Objective: To define the mechanism by which swine TRIM25 restricts vesicular stomatitis virus replication. Methods: Porcine 3D4/21 cells with TRIM25 overexpression or knockdown were infected with vesicular stomatitis virus. Viral replication was quantified by immunoblotting, quantitative reverse transcription polymerase chain reaction, and 50% tissue culture infectious dose assays. Type I interferon signaling was assessed by transcript quantification, interferon-beta and interferon-stimulated response element reporter assays, and co-immunoprecipitation.Viral RNA binding was tested by RNA immunoprecipitation. Results: TRIM25 overexpression reduced viral RNA and infectious titers, whereas TRIM25 knockdown increased replication (p < 0.01). TRIM25 increased interferon-beta and interferon-stimulated gene expression and enhanced interferon-beta and interferonstimulated response element promoter activity (p < 0.01). Mechanistically, TRIM25 promoted Lys63-linked ubiquitination of RIG-I and increased phosphorylation of TANK-binding kinase 1 and interferon regulatory factor 3. TRIM25 also bound vesicular stomatitis virus genomic RNA, and binding required the C-terminal region. Conclusions and Relevance: Porcine TRIM25 restricts vesicular stomatitis virus replication by amplifying type I interferon signaling and directly binding viral RNA.

ObjectiveTo observe the effect of functional pelvic floor muscle training on pelvic floor muscle function, incontinence symptoms and quality of life in female patients with stress urinary incontinence (SUI). MethodsFrom February, 2024 to July, 2025, 40 female patients with SUI were recruited from Beijing Bo'ai Hospital and advertisements. They were randomly divided into control group (n = 20) and experimental group (n = 20). The control group received conventional pelvic floor muscle contraction and relaxation training, and the experimental group received functional pelvic floor muscle contraction and relaxation training, for eight weeks. The data of pelvic floor muscle strength, pelvic floor muscle surface electromyography (sEMG), 1-hour urine pad test and Incontinence Quality of Life questionnaire (I-QOL) were collected before and after training. The compliance rate was counted after training. ResultsOne case dropped down in the control group and three cases dropped down in the experimental group. After treatment, the pelvic floor muscle strength increased in both groups (|Z| > 3.317, P < 0.01), the pre-resting average value, sustained contraction average value and durable contraction average value of sEMG improved in the control group (|t| > 2.731, P < 0.05), the mass of 1-hour urine pad significantly reduced (t > 9.215, P < 0.001) and the I-QOL score significantly increased (|t| > 13.229, P < 0.001) in both groups; compared with the control group, the pelvic floor muscle strength significantly increased (Z = -2.281, P = 0.023), the mass of 1-hour urine pad significantly reduced (t = 4.215, P < 0.001), the I-QOL score significantly increased (t = -4.501, P < 0.001), and the compliance rate significantly increased (Z = -2.798, P < 0.01) in the experimental group . ConclusionFunctional pelvic floor muscle training can significantly improve pelvic floor muscle strength, incontinence symptoms and quality of life in female patients with SUI.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective To compare the effects of main artery clamping(MAC)and selective artery clamping(SAC)strategies on postoperative renal function in patients with chronic renal insufficiency undergoing robot-assisted partial nephrectomy.Methods A retrospective cohort study was conducted on 231 patients with preoperative chronic renal insufficiency[eGFR<90 mL/(min·1.73 m2)with renal injury markers or eGFR<60 mL/(min·1.73 m2)]who underwent robot-assisted partial nephrectomy in the Department of Urology of the First Affiliated Hospital of Army Medical University from February 2018 to February 2024.According to intraoperative renal artery clamping strategy,they were divided into a MAC group(n=129)and a SAC group(n=102).Preoperatively,individualized renal artery clamping strategies were developed using a machine learning-based multimodal holographic 3-D reconstruction technique.Serum creatinine(Scr)level was measured at 3 d and 3 months after surgery,and estimated glomerular filtration rate(eGFR)was calculated using the chronic kidney disease epidemiology collaboration equation(CKD-EPI)formula.Renal dynamic imaging with 99mTc-DTPA or 99mTc-MAG3 was used to assess the GFR of the affected kidney.Results At 3 d after surgery,the decrease in GFR of the affected kidney was significantly lower[(8.3±7.7)vs(16.0±10.2)mL/(min·1.73 m2),95%CI:-10.2～-5.2,P<0.001]in the SAC group than the MAC group.Scr increment analysis showed that the SAC group exhibited notably lower Scr increase[8.2(2.5,18.7)vs 15.5(5.8,28.3)μmol/L,95%CI:-12.3～-1.8,P=0.027],and milder eGFR decline[3.0(0.5,7.8)vs 7.5(2.0,14.3)mL/(min·1.73 m2),95%CI:-6.2～-0.8,P=0.015].And,in 3 months after surgery,the SAC group had lower Scr level[(89.2±23.1)vs(95.3±22.1)μmol/L,95%CI:-11.9～-0.3,P=0.042],and higher GFR of the affected kidney[(33.5±10.5)vs(26.1±10.9)mL/(min·1.73 m2),95%CI:4.6～10.2,P<0.001].Conclusion For patients with chronic renal insufficiency undergoing robot-assisted partial nephrectomy,SAC strategy is superior to MAC strategy in protecting postoperative renal function without increasing surgical risk.

