Chongqing Dazu District People's Hospital

The use of plant-based medicines for the production of green nanomaterials represents a viable route for cancer treatment. In this study, we report a novel method for the biosynthesis of zinc oxide nanoparticles (ZnO NPs) using the leaf extract of Ruellia tuberosa L. a medicinal plant known for its therapeutic properties. The study aims to test and investigate the ability of these R. tuberosa-derived ZnO NPs (RT-ZnO NPs) to cause apoptosis in human prostate cancer (PC) cells and clarify their fundamental molecular pathways. The developed RT-ZnO NPs were analysed employing field emission scanning electron microscopy, which exhibited a spherical shape with a median particle size of 156.7 nm. The methylthiazolyldiphenyl-tetrazolium bromide experiment showed that RT-ZnO NPs demonstrated considerable cytotoxicity toward DU 145 PC cells, with an IC₅₀ value of 26.82 µg/mL, while demonstrating low toxicity against normal HPE-15 prostate epithelial cells. Furthermore, the molecular analysis demonstrated that the NPs boosted p53 expression while suppressing both total and phosphorylated STAT3, indicating that anticancer activity is mediated by the p53 and STAT3 signalling pathways. This study focuses on a green and cost-effective method for creating anticancer nanomaterials, with RT-ZnO NPs emerging as a potential alternative for PC therapy.

To develop and validate a computed tomography (CT) radiomics-based interpretable machine learning (ML) model for predicting 5-year recurrence-free survival (RFS) in non-metastatic clear cell renal cell carcinoma (ccRCC).

559 patients with non-metastatic ccRCCs were retrospectively enrolled from eight independent institutes between March 2013 and January 2019, and were assigned to the primary set (n=271), external test set 1 (n=216), and external test set 2 (n=72). 1316 Radiomics features were extracted via "Pyradiomics." The least absolute shrinkage and selection operator algorithm was used for feature selection and Rad-Score construction. Patients were stratified into low and high 5-year recurrence risk groups based on Rad-Score, followed by Kaplan-Meier analyses. Five ML models integrating Rad-Score and clinicopathological risk factors were compared. Models' performances were evaluated via the discrimination, calibration, and decision curve analysis. The most robust ML model was interpreted using the SHapley Additive exPlanation (SHAP) method.

13 radiomic features were filtered to produce the Rad-Score, which predicted 5-year RFS with area under the receiver operating characteristic curve (AUCs) of 0.734-0.836. Kaplan-Meier analysis showed significant survival differences based on Rad-Score (all Log-Rank p values <0.05). The random forest model outperformed other models, obtaining AUCs of 0.826 [95% confidential interval (CI): 0.766-0.879] and 0.799 (95% CI: 0.670-0.899) in the external test set 1 and 2, respectively. The SHAP analysis suggested positive associations between contributing factors and 5-year RFS status in non-metastatic ccRCC.

CT radiomics-based interpretable ML model can effectively predict 5-year RFS in non-metastatic ccRCC patients, distinguishing between low and high 5-year recurrence risks.

Osteoporosis fracture is a common and most serious complication of osteoporosis.

This study sought to assess the level, the diagnostic potential, and the effect of circulating miR-4534 in osteoporotic fractures.

GSE74209 and GSE93883 were analyzed using GEO2R online tool for differentially expressed microRNAs in osteoporotic fractures. Postmenopausal women were recruited and serum samples were determined by RT-qPCR for level of miR-4534. ROC curves were plotted to evaluate the diagnostic value of miR-4534 in osteoporotic fractures. The effects of miR-4534 on hFOB 1.19 osteogenic marker levels, proliferation, and migration were measured by RT-qPCR, CCK-8 assay and Transwell assay, respectively. The target gene for miR-4534 was predicted and rescue experiments were conducted.

miR-4534 was identified as an upregulated miR in osteoporotic fracture. Up-regulated miR-4534 had the potential to distinguish osteoporotic fracture from health, and from common osteoporosis. The up-regulated miR-4534 in hFOB 1.19 cocultured with human umbilical vein endothelial cells could inhibit cell osteogenic marker levels, proliferation and migration. CFTR was a target gene of miR-4534 and rescued the suppression of miR-4534 on hFOB 1.19 activity.

MiR-4534 is a new potential diagnostic biomarker for osteoporotic fractures. MiR-4534 can regulate osteoblast differentiation, proliferation, and migration by targeting CFTR.