The welfare and ethics of laboratory animals are the ethical principles and behavioral norms that need to be followed in conducting animal-based scientific research, breeding and managing laboratory animals, and supervising and regulating such activities. The level of protection of laboratory animal welfare and ethics is closely related to the development of science and technology, which has become a widely recognized international consensus. At present, Guangdong Province is accelerating the construction of a high-level science and technology innovation province and the Guangdong-Hong Kong-Macao Greater Bay Area International Science and Technology Innovation Center. Guangdong Province should rely on its advanced governance capacity in the field of laboratory animal science and technology ethics to promote the high-quality development of its laboratory animal science and technology sector. Based on the management laws, regulations, and institutional mechanisms of laboratory animals in China, this paper explores the optimization of the laboratory animal science and technology ethics governance system, which includes the institutional guarantees, responsibility systems, ethical review and supervision mechanisms, and education and outreach. Through methods such as literature research, questionnaire surveys, and interview investigations, an empirical study of the laboratory animal science and technology ethics governance system in Guangdong Province has been conducted. Analysis of literature and research results shows that Guangdong Province has basically established a laboratory animal management system, collaboration mechanism, supervision mechanism, and education and training system that meet the current requirements of the laboratory animal science and technology ethics governance system in China. However, there are still problems such as an incomplete laboratory animal science and technology ethics supervision mechanism, an underdeveloped operation mechanism of review institutions, insufficient attention paid by laboratory animal units to the ethical review of animal experiments, inconsistent ethical review standards, and a lack of professional ethical education and training for ethics review personnel. Therefore, optimization measures such as improving the laboratory animal science and technology ethics review system, strengthening supervision and inspection, further strengthening the accountability of responsible entities, formulating review norms, and enhancing hierarchical and classified education and training are proposed, to provide a theoretical basis for promoting the normalized and long-term governance of laboratory animal science and technology ethics in Guangdong Province.

Pancreatic cancer is a highly malignant solid tumor of the digestive system with extremely poor treatment prognosis. Although its incidence rate is low， its mortality rate is extremely high. In recent years， the number of diagnosed cases worldwide has continued to rise， making pancreatic cancer the sixth leading cause of cancer-related deaths globally. Currently， clinical treatment primarily relies on operation and chemotherapy to suppress tumors. However， these approaches face challenges such as suboptimal efficacy， high postoperative recurrence rates， and severe adverse reactions. Therefore， identifying safe and effective treatment modalities remains a pressing challenge for the medical community. In recent years， research on traditional Chinese medicine （TCM） interventions for pancreatic cancer has increased significantly. Multiple studies have shown that single-herb TCM， TCM formulas， and their derived single compounds can regulate the levels of tumor cell signaling pathways through multiple action targets. They inhibit the development and progression of pancreatic cancer by inhibiting cancer cell proliferation， promoting cell apoptosis， inhibiting tumor angiogenesis， reducing cancer cell invasion and migration capabilities， regulating the cell cycle， and modulating the tumor microenvironment. Additionally， TCM has the advantages of significantly enhancing the anticancer efficacy of chemotherapy drugs and causing fewer adverse reactions. However， the specific action mechanisms by which TCM intervenes in pancreatic cancer remain unclear. Further extensive research is still needed to validate the role of regulating classical signaling pathways such as phosphoinositide 3-kinase （PI3K）/protein kinase B （Akt）/mammalian target of rapamycin （mTOR）， Wnt/β-catenin， nuclear transcription factor-κB （NF-κB）， notch， and hedgehog in the treatment of pancreatic cancer. Therefore， this paper reviewed Chinese and international studies on TCM intervention in pancreatic cancer through relevant signaling pathways in recent years， summarized the potential action mechanisms of TCM in the treatment of pancreatic cancer， and provided references for related research in the future.