BACKGROUND: Biomarkers-based prediction of long-term risk of acute coronary syndrome (ACS) is scarce. We aim to develop a risk score integrating clinical routine information (C) and plasma biomarkers (B) for predicting long-term risk of ACS patients. METHODS: We included 2729 ACS patients from the OCEA (Observation of cardiovascular events in ACS patients). The earlier admitted 1910 patients were enrolled as development cohort; and the subsequently admitted 819 subjects were treated as validation cohort. We investigated 10-year risk of cardiovascular (CV) death, myocardial infarction (MI) and all cause death in these patients. Potential variables contributing to risk of clinical events were assessed using Cox regression models and a score was derived using main part of these variables. RESULTS: During 16,110 person-years of follow-up, there were 238 CV death/MI in the development cohort. The 7 most important predictors including in the final model were NT-proBNP, D-dimer, GDF-15, peripheral artery disease (PAD), Fibrinogen, ST-segment elevated MI (STEMI), left ventricular ejection fraction (LVEF), termed as CB-ACS score. C-index of the score for predication of cardiovascular events was 0.79 (95% CI: 0.76-0.82) in development cohort and 0.77 (95% CI: 0.76-0.78) in the validation cohort (5832 person-years of follow-up), which outperformed GRACE 2.0 and ABC-ACS risk score. The CB-ACS score was also well calibrated in development and validation cohort (Greenwood-Nam-D'Agostino: P = 0.70 and P = 0.07, respectively). CONCLUSIONS CB-ACS risk score provides a useful tool for long-term prediction of CV events in patients with ACS. This model outperforms GRACE 2.0 and ABC-ACS ischemic risk score.

OBJECTIVE: To evaluate the dynamic changes of glucocorticoid (GC) dose and the feasibility of GC discontinuation in rheumatoid arthritis (RA) patients under the background of Chinese medicine (CM). METHODS: This multicenter retrospective cohort study included 1,196 RA patients enrolled in the China Rheumatoid Arthritis Registry of Patients with Chinese Medicine (CERTAIN) from September 1, 2019 to December 4, 2023, who initiated GC therapy. Participants were divided into the Western medicine (WM) and integrative medicine (IM, combination of CM and WM) groups based on medication regimen. Follow-up was performed at least every 3 months to assess dynamic changes in GC dose. Changes in GC dose were analyzed by generalized estimator equation, the probability of GC discontinuation was assessed using Kaplan-Meier curve, and predictors of GC discontinuation were analyzed by Cox regression. Patients with <12 months of follow-up were excluded for the sensitivity analysis. RESULTS: Among 1,196 patients (85.4% female; median age 56.4 years), 880 (73.6%) received IM. Over a median 12-month follow-up, 34.3% (410 cases) discontinued GC, with significantly higher rates in the IM group (40.8% vs. 16.1% in WM; P<0.05). GC dose declined progressively, with IM patients demonstrating faster reductions (median 3.75 mg vs. 5.00 mg in WM at 12 months; P<0.05). Multivariate Cox analysis identified age <60 years [P<0.001, hazard ratios (HR)=2.142, 95% confidence interval (CI): 1.523-3.012], IM therapy (P=0.001, HR=2.175, 95% CI: 1.369-3.456), baseline GC dose ⩽7.5 mg (P=0.003, HR=1.637, 95% CI: 1.177-2.275), and absence of non-steroidal anti-inflammatory drugs use (P=0.001, HR=2.546, 95% CI: 1.432-4.527) as significant predictors of GC discontinuation. Sensitivity analysis (545 cases) confirmed these findings. CONCLUSIONS RA patients receiving CM face difficulties in following guideline-recommended GC discontinuation protocols. IM can promote GC discontinuation and is a promising strategy to reduce GC dependency in RA management. (Trial registration: ClinicalTrials.gov, No. NCT05219214).
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Arthritis, Rheumatoid/drug therapy* ; Glucocorticoids/therapeutic use* ; Medicine, Chinese Traditional ; Retrospective Studies

