Traditional Chinese medicine （TCM） has unique advantages in alleviating the symptoms of diabetic kidney disease （DKD） and slowing its progression. However， traditional clinical trials often use the occurrence of end-stage renal disease as the end point， requiring long-term follow-up， which increases trial complexity and costs， thereby limiting the feasibility of TCM clinical studies. This paper suggested that in clinical trials of TCM for DKD， both the estimated glomerular filtration rate （eGFR） change rate （≥30%） and eGFR slope can serve as potential surrogate outcome measures. If the intervention course is short （＜1 year）， the eGFR change rate （≥30%） is recommended as a surrogate outcome measure， whereas in long-term interventional studies （≥1 year）， the eGFR slope may be more appropriate. Furthermore， based on biochemical indicators such as eGFR slope and urinary albumin-to-creatinine ratio （UACR） change rate， integrating TCM symptom evaluation， TCM syndrome evaluation， and quality of life scales can help develop internationally recognized patient-reported outcome measures （PROMs） for TCM clinical trials， which will be a key step in enhancing the evaluation system for the effectiveness of TCM in treating DKD.

BACKGROUND:Flap transplantation technique is a commonly used surgical procedure for the treatment of severe tissue defects,but postoperative flap necrosis is easily triggered by ischemia-reperfusion injury.Therefore,it is still an important research topic to improve the survival rate of transplanted flaps. OBJECTIVE:To review the pathogenesis and latest treatment progress of flap ischemia-reperfusion injury. METHODS:CNKI,WanFang Database and PubMed database were searched for relevant literature published from 2014 to 2024.The search terms used were"flap,ischemia-reperfusion injury,inflammatory response,oxidative stress,Ca2+overload,apoptosis,mesenchymal stem cells,platelet-rich plasma,signaling pathways,shock wave,pretreatment"in Chinese and English.After elimination of irrelevant literature,poor quality and obsolete literature,77 documents were finally included for review. RESULTS AND CONCLUSION:Flap ischemia/reperfusion injury may be related to pathological factors such as inflammatory response,oxidative stress response,Ca2+overload,and apoptosis,which can cause apoptosis of vascular endothelial cells,vascular damage and microcirculation disorders in the flap,and eventually lead to flap necrosis.Studies have found that mesenchymal stem cell transplantation,platelet-rich plasma,signaling pathway modulators,shock waves,and pretreatment can alleviate flap ischemia/reperfusion injuries from different aspects and to varying degrees,and reduce the necrosis rate and necrosis area of the grafted flap.Although there are many therapeutic methods for skin flap ischemia/reperfusion injury,a unified and effective therapeutic method has not yet been developed in the clinic,and the advantages and disadvantages of various therapeutic methods have not yet been compared.Most of the studies remain in the stage of animal experiments,rarely involving clinical observations.Therefore,a lot of research is required in the future to gradually move from animal experiments to the clinic in order to better serve the clinic.

Objective To explore the efficiency improvement in segmenting neural network with the application of Transformer + U-Net artificial intelligence (AI) and modeling with the application of Python scripts in three-dimensional (3D) printing brachytherapy. Methods A Transformer + U-Net AI neural network model was constructed, and Adam optimizer was used to ensure rapid gradient descent. Computed tomography or magnetic resonance imaging data of patients were standardized and processed as self-made data sets. The training set was used to train AI and the optimal result weight parameters were saved. The test set was used to evaluate the AI ability. Python programming language was used to write an automated script to obtain the output segmentation image and convert it to the STL file for import. The source applicator and needle could be automatically modeled. The time of automatic segmentation and modeling and the time of manual segmentation and modeling were entered by two people, and the difference was verified by paired t-test. Results Dice similarity coefficient (DSC), mean intersection over union (MIOU), and Hausdorff distance (HD95) were used for evaluation. DSC was 0.9341, MIOU was 0.8762, and mean HD95 was 2.516. The mean time of manual segmentation was 1187 s, and the mean time of AI segmentation was 145 s (P < 0.01). The mean time of manual modeling was 321 s, and the mean time of modeling with Python automated scripts was 18 s (P < 0.01). Conclusion The application of Transformer + U-Net automatic segmentation and Python automated scripts can effectively improve work efficiency in the 3D printing brachytherapy for cervical cancer.

Perimenopause raises the risk and incidence of depression, whereas the underlying molecular mechanism remains unclear. Disturbed glucose regulation has been widely documented in depressive disorders, which renders the brain susceptible to various stresses such as estrogen depletion. However, whether and how glucose dysfunction regulates depression-like behaviors and neuronal damage in perimenopausal transition remains unexplored. Here, a prominent depressive phenotype was found in perimenopausal mice induced by the ovarian toxin 4-vinylcyclohexene diepoxide (VCD). The VCD depression susceptible group (VCD^SS) and the VCD depression resilient group (VCD^RES) were determined using a ROC-based behavioral screening approach. We found that the hippocampus, a crucial region linked to depression, had hyperglycemia and mitochondrial abnormalities. Interestingly, oral administration of the SGLT2 inhibitor empagliflozin (EMPA) and intrahippocampal glucose infusion suggest a close relationship between hyperglycemia in the hippocampus and the susceptibility to depression. We verified that cytochrome c oxidase 7c (COX7C) downregulation is a potential cause of the high glucose-induced neuronal injury using proteomic screening and biochemical validations. High glucose causes COX7C to be ubiquitinated in a S-phase kinase associated protein 1 (SKP1)-dependent manner. According to these results, SKP1/COX7C represents a unique therapeutic target and a novel molecular route for treating perimenopausal depression.