BACKGROUND:Sarcopenia is an age-related condition characterized by the loss of skeletal muscle mass,strength,and/or physical function.Currently,effective treatments for sarcopenia remain limited.A new therapeutic approach to improve symptoms and prognosis of sarcopenia patients clinically was important.OBJECTIVE:To explore the effects of canine adipose-derived mesenchymal stem cells and their exosomes on a dexamethasone-induced sarcopenia in mice.METHODS:Mesenchymal stem cells were isolated and cultured from canine adipose tissue,and identified and functionally evaluated through flow cytometry and differentiation assays for osteogenesis,adipogenesis,and chondrogenesis.Subsequently,exosomes from adipose-derived mesenchymal stem cells were extracted and characterized using transmission electron microscopy,western blot assay,and nanocoulter tracking analysis.In vitro,the effects of canine adipose-derived mesenchymal stem cells and their exosomes on myotube growth and the expression of muscle atrophy-related genes were investigated using dexamethasone-induced C2C12 myotube atrophy and aging C2C12 models.In vivo,a dexamethasone-induced mouse sarcopenia model was established and received intraperitoneal or intravenous injection of canine adipose-derived mesenchymal stem cells.Therapeutic efficacy was assessed through mouse rotarod performance,histopathological analysis,and muscle atrophy-related genes testing.RESULTS AND CONCLUSION:(1)The isolated canine adipose-derived mesenchymal stem cells highly expressed CD73,CD90,and CD105,and lowly expressed MHC-Ⅱ,CD14,CD19,CD34,and CD45,and successfully differentiated into osteoblasts,adipocytes,and chondrocytes in vitro.(2)The adipose-derived mesenchymal stem cells-derived exosomes met the identification criteria in terms of particle size,electron microscopy morphology,and positive expression of specific markers.(3)Compared to the dexamethasone-induced C2C12 atrophy group,treatment with adipose-derived mesenchymal stem cells and their exosomes promoted the recovery and growth of myotubes,inhibited the expression of muscle atrophy-related genes MuRF1 and Atrogin-1.(4)Compared to the aging C2C12 group,adipose-derived mesenchymal stem cells and their exosomes significantly enhanced the recovery and growth of aged muscle tubes in aging cells.(5)Compared to the control group,the rotarod time in dexamethasone-induced sarcopenia model mice was significantly decreased(P＜0.01).After 7 days(P＜0.01,P＜0.01)and 10 days(P＜0.01,P＜0.05)of adipose-derived mesenchymal stem cells treatment via intraperitoneal and intravenous injection,rotarod time was significantly increased,respectively.After 14 days,all treatment groups showed longer rotarod times than the model group,although with no significant differences between them.(6)Compared to the control group,the cross-sectional area of anterior tibial muscle in the model group was significantly reduced(P＜0.01),and it was significantly increased after intraperitoneal and intravenous administration of adipose-derived mesenchymal stem cells(P＜0.05,P＜0.01).(7)Compared to the model group,intraperitoneal and intravenous administration of adipose-derived mesenchymal stem cells significantly inhibited the mRNA expression of MuRF1 and Atrogin-1 genes(P＜0.01,P＜0.01,P＜0.01,P＜0.01).The results indicated that adipose-derived mesenchymal stem cells and their exosomes promoted recovery and growth of atrophic myotube cells by inhibiting the expression of muscle atrophy-related genes,and both intraperitoneal and intravenous administration of adipose-derived mesenchymal stem cells provided good therapeutic effects on sarcopenia in mice.

ObjectiveFlavonoids are clinically significant natural products, yet their oxygen-glycosylation in aqueous environments relies heavily on expensive nucleotide-activated sugar donors such as UDP-glucose. Liquid-liquid phase separation (LLPS) creates specialized, membraneless physicochemical microenvironments capable of modulating enzymatic functions and overcoming mass transfer limitations. This study aims to investigate whether the gut microbiota-derived DgpB/C complex—a multienzyme system traditionally recognized for cleaving stable C-glycosidic bonds and facilitating isomerization—can undergo functional remodeling within phase-separated condensates. Our core objective is to elucidate the role of phase separation in expanding enzymatic catalytic plasticity and to provide a non-canonical, highly cost-effective biocatalytic mechanism for the direct utilization of free sugars in the synthesis of O-glycosylated natural products. MethodsAn artificial phase-separation platform was constructed utilizing the multivalent arginine-glycine-glycine motif (RGG)-repeat domain derived from the Caenorhabditis elegans LAF-1 protein. To ensure precise spatial compartmentalization, the DgpB/C complex was specifically recruited into the RGG condensates via a high-affinity SZ1/SZ2 heterodimerization tag system. Condensate formation and substrate partitioning were visualized using light and confocal fluorescence microscopy. The chemical structures and regioselectivity of the reaction products were rigorously characterized using high-performance liquid chromatography (HPLC) and liquid chromatography-mass spectrometry (LC-MS)/MS systems. Furthermore, molecular docking and 20-ns molecular dynamics (MD) simulations were performed via the Hermite platform and Uni-GBSA pipeline to elucidate the structural and thermodynamic basis underlying the phase-transition-induced functional shift. ResultsWe observed that the condensates formed by RGG proteins selectively recruited and significantly enriched hydrophobic flavonoid substrates. Strikingly, within the LLPS microenvironment, the DgpB/C complex—which typically exhibits only degradative or isomerase activities—underwent a profound functional remodeling, transforming into an efficientO-glycosyltransferase. Diverging from canonical pathways that require high-energy donors, the sequestered DgpB/C complex directly utilized unactivated free glucose to catalyze the formation of new O-glycosidic bonds. This remodeled activity was found to be evolutionarily conserved across DgpB/C homologs from diverse gut microbiota strains, such as P581a and W974-1. LC-MS/MS analysis further revealed that the phase-separated environment reduced the regioselectivity constraints of the enzyme, enabling catalytic action on multiple hydroxyl positions of the flavonoid scaffold. MD simulations further indicated that the low-water-activity microenvironment of the condensates reconfigured the conformational dynamics of the catalytic pocket, favoring a spatial orientation highly conducive to dehydration condensation. ConclusionThis study demonstrates that LLPS drives the functional remodeling of the gut microbiota enzyme DgpB/C through the reconfiguration of the physicochemical microenvironment. These findings challenge traditional perceptions regarding the functional boundaries of metabolic enzymes and reveal a novel biocatalytic pathway that bypasses the requirement for nucleotide-activated sugars. Consequently, this provides a highly promising artificial compartmentalization strategy for the green manufacturing of complex, high-value-added natural products.

