Renal unilateral ischemia-reperfusion injury (UIRI) constitutes a significant global health challenge, with poor recovery leading to chronic kidney disease and subsequent renal fibrosis. Extracellular vesicles (EVs) present substantial potential benefits for renal diseases. However, the limited yield and efficacy of EVs produced through traditional methodologies (2D-EVs) severely restrict their widespread application. Moreover, the efficient and effective strategies for using EVs in UIRI treatment and their mechanisms remain largely unexplored. In this study, we propose an innovative approach by integrating bioprinted mesenchymal stem cell microfiber extracellular vesicles production technology (3D-EVs) with a tail vein injection method, introducing a novel treatment strategy for UIRI. Our comparison of the biological functions of 2D-EVs and 3D-EVs, both in vitro and in vivo, reveals that 3D-EVs significantly outperform 2D-EVs. Specifically, in vitro, 3D-EVs demonstrate a superior capacity to enhance the proliferation and migration of NRK-52E cells and mitigate hypoxia/reoxygenation (H/R)-induced injuries by reducing epithelial-mesenchymal transformation, extracellular matrix deposition, and ferroptosis. In vivo, 3D-EVs exhibit enhanced therapeutic effects, as evidenced by improved renal function and decreased collagen deposition in UIRI mouse kidneys. We further elucidate the mechanism by which 3D-EVs derived from KLF15 ameliorate UIRI-induced tubular epithelial cells (TECs) ferroptosis through the modulation of SLC7A11 and GPX4 expression. Our findings suggest that bioprinted mesenchymal stem cells microfiber-derived EVs significantly ameliorate renal UIRI, opening new avenues for effective and efficient EV-based therapies in UIRI treatment.

To identify independent risk factors for urosepsis in diabetic patients with upper urinary tract stones (UUTS) and develop a prediction model to facilitate early detection and diagnosis, we retrospectively reviewed medical records of patients admitted between January 2020 and June 2023. Patients were divided based on the quick Sequential Organ Failure Assessment (qSOFA) score. The least absolute shrinkage and selection operator (LASSO) regression analysis was used for variable selection to form a preliminary model. The model was optimized and validated using the receiver operating characteristic (ROC) curve, the Hosmer-Lemeshow test and calibration curve, and decision curve analysis (DCA). A nomogram was constructed for visualization. A total of 434 patients were enrolled, with 66 cases and 368 controls. Six optimal predictors were identified: underweight, sarcopenia, poor performance status, midstream urine culture, urinary leukocyte count, and albumin-globulin ratio (AGR). The midstream urine culture was excluded due to its inability to provide rapid results. The final model demonstrated good prediction accuracy and clinical utility, with no significant difference in performance compared to the initial model. The study developed a prediction model for urosepsis risk in diabetic patients with UUTS, presenting a convenient tool for timely diagnosis, particularly in non-operated patients.

Background: The study aimed to find a potential long non-coding RNA (lncRNA) model related to survival in bladder cancer by analyzing data in The Cancer Genome Atlas (TCGA). Methods: We downloaded the gene expression data from the TCGA and analyzed the differentially expressed lncRNAs (DELs) between tumor and normal tissues. Patients were divided into training and testing groups, and a prognostic risk score model with lncRNAs was constructed by using data in the training group using multivariate Cox and lasso regression analysis. We divided patients into high-and low-risk groups according to the median value in the lncRNA signature model. Survival and receiver operating characteristic (ROC) curves were visualized in both groups. Further, we validated the model in the testing group. Results: We screened 169 DELs for bladder cancer. The univariate Cox regression analysis showed that 13 lncRNAs were associated with prognosis with a p-value <0.01. We selected 12 of these lncRNAs to perform a multivariate Cox analysis to build the lncRNA signature. The formula with nine lncRNAs, namely, MIR497HG, LINC00968, NALCN-AS1, LINC02321, RNF144A-AS1, MNX1-AS1, FLJ22447, LINC01956, FLJ42969, was significantly related to prognosis. Patients in the high-risk group had a lower survival rate compared with the low‐risk group in the training and testing sets (both p-values < 0.05) and the area of the ROC curve was 0.737 and 0.68, respectively. Conclusions: The study illustrated a significant lncRNA signature and indicated the risk score Cox model could be an important biomarker to predict the prognosis of bladder cancer.

Background: Specific alterations in human leukocyte antigen class I (HLA-I) loci are associated with clinical outcomes for immune checkpoint inhibitors, which increases the clinical relevance of accurate high-resolution HLA genotyping in immuno-oncology applications. Numerous algorithms have been developed for high- to full-resolution HLA genotyping by next-generation sequencing (NGS) data; however, Sanger sequencing-based typing (SBT) remains the gold standard. With the increasing use of NGS for clinical oncology, it is important to identify the computational tool with comparable performance as the gold standard. This study aimed to benchmark 5 algorithms against SBT for the high-resolution typing of classical HLA-I genes for targeted NGS data from blood and tissue samples. Methods: Paired white blood cell (WBC), plasma, and tissue deoxyribonucleic acid (DNA) samples derived from 22 cancer patients with known HLA genotypes were sequenced using a panel of all the following exons of classical HLA-I genes: HLA-A, HLA-B, and HLA-C. NGS-based genotypes were generated by the 5 different algorithms, including HLA-HD, HLAscan, OptiType, Polysolver, and xHLA. Accuracy was defined as the concordance between the SBT and NGS-based algorithms. Accuracy was computed as the fraction of all the alleles with concordant genotype using the SBT and any of the algorithm over the total number of alleles. Results: In relation to the WBC, plasma, and tissue samples, all 5 algorithms were highly accurate at low-resolution HLA-I genotyping, but had more varied accuracy at high-resolution HLA-I genotyping, particularly in HLA-A. The in-silico analyses revealed that high-resolution genotyping by all 5 algorithms achieved approximately 90% accuracy at sequencing depths of 6,000× – 100× for the WBC samples, at 6,000× – 700× for the plasma samples, and at 1,000× – 100× for the tissue samples. Among the 5 algorithms, HLA-HD was consistently accurate at high-resolution HLA-I genotyping, and had an accuracy of 93.9% for the WBC samples, 87.9% for the plasma samples, and 94.2% for tissue samples even at a 50× sequencing depth. Conclusions: We found that HLA-HD was an accurate algorithm for the high-resolution genotyping of classical HLA-I genes sequenced by our targeted panel, particularly at a sequencing depth ≥300× for blood and tissue samples.

Tumors have a unique niche system that plays an important role in their occurrence and development. At present, there is increasing interest in the biomechanical properties of niches. The increased stemness of cancer cells is closely related to bladder cancer progression and recurrence. However, how biomechanical properties in the niche regulate bladder cancer stemness remains unclear. Here, we show that as bladder cancer progresses, matrix stiffness increases, and tumor stemness increases. Mechanistically, high matrix stiffness mediates β-catenin nuclear translocation by increasing the nuclear pore size. On the other hand, it promotes the expression of the nuclear cytoskeletal protein Lamin A/C, inhibits the nuclear export of β-catenin, and finally, it upregulates the Wnt pathway to increase the stemness of cancer cells. These findings reveal a role for matrix stiffness in the regulation of stemness in bladder cancer cells and suggest that targeting matrix stiffness may be an effective strategy to delay bladder cancer progression.