Background: Chinese herbal pieces are an essential component of traditional Chinese medicine. Accurate identification and classification of these materials are crucial in clinical practice. Objective: This study aims to enhance the recognition efficiency of Chinese herbal pieces using deep learning technology, while addressing the limitations of traditional manual classification methods in terms of both quality and efficiency. Methods: A comprehensive dataset containing 201 types of Chinese herbal pieces was established. Based on Real-time Detection Transformer (RT-DETR), we designed and integrated a Feature-focused Diffusion Network (FDN), resulting in an improved model termed RT-DETR-FDN. The proposed FDN includes a Feature-focus Module and a feature diffusion mechanism, enabling the model to capture more extensive feature information from Chinese herbal pieces and diffuse it across multiple detection scales. Results: Experimental results show that RT-DETR-FDN achieved a precision of 0.925, a recall of 0.943, and an mAP50-95 of 0.851. In addition, the model was compared with representative You Only Look Once series models commonly used in object detection. Compared with these models, RT-DETR-FDN achieved higher recognition accuracy while maintaining a lightweight architecture. Conclusion: This study integrates deep learning with traditional Chinese medicine, providing a more effective solution for the recognition of Chinese herbal pieces.

Primary biliary cholangitis （PBC） is a cholestatic autoimmune liver disease characterized by the injury of small intrahepatic bile ducts， and at present， the pathogenesis of PBC remains unclear. Recent studies have shown that bile acid metabolism disorder and gut microbiota imbalance play a key role in the development and progression of PBC， and they form a complex and dynamic interaction network via the “gut-liver axis” and regulate core physiopathological processes such as immune response， metabolic homeostasis， and inflammatory response in a synergistic manner. This article systematically elaborates on the abnormal features of bile acid metabolism and gut microbiota in PBC， discusses their synergistic mechanisms in PBC， and then proposes a combined strategy of targeting bile acid receptors and modulating gut microbiota， in order to overcome the limitations of current treatment modalities and provide new insights and directions for the clinical management of PBC.

The development of diabetic bladder dysfunction(DBD)is closely related to the dysregulation of the microbiotagut-bladder axis. Diabetic conditions significantly alter gut microbiota composition, causing intestinal barrier disruption, endotoxemia, and systemic inflammation. These alterations impair bladder function by activating bladder NOD-like receptor thermal protein domain associated protein 3(NLRP3)inflammasome, inducing oxidative stress, and nerve remodeling. Emerging technologies for microbiota analysis, Mendelian randomization, and targeted marker development have revealed the causal links between specific gut microbiota and DBD. Modulating gut microbiota increases short-chain fatty acids(SCFAs)production and suppresses inflammation; NLRP3 pathway targeting or direct SCFAs delivery are under clinical evaluation. The therapeutic directions for DBD include microbiota regulation therapy, application of probiotics and dietary fiber, as well as clinical translational research on the microbiota-gut-bladder axis. Engineered live bacterial therapies and nanocarrier technologies are emerging research directions. Current controversies focus on the causal role of dysbiosis, safety and long-term efficacy of individualized therapies, and technical bottlenecks. Future efforts require integrating multi-omics, organoid models, and cross-disciplinary collaboration to advance precision diagnostics and therapeutics targeting the microbiota-gut-bladder axis in DBD.