Objective To evaluate the application value of artificial intelligence (AI) in the quality control of mammographic images and explore its feasibility for improving image quality. Methods A retrospective analysis was conducted on 500 mammographic images from 125 female patients. These images were acquired in December 2024 at the Department of Radiology, Obstetrics and Gynecology Hospital of Tongji University by two junior technologists with qualification certificate for junior radiologic technologists and ≤ 2 months of independent operation. The reference standard was the evaluation by a panel of senior experts comprising one associate chief radiologist and two intermediate radiologic technologists with over 10 years of experience in mammography. The evaluation was based on nine criteria within an AI quality control system. The correlation and consistency (Kappa test) of the assessment outcomes were compared among the junior technologist group, the AI quality control group, and the panel group. Additionally, the differences in the proportions of high-, medium-, and low-quality images rated by the three groups were analyzed. After one month of AI assistance, 200 images from 50 additional patients acquired by the same two junior technologists in January 2025 were collected. The area under the receiver operating characteristic curve and 95% confidence interval (95%CI) were calculated for the assessment performance of the junior technologist group before and after AI assistance. Differences were compared using the DeLong test. Changes in the disqualification rates for the nine criteria were analyzed. Results In consistency analysis, the AI quality control group showed high consistency with the panel group across multiple key criteria, with Kappa values ranging from 0.41 to 1.00. In contrast, the consistency between the junior technologist group and the panel group was generally low, with Kappa values ranging from 0.13 to 0.49. In comparison of image quality classification, no significant differences were observed in image quality classification between the AI group and the panel group (P>0.05). However, the proportion of images rated as high quality by the junior technologist group was significantly higher, while the proportions rated as medium and low quality were significantly lower, compared to those rated by the panel group (P<0.05). After AI assistance, the area under the receiver operating characteristic curve for the assessment performance of junior technologist group increased significantly from 0.56 (95%CI: 0.51-0.61) to 0.91 (95%CI: 0.87-0.94) (P<0.001). The disqualification rates for skin folds, incomplete inclusion of the pectoralis major muscle, and nipple not in profile decreased significantly. Conclusion AI demonstrates assessment capability comparable to the panel group in mammographic quality control. AI effectively enhances the quality of images acquired by junior technologists through real-time, objective feedback.

