Heat shock protein family A member 8 is a prognostic marker for bladder cancer: Evidences based on experiments and machine learning

Prediction of spontaneous distal ureteral stone passage using artificial intelligence

PURPOSE

Identifying factors predicting the spontaneous passage of distal ureteral stones and evaluating the effectiveness of artificial intelligence in prediction.

MATERIALS AND METHODS

The files of patients presenting with distal ureteral stones were retrospectively evaluated. Those who experienced spontaneous passage were assigned to Group P, while those who did not were assigned to Group N. Demographic and clinical data of both groups were compared. Then, logistic regression analysis was performed to determine the factors predicting spontaneous stone passage. Based on these factors, a logistic regression model was prepared, and artificial intelligence algorithms trained on the dataset were compared with this model to evaluate the effectiveness of artificial intelligence in predicting spontaneous stone passage.

RESULTS

When comparing stone characteristics and NCCT findings, it was found that the stone size was significantly smaller in Group P (4.9 ± 1.7 mm vs. 6.8 ± 1.4 mm), while the ureteral diameter was significantly higher in Group P (3.3 ± 0.9 mm vs. 3.1 ± 1.1 mm) (p < 0.05). Parameters such as stone HU, stone radiopacity, renal pelvis AP diameter, and perirenal stranding were similar between the groups. In multivariate analysis, stone size and alpha-blocker usage were significant factors in predicting spontaneous stone passage. The ROC analysis for the logistic regression model constructed from the significant variables revealed an area under the curve (AUC) of 0.835, with sensitivity of 80.1% and specificity of 68.4%. AI algorithms predicted the spontaneous stone passage up to 92% sensitivity and up to 86% specifity.

CONCLUSIONS

AI algorithms are high-powered alternatives that can be used in the prediction of spontaneous distal ureteral stone passage.

Designing Peptide-Based Nanoinhibitors of Programmed Cell Death Ligand 1 (PD-L1) for Enhanced Chemo-immunotherapy

The combination of immune checkpoint blockade (ICB) and chemotherapy has shown significant potential in the clinical treatment of various cancers. However, circulating regeneration of PD-L1 within tumor cells greatly limits the efficiency of chemo-immunotherapy and consequent patient response rates. Herein, we report the synthesis of a nanoparticle-based PD-L1 inhibitor (FRS) with a rational design for effective endogenous PD-L1 suppression. The nanoinhibitor is achieved through self-assembly of fluoroalkylated competitive peptides that target PD-L1 palmitoylation. The FRS nanoparticles provide efficient protection and delivery of functional peptides to the cytoplasm of tumors, showing greater inhibition of PD-L1 than nonfluorinated peptidic inhibitors. Moreover, we demonstrate that FRS synergizes with chemotherapeutic doxorubicin (DOX) to boost the antitumor activities via simultaneous reduction of PD-L1 abundance and induction of immunogenic cell death in murine colon tumor models. The nano strategy of PD-L1 regulation present in this study is expected to advance the development of ICB inhibitors and overcome the limitations of conventional ICB-assisted chemo-immunotherapy.