Jun-Hui Chen¹, Si-Run Du¹, Chang Liu¹, Bei-Bei Liu¹, Hai-Ying Xu², Xin-Ying Ji², Bo Feng³, Chun-Zheng Ma³, Jun-Hui Guo^3,*

Oncology Research, Vol.34, No.5, 2026, DOI:10.32604/or.2026.073484 - 22 April 2026

Abstract Esophageal cancer (EC) ranks among the most lethal gastrointestinal malignancies. Due to challenges in early diagnosis, molecular heterogeneity, and therapeutic resistance, patient prognosis remains extremely poor, necessitating the development of novel biomarkers and therapeutic targets. As a core effector of the Hippo signaling pathway, the potential significance of Yes-associated protein 1 (YAP1) has garnered increasing attention. This paper aims to systematically summarize the multi-omics research, molecular mechanisms, and preclinical/translational evidence for YAP1, covering its activation pathways, biological functions, clinical significance, and therapeutic strategies. We elucidated YAP1’s multidimensional regulatory network in EC, including Hippo-dependent and -independent mechanisms, cross-regulation… More >

Yahya Ghazwani^1,2,3, Mohammad Alghafees^1,2,3,*, Mishari Alshasha^1,2,3, Fahad Brayan^1,2,3, Abdulrahman Alsayyari^1,2,3, Ali Alyami^1,2,3

Canadian Journal of Urology, Vol.33, No.2, pp. 291-308, 2026, DOI:10.32604/cju.2026.077411 - 20 April 2026

Abstract Objectives: The emergence of artificial intelligence (AI) and predictive modeling offers prospects for clinical, anatomical, and imaging factor combination, like radiomics, to help with stone-free status (SFS) estimation and peroperative decision-making. The goal of this study was, therefore, to define the present performance range, determine sources of heterogeneity, and determine methodological practices permitting reliable implementation by varied circumstances. Methods: We searched six bibliographic databases through 19 September 2025. Studies deriving or validating AI/predictive models for SFS after ureteroscopy were eligible. Independent dual screening, duplicate data extraction, and risk-of-bias consideration using QUADAS-AI were conducted. Results: Five retrospective… More >

Jaisa Kaufmann^1,*, Max Bouvette², Abdul Qadar¹, Dominic Frimberger¹, Adam Rensing¹, Bhalaajee Meenakshi-Sundaram¹

Canadian Journal of Urology, Vol.33, No.2, pp. 451-457, 2026, DOI:10.32604/cju.2026.074190 - 20 April 2026

Abstract Background: Pediatric opioid use has been associated with serious adverse effects, including persistent use and overdose. Recent studies have shown that opioid needs may be minimal following outpatient pediatric urologic surgery. Post-operative pain regimens following pediatric penile surgery are not standardized. This study aimed to identify current opioid prescribing practices following hypospadias repair. Methods: An online survey was administered to members of the Societies for Pediatric Urology, including eight questions surrounding physician demographics, hypospadias repair case volume, attitudes regarding opioid prescription in pediatric urology, and post-operative pain regimens. Responses were stratified for analysis. Results: A total… More >

Zhenzhen Yang^1,#, Linxin Jiang^2,#, Shan Yin^3,*

Canadian Journal of Urology, Vol.33, No.2, pp. 261-270, 2026, DOI:10.32604/cju.2025.069717 - 20 April 2026

Abstract Objectives: Kidney stone disease is increasingly prevalent and may be linked to metabolic factors such as insulin resistance, but there is currently no direct evidence connecting estimated glucose disposal rate (eGDR) to kidney stones. This study aimed to investigate the relationship between eGDR and kidney stone prevalence. Methods: We conducted a cross-sectional analysis utilizing data from the National Health and Nutrition Examination Survey (NHANES) from 2007–2018, including 29,753 participants aged 20 years and older. Weighted multivariable logistic regression and nonlinear models were employed to assess the relationship between eGDR and self-reported kidney stone history. Results: Among… More > Graphic Abstract

Xiangyu Chen^#, Congzhe Ren^#, Lijun Xie, Xiaoqiang Liu^*

Canadian Journal of Urology, Vol.33, No.2, pp. 339-348, 2026, DOI:10.32604/cju.2025.069578 - 20 April 2026