Radiotherapy is a cornerstone in the treatment of malignant tumors. It induces DNA damage through high-energy radiation, preferentially eliminating rapidly proliferating tumor cells. However, its clinical efficacy is often limited by tumor radioresistance and collateral damage to normal tissues. Consequently, elucidating the cellular response mechanisms to radiation stress and identifying key targets that can both sensitize tumor cells and protect normal tissues have become critical strategies for improving radiotherapy outcomes. Radiation stress triggers structural remodeling of the nucleolus, leading to the dissociation of certain ribosomal proteins from the ribosome and enabling them to acquire extra-ribosomal functions. Among these, RPL11 can be released and specifically binds to MDM2, thus inhibiting its E3 ubiquitin ligase activity, stabilizing p53, and mediating cell cycle arrest and apoptosis. The RPL11-MDM2-p53 pathway, acting as a signaling hub that links nucleolar dysfunction to cell fate determination, plays a pivotal role in maintaining genomic stability and regulating cellular responses to radiation. This review first introduces the basic characteristics of RPL11 and elucidates the molecular basis of radiation-induced ribosomal stress. It then outlines the core regulatory mechanisms of the cell cycle. On this basis, it focuses on the mechanisms by which radiation-induced RPL11 regulates the cell cycle and analyzes the specific effects of RPL11 on cell cycle. Furthermore, it discusses the role of the RPL11-MDM2-p53 pathway in cell cycle regulation. Finally, it explores the role of this pathway in maintaining genomic stability and determining cell fate, and highlights its potential value as a target for radiosensitization, aiming to provide new perspectives for enhancing tumor radiosensitivity and reducing damage to normal tissues.

Objective To evaluate the application value of artificial intelligence (AI) in the quality control of mammographic images and explore its feasibility for improving image quality. Methods A retrospective analysis was conducted on 500 mammographic images from 125 female patients. These images were acquired in December 2024 at the Department of Radiology, Obstetrics and Gynecology Hospital of Tongji University by two junior technologists with qualification certificate for junior radiologic technologists and ≤ 2 months of independent operation. The reference standard was the evaluation by a panel of senior experts comprising one associate chief radiologist and two intermediate radiologic technologists with over 10 years of experience in mammography. The evaluation was based on nine criteria within an AI quality control system. The correlation and consistency (Kappa test) of the assessment outcomes were compared among the junior technologist group, the AI quality control group, and the panel group. Additionally, the differences in the proportions of high-, medium-, and low-quality images rated by the three groups were analyzed. After one month of AI assistance, 200 images from 50 additional patients acquired by the same two junior technologists in January 2025 were collected. The area under the receiver operating characteristic curve and 95% confidence interval (95%CI) were calculated for the assessment performance of the junior technologist group before and after AI assistance. Differences were compared using the DeLong test. Changes in the disqualification rates for the nine criteria were analyzed. Results In consistency analysis, the AI quality control group showed high consistency with the panel group across multiple key criteria, with Kappa values ranging from 0.41 to 1.00. In contrast, the consistency between the junior technologist group and the panel group was generally low, with Kappa values ranging from 0.13 to 0.49. In comparison of image quality classification, no significant differences were observed in image quality classification between the AI group and the panel group (P>0.05). However, the proportion of images rated as high quality by the junior technologist group was significantly higher, while the proportions rated as medium and low quality were significantly lower, compared to those rated by the panel group (P<0.05). After AI assistance, the area under the receiver operating characteristic curve for the assessment performance of junior technologist group increased significantly from 0.56 (95%CI: 0.51-0.61) to 0.91 (95%CI: 0.87-0.94) (P<0.001). The disqualification rates for skin folds, incomplete inclusion of the pectoralis major muscle, and nipple not in profile decreased significantly. Conclusion AI demonstrates assessment capability comparable to the panel group in mammographic quality control. AI effectively enhances the quality of images acquired by junior technologists through real-time, objective feedback.

Radiotherapy is a cornerstone in the treatment of malignant tumors. It induces DNA damage through high-energy radiation, preferentially eliminating rapidly proliferating tumor cells. However, its clinical efficacy is often limited by tumor radioresistance and collateral damage to normal tissues. Consequently, elucidating the cellular response mechanisms to radiation stress and identifying key targets that can both sensitize tumor cells and protect normal tissues have become critical strategies for improving radiotherapy outcomes. Radiation stress triggers structural remodeling of the nucleolus, leading to the dissociation of certain ribosomal proteins from the ribosome and enabling them to acquire extra-ribosomal functions. Among these, RPL11 can be released and specifically binds to MDM2, thus inhibiting its E3 ubiquitin ligase activity, stabilizing p53, and mediating cell cycle arrest and apoptosis. The RPL11-MDM2-p53 pathway, acting as a signaling hub that links nucleolar dysfunction to cell fate determination, plays a pivotal role in maintaining genomic stability and regulating cellular responses to radiation. This review first introduces the basic characteristics of RPL11 and elucidates the molecular basis of radiation-induced ribosomal stress. It then outlines the core regulatory mechanisms of the cell cycle. On this basis, it focuses on the mechanisms by which radiation-induced RPL11 regulates the cell cycle and analyzes the specific effects of RPL11 on cell cycle. Furthermore, it discusses the role of the RPL11-MDM2-p53 pathway in cell cycle regulation. Finally, it explores the role of this pathway in maintaining genomic stability and determining cell fate, and highlights its potential value as a target for radiosensitization, aiming to provide new perspectives for enhancing tumor radiosensitivity and reducing damage to normal tissues.

