Objective To explore a method for rapid and differential diagnosis of rejection after kidney transplantation through urine hyperspectral imaging technology. Methods Hyperspectral data information from urine samples of 118 recipients after kidney transplantation was collected, and a deep learning model was constructed to diagnose and classify the types of rejection. Results A deep learning diagnostic model based on the 34-layer residual network (ResNet-34) was constructed, and 118 patients were included and divided into the training set and the test set. Based on the pathological results of the transplanted kidney puncture, the urine samples of the patients were classified into five groups: the non-rejection group, the T-cell-mediated rejection group, the antibody-mediated rejection group, the mixed rejection group and the nephropathy recurrence group. The results showed that the diagnostic sensitivities of the model for the above five groups were 0.960, 0.980, 0.930, 0.940 and 0.943 respectively, and the diagnostic specificities were 0.983, 0.993, 0.997, 0.989 and 0.989 respectively. The overall diagnostic accuracy rate reached 95.7%. Conclusions The study provides a non-invasive, rapid and accurate auxiliary diagnostic method for the differential diagnosis of rejection after kidney transplantation.

BACKGROUND: Valvular heart disease (VHD) has become increasingly common with the aging in China. This study aimed to evaluate regional differences in the clinical features, management strategies, and outcomes of patients with VHD across different regions in China. METHODS: Data were collected from the China-VHD Study. From April 2018 to June 2018, 12,347 patients who presented with moderate or severe native VHD with a median of 2 years of follow-up from 46 centers at certified tertiary hospitals across 31 provinces, autonomous regions, and municipalities in Chinese mainland were included in this study. According to the locations of the research centers, patients were divided into five regional groups: eastern, southern, western, northern, and central China. The clinical features of VHD patients were compared among the five geographical regions. The primary outcome was all-cause mortality or rehospitalization for heart failure. Kaplan-Meier survival analysis was used to compare the cumulative incidence rate. RESULTS: Among the enrolled patients (mean age, 61.96 years; 6877 [55.70%] male), multiple VHD was the most frequent type (4042, 32.74%), which was mainly found in eastern China, followed by isolated mitral regurgitation (3044, 24.65%), which was mainly found in northern China. The etiology of VHD varied significantly across different regions of China. The overall rate of valve interventions was 32.67% (4008/12,268), with the highest rate in southern China at 48.46% (205/423). In terms of procedure, the proportion of transcatheter valve intervention was relatively low compared to that of surgical treatment. Patients with VHD in western China had the highest incidence of all-cause mortality or rehospitalization for heart failure. Valve intervention significantly improved the outcome of patients with VHD in all five regions (all P  <0.05). CONCLUSIONS: This study revealed that patients with VHD in China are characterized by significant geographic disparities in clinical features, treatment, and clinical outcomes. Targeted efforts are needed to improve the management and prognosis of patients with VHD in China according to differences in geographical characteristics. REGISTRATION ClinicalTrials.gov , NCT03484806.
Aged ; Female ; Humans ; Male ; Middle Aged ; China/epidemiology* ; Heart Valve Diseases/therapy* ; Kaplan-Meier Estimate ; Tertiary Care Centers ; Treatment Outcome

This study explored the drying kinetics of Salviae Miltiorrhizae Radix et Rhizoma(SM), established the suitable models simulating the drying kinetics, and then analyzed the dynamic changes of active components during the drying processes with different methods, aiming to provide a basis for the establishment of suitable drying methods and the quality control of SM. The drying kinetics were studied based on the drying curve, drying rate, moisture effective diffusion coefficient, and drying activation energy, and the appropriate drying kinetics model of SM was established. The drying performance of different methods, such as hot air drying, infrared drying, and microwave drying of SM was evaluated, and the changes in the content of 10 salvianolic acids and 6 tanshinones during drying were analyzed by UPLC-TQ-MS. The Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS) was employed to evaluate the quality of SM dried with different methods. The results showed that the drying rate and moisture effective diffusion coefficient of SM increased with the rise in drying temperature, and the maximum drying rates of different methods were in the order of microwave drying > infrared drying > hot air drying, slice > whole root. The drying rate decreased with the rise in temperature and the extension of drying time. The activation energy of hot air drying was higher than that of infrared drying in SM. The most suitable model for simulating the drying process of SM was the Page model. The TOPSIS results suggested infrared drying at 50 ℃ was the optimal drying method for SM. During the drying process, the content of salvianolic acids increased in different degrees with the loss of moisture, among which salvianolic acid B showed the largest increase of 44 times compared with that in the fresh medicinal material. Tanshinones also existed in the fresh herb of SM, and the content of tanshinone Ⅱ_A increased by 3 times after drying. The results provided a basis for the establishment of suitable drying methods and the quality control of SM.
Salvia miltiorrhiza/chemistry* ; Desiccation/methods* ; Drugs, Chinese Herbal/chemistry* ; Rhizome/chemistry* ; Kinetics ; Quality Control ; Abietanes