This paper systematically reviews the application progress of machine learning in perioperative transfusion medicine, focusing on its significant achievements in identifying transfusion risk factors, accurately predicting transfusion requirements, and enabling dynamic monitoring with real-time feedback. It also examines the methodologies, performance metrics, and clinical significance of constructing machine learning models across various surgical specialties, including orthopaedics, cardiac surgery, trauma, and obstetrics. The review further analyzes major challenges currently facing the field, including data bias, model overfitting and interpretability issues, alongside privacy and ethical concerns. Finally, it outlines future directions, highlighting how multimodal data fusion, deep learning applications, multicentre validation, and interdisciplinary collaboration are poised to significant potential for advancing the clinical translation of intelligent transfusion models, achieve personalized precision transfusion management, and enhance patient safety and therapeutic outcomes.

Objective To explore the symptom clusters in renal transplant recipients and con-struct a concurrent symptom network to identify core symptoms.Methods A total of 343 patients with followed up after renal transplantation were selected as the study subjects.A general information questionnaire and the Chinese version of the Modified Transplant Symptom Occurrence and Symptom Distress Scale were employed to analyze the occurrence of symptoms in patients.In this study,only symptoms with an incidence rate greater than 20％and Spearman correlation coefficient greater than 0.40 between symptom severity and total score were retained.Exploratory factor analysis was used to extract symptom clusters with a factor loading of ≥0.45 as the criterion.The R language was utilized to construct symptom network,based on which core symptoms and bridge symptoms were identified.Results A total of 5 symptom clusters were extracted in this study:the neuro-gastrointestinal symp-tom cluster,the mood-related symptom cluster,the hormone-related symptom cluster,the energy de-ficiency symptom cluster and the vision-related symptom cluster.The core symptoms were anxiety(rs=1.75),mood swings(rs=1.50)and muscle weakness(rs=1.27).The top three symptoms in terms of bridge strength were muscle weakness(rb=0.87),lack of vitality(rb=0.66)and fa-tigue(rb=0.65).Conclusion Multiple symptoms are presented in patients after renal transplanta-tion.Based on the results of symptom network analysis,clinicians can strengthen the assessment of core symptoms and bridge symptoms to develop precise intervention strategies and improve the effectiveness of symptom management.

Objective: To investigate the effects of acupotomy on skeletal muscle fibrosis and collagen deposition in a rabbit knee osteoarthritis (KOA) model. Methods: Rabbits (n = 18) were randomly divided into control, KOA, and KOA + acupotomy (Apo) groups (n = 6). The rabbits in the KOA and Apo groups were modeled using the modified Videman's method for 6 weeks. After modeling, the Apo group was subjected to acupotomy once a week for 3 weeks on the vastus medialis, vastus lateralis, rectus femoris, biceps femoris, and anserine bursa tendons around the knee. The behavior of all animals was recorded, rectus femoris tissue was obtained, and histomorphological changes were observed using Masson staining and transmission electron microscopy. The expression of transforming growth factor-β1 (TGF-β1), Smad 3, Smad 7, fibrillar collagen types I (Col-I) and III (Col-III) was detected using Western blot and real-time polymerase chain reaction (RT-PCR). Results: Histological analysis revealed that acupotomy improved the microstructure and reduced the collagen volume fraction of rectus femoris, compared with the KOA group (P = .034). Acupotomy inhibited abnormal collagen deposition by modulating the expression of fibrosis-related proteins and mRNA, thus preventing skeletal muscle fibrosis. Western blot and RT-PCR analysis revealed that in the Apo group, Col-I, and Col-III protein levels were significantly lower than those in the KOA group (both P < .01), same as Col-I and Col-III mRNA levels (P = .0031; P = .0046). Compared with the KOA group, the protein levels of TGF-β1 and Smad 3 were significantly reduced (both P < .01), as were the mRNA levels of TGF-β1 and Smad 3 (P = .0007; P = .0011). Conversely, the levels of protein and mRNA of Smad 7 were significantly higher than that in the KOA group (P < .01; P = .0271). Conclusion Acupotomy could alleviate skeletal muscle fibrosis and delay KOA progress by inhibiting collagen deposition through the TGF-β/Smad pathway in the skeletal muscle of KOA rabbits.