Objective To evaluate the application value of artificial intelligence (AI) in the quality control of mammographic images and explore its feasibility for improving image quality. Methods A retrospective analysis was conducted on 500 mammographic images from 125 female patients. These images were acquired in December 2024 at the Department of Radiology, Obstetrics and Gynecology Hospital of Tongji University by two junior technologists with qualification certificate for junior radiologic technologists and ≤ 2 months of independent operation. The reference standard was the evaluation by a panel of senior experts comprising one associate chief radiologist and two intermediate radiologic technologists with over 10 years of experience in mammography. The evaluation was based on nine criteria within an AI quality control system. The correlation and consistency (Kappa test) of the assessment outcomes were compared among the junior technologist group, the AI quality control group, and the panel group. Additionally, the differences in the proportions of high-, medium-, and low-quality images rated by the three groups were analyzed. After one month of AI assistance, 200 images from 50 additional patients acquired by the same two junior technologists in January 2025 were collected. The area under the receiver operating characteristic curve and 95% confidence interval (95%CI) were calculated for the assessment performance of the junior technologist group before and after AI assistance. Differences were compared using the DeLong test. Changes in the disqualification rates for the nine criteria were analyzed. Results In consistency analysis, the AI quality control group showed high consistency with the panel group across multiple key criteria, with Kappa values ranging from 0.41 to 1.00. In contrast, the consistency between the junior technologist group and the panel group was generally low, with Kappa values ranging from 0.13 to 0.49. In comparison of image quality classification, no significant differences were observed in image quality classification between the AI group and the panel group (P>0.05). However, the proportion of images rated as high quality by the junior technologist group was significantly higher, while the proportions rated as medium and low quality were significantly lower, compared to those rated by the panel group (P<0.05). After AI assistance, the area under the receiver operating characteristic curve for the assessment performance of junior technologist group increased significantly from 0.56 (95%CI: 0.51-0.61) to 0.91 (95%CI: 0.87-0.94) (P<0.001). The disqualification rates for skin folds, incomplete inclusion of the pectoralis major muscle, and nipple not in profile decreased significantly. Conclusion AI demonstrates assessment capability comparable to the panel group in mammographic quality control. AI effectively enhances the quality of images acquired by junior technologists through real-time, objective feedback.

Radiotherapy is a cornerstone in the treatment of malignant tumors. It induces DNA damage through high-energy radiation, preferentially eliminating rapidly proliferating tumor cells. However, its clinical efficacy is often limited by tumor radioresistance and collateral damage to normal tissues. Consequently, elucidating the cellular response mechanisms to radiation stress and identifying key targets that can both sensitize tumor cells and protect normal tissues have become critical strategies for improving radiotherapy outcomes. Radiation stress triggers structural remodeling of the nucleolus, leading to the dissociation of certain ribosomal proteins from the ribosome and enabling them to acquire extra-ribosomal functions. Among these, RPL11 can be released and specifically binds to MDM2, thus inhibiting its E3 ubiquitin ligase activity, stabilizing p53, and mediating cell cycle arrest and apoptosis. The RPL11-MDM2-p53 pathway, acting as a signaling hub that links nucleolar dysfunction to cell fate determination, plays a pivotal role in maintaining genomic stability and regulating cellular responses to radiation. This review first introduces the basic characteristics of RPL11 and elucidates the molecular basis of radiation-induced ribosomal stress. It then outlines the core regulatory mechanisms of the cell cycle. On this basis, it focuses on the mechanisms by which radiation-induced RPL11 regulates the cell cycle and analyzes the specific effects of RPL11 on cell cycle. Furthermore, it discusses the role of the RPL11-MDM2-p53 pathway in cell cycle regulation. Finally, it explores the role of this pathway in maintaining genomic stability and determining cell fate, and highlights its potential value as a target for radiosensitization, aiming to provide new perspectives for enhancing tumor radiosensitivity and reducing damage to normal tissues.