BACKGROUND: Based on the China-VHD database, this study sought to develop and validate a Valvular Heart Disease- specific Age-adjusted Comorbidity Index (VHD-ACI) for predicting mortality risk in patients with VHD. METHODS & RESULTS: The China-VHD study was a nationwide, multi-centre multi-centre cohort study enrolling 13,917 patients with moderate or severe VHD across 46 medical centres in China between April-June 2018. After excluding cases with missing key variables, 11,459 patients were retained for final analysis. The primary endpoint was 2-year all-cause mortality, with 941 deaths (10.0%) observed during follow-up. The VHD-ACI was derived after identifying 13 independent mortality predictors: cardiomyopathy, myocardial infarction, chronic obstructive pulmonary disease, pulmonary artery hypertension, low body weight, anaemia, hypoalbuminaemia, renal insufficiency, moderate/severe hepatic dysfunction, heart failure, cancer, NYHA functional class and age. The index exhibited good discrimination (AUC, 0.79) and calibration (Brier score, 0.062) in the total cohort, outperforming both EuroSCORE II and ACCI (P < 0.001 for comparison). Internal validation through 100 bootstrap iterations yielded a C statistic of 0.694 (95% CI: 0.665-0.723) for 2-year mortality prediction. VHD-ACI scores, as a continuous variable (VHD-ACI score: adjusted HR (95% CI): 1.263 (1.245-1.282), P < 0.001) or categorized using thresholds determined by the Yoden index (VHD-ACI ≥ 9 vs. < 9, adjusted HR (95% CI): 6.216 (5.378-7.184), P < 0.001), were independently associated with mortality. The prognostic performance remained consistent across all VHD subtypes (aortic stenosis, aortic regurgitation, mitral stenosis, mitral regurgitation, tricuspid valve disease, mixed aortic/mitral valve disease and multiple VHD), and clinical subgroups stratified by therapeutic strategy, LVEF status (preserved vs. reduced), disease severity and etiology. CONCLUSION The VHD-ACI is a simple 13-comorbidity algorithm for the prediction of mortality in VHD patients and providing a simple and rapid tool for risk stratification.

Radiomics provides quantitative support for the diagnosis,treatment strategy and prognosis evaluation of brain arteriovenous malformation(bAVM)through high-throughput analysis of imaging data,and it also shows significant advantages in clinical symptom prediction,personalized treatment and clinical outcome prediction.Emerging imaging techniques,such as blood oxygen level-dependent cerebrovascular reactivity imaging and ultrasound technology,provide a new perspective for evaluating the hemodynamic changes and epilepsy susceptibility associated with bAVM.In addition,advances in deep learning algorithms in automatic segmentation of bAVM lesions have greatly improved the accuracy and efficiency of segmentation.With the continuous progress of imaging technology,data analysis algorithms and software,radiomics is expected to play a greater role in precision medicine and individualized treatment,bringing better diagnosis and treatment services and better treatment effects for bAVM patients.

Single-incision laparoscopic cholecystectomy (SILC) represents a significant evolution in minimally invasive surgery, designed to accomplish cholecystectomy via a single umbilical incision. This approach seeks to reduce abdominal wall trauma while optimizing cosmetic outcomes. SILC is a safe and feasible minimally invasive technique for cholecystectomy under defined conditions; however, its broader adoption will require further evidence-based research and the establishment of standardized protocols to support its widespread implementation. When performed by skilled surgeons in carefully selected patients, SILC demonstrates clinical outcomes comparable to those of conventional multiport laparoscopic cholecystectomy, with notable improvements in incision aesthetics. Nonetheless, the technique is limited by a constrained operative field and a protracted learning curve. In response, continuous advancements in instrumentation and procedural modifications have propelled the further development and clinical integration of SILC. Drawing on current literature and clinical experience, this review delineates the technical characteristics, current clinical applications, primary benefits, and prevailing challenges associated with SILC.