Objective: To investigate the relationship between tropomodulin 3(TMOD3) and the malignant biological characteristics of hepatocellular carcinoma, and the predictive potential of TMOD3 as a biomarker for the recurrence and metastasis of hepatocellular carcinoma. Methods: Firstly, the structure of TMOD3 and its subcellular localization in cells and tissues were analyzed using database of Human Protein Atlas. Then explored the differential expression of TMOD3 in hepatocellular carcinoma tissues and normal liver tissues and its impact on clinical pathological characteristics and prognosis using TCGA and GEO datasets. Subsequently, the STRING database was utilized to explore the interacting proteins of TMOD3, followed by enrichment analysis conducted using the Metascape database. Finally, Logistic regression was used to analyze the independent risk factors for metastasis of hepatocellular carcinoma and evaluated the importance of predictive variables using ROC curves and Wald tests. Survival analysis was conducted using the Kaplan-Meier curve and the Log-rank test. Results: TMOD3 was localized to actin filaments in cells, and compared with normal tissues, the expression level of TMOD3 in liver cancer tissues is higher(P<0.05), and high expression of TMOD3 was closely related to lymph node metastasis and distant metastasis of hepatocellular carcinoma patients(P<0.05). Enrichment analysis results revealed that TMOD3 and its interacting proteins mainly function and signaling pathways related to tumor invasion and migration. Logistic regression found that TMOD3 was an independent risk factor for recurrence and metastasis of hepatocellular carcinoma(OR: 4.359, 95%CI: 1.235-15.384,P=0.022). Survival analysis revealed that high expression of TMOD3 was associated with poor OS, DFS, and RFS of hepatocellular carcinoma patients(P<0.05). Both ROC analysis and Wald test indicated that TMOD3 has good predictive characteristics for recurrence and metastasis of hepatocellular carcinoma. Conclusion TMOD3 is closely associated with the invasion and metastasis of hepatocellular carcinoma and is an independent risk factor for the recurrence and metastasis of liver cancer. TMOD3 performs well in predicting the recurrence and metastasis of hepatocellular carcinoma and has the potential to become a biomarker for predicting the recurrence and metastasis of hepatocellular carcinoma.

In fixed prosthodontics, clear exposure of the preparation margin is the prerequisite for obtaining accurate digital impressions and improving the marginal fit of restorations. To resolve the issues associated with the cord retraction technique, such as pain, acute injury, and prolonged procedural time, this study proposes a new technology for intraoral digital impression taking with pneumatic gingival retraction. The new scanning head blows a high-speed airflow that instantaneously separates the free gingiva, locally exposing the subgingival preparation margin. Combined with the farthest point preservation stitching algorithm based on the distance from the normal vector and high-speed laser scanning photography, it achieves global preparation edge data and gingival reconstruction, realizing painless, non-invasive, and efficient precise acquisition of the preparation margin. Using this new technique, a patient with a full porcelain crown restoration on a posterior tooth was treated. The digital impression revealed a clear margin of the preparation, and the crown made from this data has a good marginal fit.

BACKGROUND:Epidemiologic studies have shown a correlation between type 2 diabetes mellitus and bone mineral density,but the causal association between the two and whether it is age-related remains unknown. OBJECTIVE:To study the correlation between type 2 diabetes mellitus and whole body bone mineral density at unspecified age and at all ages based on the Mendelian randomization technique. METHODS:The genome-wide association study(GWAS)data of type 2 diabetes mellitus and bone mineral density at all ages were selected from the IEU GWAS database of the University of Bristol.The exposure data were single nucleotide polymorphisms with significant correlation with type 2 diabetes mellitus as instrumental variables,and bone mineral density at all ages was selected as the outcome variable.Two-sample Mendelian randomization analysis of type 2 diabetes mellitus and bone mineral density was performed using inverse variance weighted method,weighted median estimator,and MR-Egger regression.The βvalue was used to evaluate the causal relationship between type 2 diabetes mellitus and bone mineral density at all ages. RESULTS AND CONCLUSION:A total of 118 single nucleotide polymorphisms were extracted from the GWAS summary data as instrumental variables.The MR-Egger regression results showed that there was no horizontal pleiotropy,but there was heterogeneity.Therefore,this study was based on the inverse variance weighted results.Inverse variance weighted results showed that type 2 diabetes mellitus may be a potential protective factor for bone mineral density and is associated with age:age-unspecified bone mineral density[β=0.038,95%confidence interval(CI):1.01-1.07,P=0.002],bone mineral density over 60 years old(β=0.052,95%CI:1.01-1.09,P=0.027),bone mineral density between 45-60 years old(β=0.049,95%CI:1.01-1.09,P=0.009),bone mineral density between 30-45 years old(β=0.033,95%CI:0.99-1.07,P=0.127).bone mineral density of 15-30 years old(β=0.025,95%CI:0.95-1.10,P=0.506),bone mineral density of 0-15 years old(β=0.006,95%CI:0.96-1.04,P=0.716).Similar results were obtained from the MR-Egger regression and weighted median estimator analyses.These findings indicate that type 2 diabetes mellitus may be one of the protective factors of bone mineral density,and there is a correlation with age.