Triple-negative breast cancer is therapeutically challenging due to the low expression of tumor markers and 'cold' tumor immunosuppressive microenvironment. Here, we present a dual-targeting peptide-drug conjugate (PDC) for tumor inhibition. Our PDC efficiently and selectively delivers cytotoxic Monomethyl Auristatin E (MMAE) into tumor cells via C-X-C chemokine receptor type 4 (CXCR4) and folate receptor 1 (FOLR1) for synergistic inhibition of growth and metastasis. Our results show that the dual-targeting PDC has potent antitumor activity in cultured human cells and several murine transplanted tumor models without apparent toxicity. The combination of dual-targeting PDC and radiotherapy modulates the tumor immunosuppressive microenvironment by increasing CD8⁺ T cell infiltration and attenuating the proportion of myeloid-derived suppressor and regulatory T cells. Therefore, our dual-targeting PDC represents a promising new strategy for cancer therapy that rebalances the immune system and promotes tumor regression.

Objective To investigate the correlation between dual-energy computed tomography angiography(CTA)parameters of carotid plaques and acute stroke events.Methods A retrospective analysis was conducted on the clinical and imaging data of patients who underwent dual-energy head and neck CTA and brain MRI scans.Utilizing the Siemens workstation(Syngo.Via VB40B),region of interest(ROI)were placed on the thickest slice of the carotid plaque in the axial plane to obtain parameters such as fat fraction(FF),virtual non-contrast(VNC)value,iodine concentration(IC),electron density(Rho),effective atomic number(Zeff),dual energy index(DEI),spectral curve,and corresponding CT values at 40 keV(40 keVHU)and 90 keV(90 keVHU).The slope of the energy spectrum curve(λ)was calculated within the 40 keV-90 keV range.Patients with acute cerebral infarction(ACI)in the ipsilateral anterior circulation territory were classified into the ACI group,while those without were classified into the non-acute cerebral infarction(NACI)(NACI group).Qualitative data were analyzed using the x2 test,and quantitative data were analyzed using the t-test.The predictive performance was assessed using the area under the curve(AUC)of the receiver operating characteristic(ROC)curve,and the differences between different ROC curves were compared using the DeLong test.Results A total of 72 patients were included,with 21 in the ACI group and 51 in the NACI group.The mean values of FF,Zeff,and 40 keVHU in the ACI group were greater than those in the NACI group.Statistically significant differences were observed between the groups for Zeff,DEI,40 keVHU,and λ(P＜0.05).40 keVHU demonstrated the highest predictive performance,and the AUC,sensitivity,and specificity was 0.789,81.0％,and 74.5％,respectively.A combined variable constructed through logistic regression analysis yielded an AUC,sensitivity,and specificity of 0.796,85.7％,and 70.6％,respectively,with no significant statistical differences compared to single factor variables.Conclusion Dual-energy CTA parameters of carotid plaques may aid in predicting intraplaque hemorrhage(IPH)and the occurrence of acute stroke events.