Objective To investigate the impact of myeloid cell-specific knockout of the granulocyte colony-stimulating factor receptor(G-CSFR)on the progression of acute radiation pneumonitis.Methods Myeloid cell-specific G-CSFR knockout(G-CSFR-/-,Lyz2-cre)mice were constructed.G-CSFR-/-,Lyz2-cre and C57BL/6N mice underwent a single whole-body irradiation with 6.5 Gy of 60Co γ-rays to establish a model of radiation injury.The lung function of mice was assessed using a mouse lung function test system at 3,7 and 14-days post γ-ray irradiation.Pathological changes in the lung tissue were analyzed via hematoxylin and eosin(HE)staining of paraffin sections.Tumor necrosis factor-α(TNF-α)and interleukin-10(IL-10)levels were measured via radioimmunoassay.IL-8 and its receptor CXCR2 were quantified using enzyme-linked immunosorbent assay(ELISA).The infiltration of neutrophils in lung tissue was evaluated by immunohistochemical detection of myeloperoxidase.Results At 3-,7-and 14-days post-irradiation with 6.5 Gy of 60Co γ-rays,there were no significant differences observed in lung function or interstitial inflammatory lesions between G-CSFR-/-,Lyz2-cre mice and C57BL/6N mice.However,the infiltration of neutrophils in lung tissue of G-CSFR-/-,Lyz2-cre mice was significantly reduced(P＜0.01),and the levels of IL-8,CXCR2 and TNF-α in lung tissues were markedly lower than in C57BL/6N mice(P＜0.05).Conclusion The myeloid cell-specific knockout of G-CSFR can effectively diminish neutrophil infiltration as well as inflammatory cytokine levels in lung tissues following radiation exposure.

BACKGROUND:Increasing evidence suggests that N6-methyladenosine(m6A)regulators are closely associated with osteoarthritis and are considered to be a new direction in the prevention and treatment of osteoarthritis,but their specific mechanism of action is unknown. OBJECTIVE:To conduct a bioinformatics analysis of the osteoarthritis gene microarray dataset in order to explore the role of m6A in osteoarthritis and analyze the pathogenesis of osteoarthritis. METHODS:The m6A regulators associated with osteoarthritis and their expression were first extracted from the GSE1919 dataset in the GEO database using R software,and then the results were analyzed by gene difference analysis and GO and KEGG enrichment analyses.Subsequently,the results of protein-protein interaction network topology analysis and machine learning results were intersected to obtain the m6A Hub regulators,which were validated by in vitro cellular experiments. RESULTS AND CONCLUSION:A total of 16 osteoarthritis-related m6A regulators were extracted and 11 m6A differential regulators,including ZC3H13,YTHDC1,YTHDF3 and HNRNPC,were obtained by differential analysis.GO enrichment analysis showed that osteoarthritis-related m6A differential regulators played a role in the biological processes such as mRNA transport,RNA catabolism,and regulation of insulin-like growth factor receptor signaling pathway.(3)KEGG enrichment analysis showed that the differential regulators were mainly involved in the p53,interleukin-17 and AMPK signaling pathways.The combined protein-protein interaction network topology analysis and machine learning results obtained the m6A Hub regulator-YTHDC1.(5)The results of in vitro cellular experiments showed that there was a significant difference in the expression of m6A key regulator between the control and experimental groups(P<0.05).To conclude,YTHDC1 is closely related to the development of osteoarthritis,which is expected to be a molecular target of m6A for the treatment of osteoarthritis.

BACKGROUND:Cellular senescence is closely related to the development and progression of osteoarthritis,but the specific targets and regulatory mechanisms are not yet clear. OBJECTIVE:To mine key genes in cellular senescence-mediated osteoarthritis by integrating bioinformatics and machine learning approaches and validate them via experiments to explore the role of cellular senescence in osteoarthritis. METHODS:The osteoarthritis gene expression profiles obtained from the GEO database were intersected with cellular senescence-related genes obtained from the CellAge database and the expression of the intersected genes was extracted for differential analysis,followed by GO and KEGG analysis of the differential genes.The key osteoarthritis cellular senescence genes were then screened by protein-protein interaction network analysis and machine learning,and in vitro cellular experiments were performed.Finally,the expression of the key genes was detected by qPCR. RESULTS AND CONCLUSION:A total of 31 osteoarthritis cell senescence differential genes were identified.GO analysis showed that these genes were mainly involved in the biological processes,such as regulation of leukocyte differentiation,monocyte differentiation,regulation of T cell differentiation and exerted roles in DNA transcription factor binding,histone deacetylase binding,chromatin DNA binding,and chemokine binding.KEGG analysis showed that osteoarthritis cell senescence differential genes were mainly activated in the JAK/STAT signaling pathway,PI3K/Akt signaling pathway and FoxO signaling pathway.MYC,a key gene for osteoarthritis cellular senescence,was identified by protein-protein interaction network topology analysis and machine learning methods.The results of the in vitro cellular assay showed that the mRNA expression of MYC was significantly lower in the experimental group(osteoarthritis group)than the control group(normal group)(P＜0.05).To conclude,MYC can be a key gene in the senescence of osteoarthritic cells and may be a new target in the prevention and treatment of osteoarthritis by mediating immune response,inflammatory response and transcriptional regulation.