Immunoglobulin A (IgA) nephropathy is a globally prevalent type of primary glomerulonephritis, characterized by complex symptoms and diverse clinical manifestations. The internationally recognized " multiple hit hypothesis" explains the systemic immune disease features of IgA nephropathy. However, current treatment strategies primarily focus on local pathological changes, inadequately addressing its complex systemic mechanisms. The " qi cycle in round" theory, an integral concept of the academic thought of HUANG Yuanyu, a prominent medical expert from the Qing Dynasty, offers a concise and insightful framework for understanding complex pathologies. For example, this theory provides valuable insights for elucidating the pathogenesis of IgA nephropathy and guiding its clinical management by simplifying intricate systemic processes. This study applies the " qi cycle in round" theory to postulate that patients with IgA nephropathy experience disrupted qi flow owing to spleen-stomach qi deficiency and dampness-heat accumulation. These imbalances manifest as internal symptoms, such as diarrhea; external vulnerability to illness; upper body symptoms, like sore throat; and lower body symptoms, such as hematuria and proteinuria. Pathologically, the condition is characterized by immune complex deposition. This article also emphasizes strategies that prioritize tonifying spleen-stomach qi to enhance the pivotal functions of transportation and transformation. Regulating qi and relieving stagnation are emphasized to harmonize ascending and descending dynamics. Additionally, eliminating turbidity and unblocking collaterals are highlighted to promote qi transformation. These approaches aim to restore the harmonious operation of organ qi dynamics and harmonious qi transformation functions. This study aims to provide a reference for syndrome differentiation and IgA nephropathy treatment using traditional Chinese medicine based on the " qi cycle in round" theory.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Objective·To analyze the clinicopathologic characteristics,gene mutation profile,and prognostic factors of patients with adrenal diffuse large B-cell lymphoma(DLBCL).Methods·From March 2002 to December 2022,a total of 105 patients with adrenal DLBCL admitted to Ruijin Hospital,Shanghai Jiao Tong University School of Medicine were retrospectively analyzed for their clinicopathological data,survival outcomes,and prognostic factors.Patients'gene mutation profiles were evaluated by targeted sequencing of 152 lymphoma-related genes.Results·The median age of the patients was 62(15?82)years and the male-to-female ratio was 2.3∶1.Among them,63 patients(60.0%)were over 60 years old,22 patients(21.0%)had an Eastern Cooperative Oncology Group(ECOG)performance status of two or higher,87 patients(82.9%)were staged Ann Arbor Ⅲ?Ⅳ,92 patients(87.6%)had elevated serum lactate dehydrogenase(LDH)levels(above the upper limit of reference),84 patients(80.0%)had extranodal invasion in at least two organs,67 patients(63.8%)were of non-germinal center B-cell(non-GCB)origin,and 95 patients(90.5%)had an international prognosis index(IPI)scored over 2.With a median follow-up of 28.3(0.7?191.9)months,the estimated 2-year overall survival(OS)rate and progression-free survival(PFS)rate were 68.3%and 53.1%,respectively.The estimated 5-year OS rate and PFS rate were 52.6%and 44.0%,respectively.Among 93 patients who could be evaluated for clinical outcomes,62(66.7%)got a complete response(CR).Univariate analysis and multivariate Cox analysis revealed that age over 60 years was an adverse prognostic factor for PFS,and ECOG performance status of two or higher was an adverse prognostic factor for both OS and PFS.Targeted gene sequencing in 46 adrenal diffuse DLBCL patients showed high mutation frequencies in lysine methyltransferase 2D(KMT2D;n=17,37%),Pim-1 proto-oncogene,serine/threonine kinase(PIM1;n=17,37%),MYD88 innate immune signal transduction adaptor(MYD88;n=15,33%),CD79b molecule(CD79B;n=13,28%),and BTG anti-proliferation factor 2(BTG2;n=10,22%).Conclusion·Age over 60 years is an adverse prognostic factor for PFS,and ECOG performance status of two or higher is an adverse prognostic factor for both OS and PFS in patients with adrenal DLBCL.Patients exhibited high frequencies of KMT2D,PIM1,MYD88,CD79B,and BTG2 mutations,as well as an increased proportion of the MCD-like subtype.

Objective:To investigate the inhibitory effect and mechanism of puerarin(Pue)on hydrogen peroxide(H2 O2)induced ferroptosis in the rat Schwann cell derived cell line RSC96.Methods:The RSC96 cells were incuba-ted with H2 O2 to establish a cellular injury model,while a subset of cells were co-incubated with H2 O2 and Pue.The vi-ability of cells was examined using the CCK-8 assay.The intracellular levels of glutathione(GSH),total superoxide dismutase(T-SOD),reactive oxygen species(ROS),malondialdehyde(MDA),and ferrous ions(Fe2+)levels were quantified using commercial kits.Western blot was employed to detect the protein expression level of glutathione peroxi-dase 4(GPX4),cyclooxygenase-2(COX-2),solute carrier family 7 member 11(SLC7A11),nuclear factor erythroid 2-related factor 2(Nrf2),and heme oxygenase-1(HO-1).Immunofluorescence assay was performed to examine the expression and nuclear distribution of Nrf2.Results:Pue pretreatment significantly increased the survival rate of H2 O2-treated RSC96 cells,increased the intracellular content of GSH and T-SOD,and inhibited the concentration of ROS and MDA.It also activated the nuclear translocation of Nrf2 and upregulated GPX4,SLC7A11,HO-1,and Nrf2 proteins in RSC96 cells;This effect was abolished by the Nrf2 inhibitor ML385.Conclusion:Pue treatment alleviated the H2 O2-induced ferroptosis of RSC96 cells via the Nrf2/SLC7A11/GPX4 signaling pathway.

Pigeon circovirus(PiCV)is globally widespread and is considered a potential cause of young pigeon sickness syndrome(YPDS),which leads to severe immunosuppression and high mortality.Due to the inability of PiCV to be cultured in cells,the development of traditional vac-cines is severely limited,and no effective vaccines is currently available.To develop a novel PiCV DNA candidate vaccine,we cloned the △Cap gene lacking a nuclear localization signal(NLS),and fused it at its C-terminus with the transmembrane and cytoplasmic regions of the Newcastle dis-ease virus(NDV)F protein(△Cap-TMCT).Two DNA vaccine candidates were constructed:pCAGG-△Cap,targeting intracellular expression,and pCAGG-△Capt,for cell surface expression,respectively.The results of indirect immunofluorescence and Western blot analyses confirmed suc-cessful expression of both recombinant plasmids in DF1 cells.Immunization studies in mice re-vealed that pCAGG-△Capt induced significantly higher levels of specific IgG antibodies,T-cell re-sponses,and cytokine secretion compared to pCAGG-△Cap,as assessed by ELISA,flow cytome-try,and ELISpot assays.These findings suggest that targeting △Cap-TMCT fusion protein to the cell surface can effectively enhance its immunogenicity,highlighting its potential as a PiCV DNA vaccine candidate.This study provides new strategies and theoretical foundations for the design and development of PiCV DNA vaccines.

