ segmentation and SUV analysis, significantly enhancing accuracy in predicting breast cancer prognosis.

This paper systematically summarizes the practical experience of the 2025 Dingri earthquake emergency medical rescue in Tibet. It analyzes the requirements for earthquake medical rescue under conditions of high-altitude hypoxia, low temperature, and low air pressure. The paper provides a detailed discussion on the strategic layout of earthquake medical rescue at the national level, local government level, and through social participation. It covers the construction of rescue organizational systems, technical systems, material support systems, and information systems. The importance of building rescue teams is emphasized. In high-altitude and cold conditions, rapid response, scientific decision-making, and multi-party collaboration are identified as key elements to enhance rescue efficiency. By optimizing rescue organizational structures, strengthening the development of new equipment, and promoting telemedicine technologies, the precision and effectiveness of medical rescue can be significantly improved, providing important references for future similar disaster rescues.

OBJECTIVE: To examine the therapeutic effect of Fangji Fuling Decoction (FFD) on sepsis through network pharmacological analysis combined with in vitro and in vivo experiments. METHODS: A sepsis mouse model was constructed through intraperitoneal injection of 20 mg/kg lipopolysaccharide (LPS). RAW264.7 cells were stimulated by 250 ng/mL LPS to establish an in vitro cell model. Network pharmacology analysis identified the key molecular pathway associated with FFD in sepsis. Through ectopic expression and depletion experiments, the effect of FFD on multiple organ damage in septic mice, as well as on cell proliferation and apoptosis in relation to the mitogen-activated protein kinase 14/Forkhead Box O 3A (MAPK14/FOXO3A) signaling pathway, was analyzed. RESULTS: FFD reduced organ damage and inflammation in LPS-induced septic mice and suppressed LPS-induced macrophage apoptosis and inflammation in vitro (P<0.05). Network pharmacology analysis showed that FFD could regulate the MAPK14/FOXO signaling pathway during sepsis. As confirmed by in vitro cell experiments, FFD inhibited the MAPK14 signaling pathway or FOXO3A expression to relieve LPS-induced macrophage apoptosis and inflammation (P<0.05). Furthermore, FFD inhibited the MAPK14/FOXO3A signaling pathway to inhibit LPS-induced macrophage apoptosis in the lung tissue of septic mice (P<0.05). CONCLUSION FFD could ameliorate the LPS-induced inflammatory response in septic mice by inhibiting the MAPK14/FOXO3A signaling pathway.
Mice ; Animals ; Mitogen-Activated Protein Kinase 14/metabolism* ; Wolfiporia ; Lipopolysaccharides/pharmacology* ; Sepsis/complications* ; Signal Transduction ; Inflammation/drug therapy* ; Oxygen Radioisotopes

Objective To explore the prognostic factor and its predictive value of patients with Wilson disease-related acute-on-chronic liver failure(WD-ACLF).Methods The clinical data of 70 patients diagnosed as WD-ACLF admitted to the Department of Encephalopathy of the First Affiliated Hospital of Anhui University of Chinese Medicine from January 1,2017 to January 1,2022 were retrospectively collected.According to the 12-week prognosis,patients were divided into survival group(n=36)and death group(n=34).The data of the two groups were analyzed by univariate and multivariate logistic analysis to screen the prognostic risk factors and evaluate their predictive value.The model coefficient is omnibus tested,and the model-fitting degree is evaluated by the Hosmer-Lemeshow test.ROC curve was used to analyze the prognostic value for WD-ACLF between the new model and chronic liver failure-sequential organ failure assessment(CLIF-SOFA)score,model for end-stage liver disease(MELD)score and Child-Turcotte-Pugh(CTP)score.Results A total of 70 WD-ACLF patients were enrolled in present study,including 36 cases in survival group[22 males and 14 females with median age of 30.0(17.3,40.0)]and 34 cases in death group[25 males and 9 females with median age of 34.0(28.8,41.0)].Univariate analysis showed that the course of disease,prothrombin time(PT),activated partial thromboplastin time(APTT)were shorter in survival group than that in death group,the white blood cells(WBC),international normalized ratio(INR),aspartate transaminase(AST),total bilirubin(TBIL),blood urea nitrogen(BUN),creatinine(Cre)and ceruloplasmin(CER)levels and the proportion of infection,ascites,and upper gastrointestinal bleeding were lower in survival group than those in death group,however,the proportion of infection,ascites and upper digestive bleeding in the survival group were lower than those in the death group.Meanwhile,the red blood cells(RBC),hemoglobin(Hb),Na+ and total cholesterol(TC)level in the survival group were higher than those in the death group(P<0.05 or P<0.01).The results of multivariate logistic regression analysis showed that disease course(OR=1.176,95%CI 1.043-1.325),INR(OR=7.635,95%CI 1.767-32.980),TBIL(OR=1.012,95%CI 1.003-1.021),and upper gastrointestinal bleeding(OR=11.654,95%CI 1.029-131.980)were independent risk factors affecting the prognosis of WD-ACLF(P<0.05).Based on the results of logistic regression analysis,a joint model for predicting the prognosis of WD-ACLF was established.The AUC of the model for evaluating the prognosis of WD-ACLF was 0.941,which was greater than the CLIF-SOFA score(AUC=0.802),MELD score(AUC=0.897),and CTP score(AUC=0.722).Conclusions The course of disease,TBIL,INR,and upper gastrointestinal bleeding are risk factors that affect the prognosis of WD-ACLF.The prognosis model established based on this can more accurately predict the prognosis of WD-ACLF patients,and its predictive value is superior to CLIF-SOFA score,MELD score,and CTP score.

Background: Genetic defects in the human thyroid-stimulating hormone (TSH) receptor (TSHR) gene can cause congenital hypothyroidism (CH). However, the biological functions and comprehensive genotype–phenotype relationships for most TSHR variants associated with CH remain unexplored. We aimed to identify TSHR variants in Chinese patients with CH, analyze the functions of the variants, and explore the relationships between TSHR genotypes and clinical phenotypes. Methods: In total, 367 patients with CH were recruited for TSHR variant screening using whole-exome sequencing. The effects of the variants were evaluated by in-silico programs such as SIFT and polyphen2. Furthermore, these variants were transfected into 293T cells to detect their Gs/cyclic AMP and Gq/11 signaling activity. Results: Among the 367 patients with CH, 17 TSHR variants, including three novel variants, were identified in 45 patients, and 18 patients carried biallelic TSHR variants. In vitro experiments showed that 10 variants were associated with Gs/cyclic AMP and Gq/11 signaling pathway impairment to varying degrees. Patients with TSHR biallelic variants had lower serum TSH levels and higher free triiodothyronine and thyroxine levels at diagnosis than those with DUOX2 biallelic variants. Conclusions We found a high frequency of TSHR variants in Chinese patients with CH (12.3%), and 4.9% of cases were caused by TSHR biallelic variants. Ten variants were identified as loss-of-function variants. The data suggest that the clinical phenotype of CH patients caused by TSHR biallelic variants is relatively mild. Our study expands the TSHR variant spectrum and provides further evidence for the elucidation of the genetic etiology of CH.