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Introduction And Objectives: Computed tomography (CT) is one of the most commonly used diagnostic modalities for urinary stone disease. In this study we developed a CT and clinical parameter-based prediction model for shockwave lithotripsy (SWL) outcome in proximal ureteral stones.
Materials And Methods: Data from 223 patients with single proximal ureteral stones treated with SWL between January 2009 and January 2015 were reviewed retrospectively. Clinical parameters including age, sex, body weight, and body mass index (BMI) were analyzed in combination with stone-related CT parameters (stone diameter, height, volume, location, Hounsfield units [HU], stone-to-skin distance [SSD]), and secondary signs (hydronephrosis, perinephric edema, and rim sign). Based on the cutoff values determined by c-statistics, a scoring system for the prediction of SWL outcome was developed.
Results: The success rate was 65.9% (147/223), and in a univariate analysis body weight, BMI, SSD (vertical, horizontal), HU, stone diameter, height, volume, and all secondary signs were significantly associated with the success of SWL. However, on multivariate analysis only BMI (odds ratio [OR] = 1.322, confidence interval [CI] 1.156, 1.512, p = 0.00), stone diameter (OR = 1.397, CI 1.259, 1.551, p = 0.00), and perinephric edema (grade 0-1 vs 3-4, OR = 2.831, CI 1.032, 7.764, p = 0.043) were independent predictors of SWL success. The prediction model based on the logistic regression analysis was as follows: SWL success = 1/[1 + exp (-10.165 + 0.279 × [BMI] + 0.334 × [diameter] + 1.040 [perinephric edema])], having an area under the curve of 0.881. In the prediction model based on these parameters, scores of 0, 1, 2, and 3 correlated with SWL success rates of 98.5%, 65.7%, 31.4%, and 0%, respectively.
Conclusions: BMI, stone diameter, and perinephric edema were independent predictors of SWL outcome and a prediction model based on these parameters will facilitate decision-making for SWL in proximal ureteral stones.
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http://dx.doi.org/10.1089/end.2016.0056 | DOI Listing |
Eur J Radiol
September 2025
Department of Radiology, Affiliated Hospital of Hebei University, Baoding 071000, China. Electronic address:
Purpose: The present study aimed to develop a noninvasive predictive framework that integrates clinical data, conventional radiomics, habitat imaging, and deep learning for the preoperative stratification of MGMT gene promoter methylation in glioma.
Materials And Methods: This retrospective study included 410 patients from the University of California, San Francisco, USA, and 102 patients from our hospital. Seven models were constructed using preoperative contrast-enhanced T1-weighted MRI with gadobenate dimeglumine as the contrast agent.
Biomol Biomed
September 2025
Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, China.
Coronary heart disease (CHD) is a leading cause of morbidity and mortality; patients with type 2 diabetes mellitus (T2DM) are at particularly high risk, highlighting the need for reliable biomarkers for early detection and risk stratification. We investigated whether combining the stress hyperglycemia ratio (SHR) and systemic inflammation response index (SIRI) improves CHD detection in T2DM. In this retrospective cohort of 943 T2DM patients undergoing coronary angiography, associations of SHR and SIRI with CHD were evaluated using multivariable logistic regression and restricted cubic splines; robustness was examined with subgroup and sensitivity analyses.
View Article and Find Full Text PDFJ Org Chem
September 2025
State Key Laboratory of Fine Chemicals, School of Chemical Engineering, Ocean and Life Sciences, Dalian University of Technology, Panjin 124221, P. R. China.
The Buchwald-Hartwig (B-H) reaction graph, a novel graph for deep learning models, is designed to simulate the interactions among multiple chemical components in the B-H reaction by representing each reactant as an individual node within a custom-designed reaction graph, thereby capturing both single-molecule and intermolecular relationship features. Trained on a high-throughput B-H reaction data set, B-H Reaction Graph Neural Network (BH-RGNN) achieves near-state-of-the-art performance with an score of 0.971 while maintaining low computational costs.
View Article and Find Full Text PDFJ Med Internet Res
September 2025
School of Advertising, Marketing and Public Relations, Faculty of Business and Law, Queensland University of Technology, Brisbane, Australia.
Background: Labor shortages in health care pose significant challenges to sustaining high-quality care for people with intellectual disabilities. Social robots show promise in supporting both people with intellectual disabilities and their health care professionals; yet, few are fully developed and embedded in productive care environments. Implementation of such technologies is inherently complex, requiring careful examination of facilitators and barriers influencing sustained use.
View Article and Find Full Text PDFJ Chem Inf Model
September 2025
Department of Chemistry, Delaware State University, Dover, Delaware 19901, United States.
The calculation of the highest occupied molecular orbital-lowest unoccupied molecular orbital (HOMO-LUMO) gap for chemical molecules is computationally intensive using quantum mechanics (QM) methods, while experimental determination is often costly and time-consuming. Machine Learning (ML) offers a cost-effective and rapid alternative, enabling efficient predictions of HOMO-LUMO gap values across large data sets without the need for extensive QM computations or experiments. ML models facilitate the screening of diverse molecules, providing valuable insights into complex chemical spaces and integrating seamlessly into high-throughput workflows to prioritize candidates for experimental validation.
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