Abstract Background: Regional differences in the incidence of prostate cancer (PCa) and prostatitis may be due to different food intake. But which foods affect PCa and prostatitis development or progression remains controversial. This study aims to explore the causal relationship between PCa and prostatitis and 30 different foods using two-sample Mendelian randomization (MR) and multivariable MR (MVMR) analysis. Methods: Data on 30 different foods were screened from the UK Biobank. PCa data came from a large meta-analysis of 140,254 individuals; prostatitis was obtained from the FinnGen consortium. The inverse variance weighted method was the main analysis… More >

Kai Dang^1,2,#, Teng Cui^1,2,#, Yongan Zhou^1,2, Jiayuan Ji^1,2, Yang Yang^1,2, Xiangyu Wang^1,2, Jing Xiao^1,2,*

Canadian Journal of Urology, Vol.33, No.2, pp. 403-415, 2026, DOI:10.32604/cju.2026.068291 - 20 April 2026

Abstract Objectives: Although renal colic is a well-known acute manifestation of urolithiasis, the relationship between its pain severity and a range of clinical parameters has not been clearly established by comprehensive studies. This study aimed to construct and validate a simple and accurate clinical nomogram for predicting the occurrence of more intense acute renal colic (ARC) in patients with urolithiasis. Methods: The development and validation of the prediction model followed the reporting standards outlined in the TRIPOD checklist. A retrospective analysis was conducted on 285 patients who visited the Department of Urology at Beijing Friendship Hospital,… More >

Daniil Kamakaev, Khaled Mahbub^*

Journal on Artificial Intelligence, Vol.8, pp. 183-202, 2026, DOI:10.32604/jai.2026.077823 - 15 April 2026

Abstract This paper presents an innovative system designed to automate the analysis of candidate interviews by integrating multiple analytical techniques into a single multimodal framework. This system combines text sentiment analysis, audio sentiment analysis, keyword extraction, and Mel-Frequency Cepstral Coefficients (MFCC) feature extraction to evaluate candidate performance holistically. This system employs text sentiment analysis using VADER and transformer-based sentiment features (probability-based outputs), audio sentiment analysis with an SVM model trained on both IEMOCAP and MELD datasets, keyword extraction via KeyBERT, and audio feature extraction including MFCCs, delta MFCCs, pitch, and energy to evaluate candidate performance holistically. More >

Sathish Rao Udupi, Gururaj Bolar, Manjunath Shettar^*, Ashwini Bhat

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.078574 - 09 April 2026

Abstract Glass fiber-reinforced polymer composites (GFRPCs) are extensively utilized in the aerospace, automotive, and structural sectors; nevertheless, their heterogeneous and abrasive characteristics result in rapid tool wear during drilling. Drill flank wear among various wear mechanisms notably influences hole quality and dimensional accuracy. This research investigates the impact of spindle speed, feed rate, and drill diameter on flank wear during dry drilling of GFRPC laminates with high-speed steel (HSS) twist drills. A full-factorial design with 81 experiments is used to create a comprehensive dataset. ANOVA indicates that spindle speed is the dominant factor affecting wear changes,… More >

Jaeseung Lee¹, Jehyeok Rew^2,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077691 - 09 April 2026

Abstract The accelerating transition toward electrified mobility has positioned electric vehicles (EVs) as a primary technology in modern transportation systems. In this context, ensuring the reliability of EV drive motors (EVDMs) becomes increasingly critical, given their central role in propulsion performance and operational safety. Accurate and interpretable fault diagnosis of EVDMs is therefore essential for enabling effective maintenance and supporting the broader sustainability and resilience of EVs. This study presents a novel framework that combines decision tree-based fault classification with a multi-agent large language model (LLM) interpretation architecture to deliver transparent and human-readable diagnostic explanations. The… More >

Zhaojun Gu¹, Wenlong Yue², Chunbo Liu^1,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077388 - 09 April 2026

Abstract Anomaly detection in system logs is a critical technical means for identifying potential faults and security risks. In distributed environments, traditional deep learning-based log anomaly detection methods often suffer from shortcomings in transparency, computational overhead, and data privacy protection. To address these issues, this paper proposes a federated learning-driven lightweight and explainable log anomaly detection framework named FedXLog. The framework adapts to heterogeneous logs through hierarchical feature extraction, introduces the Federated Gradient Trajectory Aggregation algorithm (FedGradTrace) to enhance the explainability of the parameter aggregation process, constructs lightweight models using knowledge distillation, and achieves globally consistent… More >