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.

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 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.

Objective:To evaluate the clinical utility of an intelligent endoscope transportation system in the digestive endoscopy center.Methods:A parallel-group controlled trial was conducted at Digestive Endoscopy Center of Qilu Hospital of Shandong University from June 1st to December 31st 2024, comparing robotic intelligent endoscope transport (experimental group) versus manual transport (control group). Performance metrics, including response time, transportation speed, labor efficiency, contamination prevention, closed-loop traceability, and nursing staff satisfaction, were statistically analyzed. Full-time equivalent (FTE) was introduced to quantify the operational efficiency of the experimental group.Results:The study included a total of 60 206 instances of intelligent endoscope transportation and 60 485 instances of manual transportation data. The robotic group demonstrated significantly shorter response times versus manual group for initial dispatch (51.08±14.97 seconds VS 54.44±13.61 seconds, t=35.8, P<0.001) and recovery response time (32.52±11.26 seconds VS 40.20±11.40 seconds, t=103.93, P<0.001). During the 148 days operational period, the success rate was 99.83% (60 104/60 206) and the failure rate was 0.17% (102/60 206) for robotic transports. Primary failure causes were wireless disconnection, pathfinding errors, and mechanical faults, averaging 1.05 malfunctions/month with no adverse events. The success and failure rate was 99.26% (60 043/60 485) and 0.74% (442/60 485) respectively for manual transports. Staff satisfaction was significantly higher for robotic transport in endoscopic transportation (4.65±0.55 scores VS 3.97±0.98 scores, t=96.5, P<0.001) and delivery process (4.71±0.59 scores VS 3.90±1.04 scores, t=210.3, P<0.001). and workload intensity was significantly lower (4.06±0.77 scores VS 4.48±0.63 scores, t=59.9, P=0.025). The system reduced labor requirements by 3.68 FTE, yielding annual savings of ￥657 000. Conclusion:The robotic intelligent endoscope transport system improves work efficiency, reduces nursing labor costs and physical workload, enhances job experience and satisfaction, and enables full-process smart traceability, providing a validated solution for endoscopy center logistics.

A recombinant adenovirus(rAd)for expression of Mycobacterium tuberculosis(M.tb)c-di-AMP phosphodiesterase CnpB was constructed,and its induced humoral immune response was detected.The codon-optimized gene of M.tb CnpB was cloned into the adenoviral plasmid pcADV.The recombinant plasmid pcADV-CnpB was transfected into HEK293T cells,and expression was detected with Western blot.The recombinant plasmid pcADV-CnpB and the backbone plasmid were co-transfected into HEK293T cells to obtain the recombinant adenovirus rAd-CnpB.rAd-CnpB was amplified in HEK293T cells,and the target protein expression of rAd-CnpB was detected with Western blot and immunofluorescence.Mice were immunized with rAd-CnpB intranasally,and their sera and bronchoalveolar lavage fluid(BALF)were collected.ELISA was used to detect levels of antigen-specific antibodies.Restriction enzyme digestion and sequencing indicated that the recombinant plasmid pcADV-CnpB was successfully constructed and led to protein expression in eukaryotic cells.rAd-CnpB was packaged and produced in HEK293T cells.After amplification and purification,rAd-CnpB with a titer of 5.53×1010 PFU/mL was obtained.rAd-CnpB led to CnpB expression in HEK293T cells.Intranasal immunization with rAd-CnpB increased levels of IgG and secretory IgA in BALF and led to high levels of IgG in sera.rAd-CnpB,the recombinant adenovirus for expression of c-di-AMP phosphodiesterase CnpB was successfully constructed,and was found to induce antigen-specific humoral and mucosal immune responses through mucosal immunization.Thus,rAd-CnpB may be used in further research on new TB vaccine strategies.

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.