Objective:To investigate the clinicopathological features, immunophenotype, and gene rearrangement status of hyalinizing clear cell carcinoma (HCCC) of the salivary glands, in order to better understand this rare tumor and improve its precision diagnosis and treatment.Methods:A total of 65 cases of salivary gland HCCC diagnosed at the Peking University School and Hospital of Stomatology, Beijing, China between January 2001 and May 2024 were collected. Clinical features, pathological characteristics, EWSR1 gene rearrangement, and follow-up data were analyzed.Results:There were 33 males and 32 females. The age of the patients ranged from 22 to 85 years, with a median age of 54.0 (40.0, 63.5) years. 93.8% (61/65) of the tumors occurred in the minor salivary glands, most commonly in the palate. Microscopically, the tumors were mainly composed of clear tumor cells. Squamous differentiation was observed in 66.2% (43/65) of the tumors, and mucinous cells in 36.9% (24/65). The tumor stroma showed delicate fibrous septa or hyalinized sheets between tumor nests. Lymph node metastasis occurred in 12.3% (8/65) of the cases, and high-grade transformation was observed in 6.2% (4/65). Tumor cells were all positive for p40 and p63, while SOX10 was positive in 12.3% (8/65) of them. Myoepithelial markers were negative. EWSR1 gene rearrangement was detected in 96.6% of the tumors. Follow-up data showed a 5-year overall survival rate of 93.8%, a local recurrence rate of 9.2%, and a distant metastasis rate of 4.6%.Conclusions:HCCC predominantly arises in minor salivary glands and generally has a favorable prognosis. However, a small proportion of the cases may show high-grade transformation, lymph node metastasis, local recurrence, or distant metastasis. It thus requires long-term and regular follow-up. Accurate diagnosis should be based on a comprehensive assessment of histopathological features, immunophenotype, and gene fusion status.

Objective To investigate the protective effect and mechanism of the antioxidant Lutein in a mouse model of dry age-related macular degeneration(AMD)induced by hydroquinone.Methods Twenty-four specific pathogen-free(SPF)grade male C57BL/6 mice,aged 6-8 weeks,were randomly divided into 3 groups(n=8 per group).The control group was fed a standard diet and water for 3 months.The model group and the drug intervention group received 8 g·L-1 hydroquinone in drinking water combined with a high-fat diet for 2 months to establish the dry AMD mouse model.After modeling,the model group resumed a standard diet and water for 1 month.The drug group received oral gavage daily at 8:00 AM,administered Lutein at 0.1 g per kg body weight(dissolved in distilled water),for 1 month.At the experimental endpoint,all mice were euthanized,and samples were collected for analysis.The ultrastructure of retinal pigment epithelial(RPE)cells was observed by electron microscopy.Serum activities of superoxide dismutase(SOD),catalase(CAT),and glutathione peroxidase(GSH-Px)were measured using a microplate reader.mRNA expression levels of nuclear factor ery-throid 2-related factor 2(Nrf2),heme oxygenase-1(HO-1),and glutamate-cysteine ligase(GCL)in retinal tissue were de-tected by real-time quantitative PCR.Protein expression levels of Nrf2,HO-1,and GCL in retinal tissue were determined by Western blot.Results In the model group,mitochondria and intracellular organelles in RPE cells exhibited vacuolar de-generation,with compromised structural integrity;simultaneously,microvilli were disorganized and significantly reduced in number.In the drug group,the mitochondrial structure of RPE cells was relatively well-preserved,with morphology largely normal;microvilli structure was clear,and density showed some recovery compared to the model group.Compared to the control group,serum activities of SOD,CAT,and GSH-Px were significantly decreased in the model group(all P＜0.01).Compared to the model group,serum activities of SOD,CAT,and GSH-Px were significantly increased in the drug group(all P＜0.05).Compared to the control group,retinal mRNA expression levels of Nrf2,HO-1,and GCL were significantly decreased in the model group(all P＜0.01).Compared to the model group,retinal mRNA expression levels of Nrf2,HO-1,and GCL were significantly increased in the drug group(all P＜0.01).Compared to the control group,retinal protein ex-pression levels of Nrf2,HO-1,and GCL were decreased in the model group(all P＜0.05).Compared to the model group,retinal protein expression levels of Nrf2,HO-1,and GCL were increased in the drug group(all P＜0.05).Conclusion The antioxidant Lutein promotes the expression of downstream target genes HO-1 and GCL by activating the Nrf2 signaling pathway,which not only enhances the biosynthesis levels of antioxidant enzymes(SOD,CAT,GSH Px),but also effec-tively inhibits oxidative stress response by enhancing autophagic activity.