Background/Aims: Nephrolithiasis is one of the most prevalent diseases of the urinary system. Approximately 80% of human kidney stones are composed of calcium oxalate (CaOx), and hypercalciuria is one of the most common metabolic disorders. Emerging evidence indicates that autophagy and inflammatory responses are related to the formation of CaOx nephrolithiasis. However, the roles of autophagy and inflammation in patients with hypercalciuria remain unclear. Ethyl pyruvate (EP) displays protective effects in experimental models of many illnesses. In this study, we investigated the protective effects of EP in vitro through its inhibition of autophagy and inflammatory responses after CaCl₂-induced tubular epithelial cell injury. Methods: First, we cultured human tubular epithelial (HK-2) cells in the presence of various concentrations of CaCl₂ (0, 0.1, 0.25, 0.5, 1.0, 1.5, and 2.0 mg/ml) for 12 h and EP (0, 1.0, 2.5, 5.0, and 10.0 mM) for 2 h to select the optimum concentration using the Cell Counting Kit-8 assay and lactate dehydrogenase (LDH) assay. Cells in culture were stimulated with CaCl₂ (1.0 mg/ml, 12 h) with or without EP pretreatment (2.5 mM, 2 h). After the exposure, we detected the expression of inflammation-related proteins using an enzyme-linked immunosorbent assay and Western blot analysis. Finally, the levels of autophagy-related proteins were determined through Western blot analysis, and the number of GFP-LC3 dots and autophagic vacuoles was detected under confocal microscopy. Results: With the use of the Cell Counting Kit-8 assay and the LDH assay, we identified the optimum concentration for CaCl₂ (1.0 mg/ml) treatment and EP pretreatment (2.5 mM). Our research indicated that CaCl₂ can induce autophagy and inflammatory responses in HK-2 cells. Furthermore, treatment with EP prior to CaCl₂ stimulation attenuated HK-2 cell injury by inhibiting autophagy and inflammation. Conclusion: Our results provide evidence that EP attenuates CaCl₂-induced injury of HK-2 cells by downregulating the expression of inflammation and autophagy proteins that may be associated with the inhibition of the high-mobility group box-1 (HMGB1)/toll-like receptor 4 (TLR4)/NF-κB pathway and the competitive interaction with Beclin-1 of HMGB1.

Objective: To develop an ultrasound-driven clinical deep learning radiomics (CDLR) model for stratifying the risk of testicular masses, aiming to guide individualized treatment and minimize unnecessary procedures. Methods: We retrospectively analyzed 275 patients with confirmed testicular lesions (January 2018 to April 2023) from two hospitals, split into training (158 cases), validation (68 cases), and external test cohorts (49 cases). Radiomics and deep learning (DL) features were extracted from preoperative ultrasound images. Following feature selection, we utilized logistic regression (LR) to establish a deep learning radiomics (DLR) model and subsequently derived its signature. Clinical data underwent univariate and multivariate LR analyses, forming the "clinic signature." By integrating the DLR and clinic signatures using multivariable LR, we formulated the CDLR nomogram for testicular mass risk stratification. The model’s efficacy was gauged using the area under the receiver operating characteristic curve (AUC), while its clinical utility was appraised with decision curve analysis(DCA). Additionally, we compared these models with two radiologists' assessments (5–8 years of practice). Results: The CDLR nomogram showcased exceptional precision in distinguishing testicular tumors from non-tumorous lesions, registering AUCs of 0.909 (internal validation) and 0.835 (external validation). It also excelled in discerning malignant from benign testicular masses, posting AUCs of 0.851 (internal validation) and 0.834 (external validation). Notably, CDLR surpassed the clinical model, standalone DLR, and the evaluations of the two radiologists. Conclusion: The CDLR nomogram offers a reliable tool for differentiating risks associated with testicular masses. It augments radiological diagnoses, facilitates personalized treatment approaches, and curtails unwarranted medical procedures.

Background: Several studies have been conducted on the relationship between insulin-like growth factor 1 gene (IGF-1) rs35767 polymorphisms and cancer risk, but the results are conflicting. We performed a meta-analysis to investigate the relationship between IGF-1 rs35767 polymorphisms and cancer risk. Methods: Eight studies (5 for IGF-1 rs35767 C>T and 3 for IGF-1 rs35767 A>G) with a total of 11,257 cases and 16,213 controls were included. The studies were about the association between IGF-1 rs35767 polymorphisms and cancer risk and acquired by searching PubMed, Embase, and Web of Science databases for articles published before January 20, 2019. STATA software was used to analyze the data and identify the strength of the association by using pooled-odds ratios (ORs) with corresponding 95% confidence intervals (CIs). Results: No significant associations were observed between the IGF-1 rs35767 C>T polymorphism and cancer risk in all genetic models. However, the IGF-1 rs35767 A>G polymorphism was significantly associated with increased cancer risk for all genetic models (G vs A: OR = 1.087, 95% CI: 1.036–1.141, P _h = .338; GG vs AA: OR = 1.272, 95% CI: 1.121–1.442, P _h = .359; AG vs AA: OR = 1.187, 95% CI: 1.043–1.351, P _h = .695; AG+GG vs AA: OR = 1.187, 95% CI: 1.043–1.351, P _h = .695; GG vs AA+AG: OR = 1.086, 95% CI: 1.025–1.151, P _h = .275). Begg and Egger tests showed that no publication bias existed. Conclusion: Our findings indicated that the IGF-1 rs35767 A>G polymorphism might be a risk factor for cancer development. However, additional well-designed studies with sample sizes larger than ours need to be conducted in the future to verify our findings.

Objective: This study sought to create a CT-based clinical deep learning radiomics (CDLR) model to noninvasively differentiate between pyelocaliceal upper tract urothelial carcinoma (UTUC) and xanthogranulomatous pyelonephritis (XGP) preoperatively, aiding in personalized treatment decisions. Methods: A retrospective analysis included 161 patients with pathologically confirmed pyelocaliceal UTUC or XGP following surgery. Patients were randomly divided into a training cohort (n=112) and a validation cohort (n=49). Radiomics (Rad) features and deep learning (DL) features were extracted from the corticomedullary phase CT images. Combined with clinical features, and after feature selection, a logistic regression (LR) classifier was employed to construct the clinical model, the deep learning radiomics (DLR) model, and the clinical deep learning radiomics (CDLR) model. Model performance was evaluated using receiver operating characteristic (ROC) curves and decision curve analysis (DCA), with interpretability assessed by the shapley additive explanations (SHAP) method. In addition, the diagnostic performance of the models was compared with that of a radiologist with 5 years of experience in abdominal CT diagnosis. Results: The CDLR model achieved AUCs of 0.955 in the training cohort and 0.930 in the validation cohort. In the validation cohort, the CDLR model significantly outperformed the radiologist’s assessment (AUC: 0.930 vs. 0.695, P = 0.0006) and yielded numerically higher AUCs than the clinical model (0.823) and the DLR model (0.882). DCA indicated that the CDLR model provided the highest net clinical benefit. SHAP analysis demonstrated good model interpretability. Conclusion: The CDLR model integrating clinical, Rad, and DL features serves as an effective noninvasive tool for preoperative differentiation of pyelocaliceal UTUC and XGP, offering valuable reference for clinical decision-making.