Showing 40,021 - 40,040 results of 41,229 for search '"naturalization"', query time: 0.23s Refine Results
  1. 40021

    Solar-Driven Water Purification: Advancing PVA-Chitosan/PANI Hydrogel to Enhance Solar Vapor Generation for Freshwater Treatment by Flora Serati, Syazwani Mohd Zaki, Ahmad Akid Zulkifli, Siti Rabizah Makhsin

    Published 2025-01-01
    “…This finding indicates the capability of PVA-Chitosan/PANi/3.9 mol.% hydrogels in generating multi-scattering effects of natural sunlight for high-efficiency light-to-heat conversion via SVG. …”
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  2. 40022

    Designing an entrepreneurial model in the banking network with a digital technology approach by Afshin Nobakht, majid nasiri, parviz Saeedi

    Published 2024-12-01
    “…The research method is applicable according to its purpose, quantitative in terms of execution method, and exploratory research in terms of nature. The statistical population of the research includes 384 experts in the field of information technology and entrepreneurship in the banking industry, by simple random sampling method. …”
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    Predicting Axillary Lymph Node Metastasis in Young Onset Breast Cancer: A Clinical-Radiomics Nomogram Based on DCE-MRI by Dong X, Meng J, Xing J, Jia S, Li X, Wu S

    Published 2025-01-01
    “…Xia Dong,1 Jingwen Meng,1 Jun Xing,2 Shuni Jia,3 Xueting Li,4 Shan Wu1 1Department of Radiology, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, 030032, People’s Republic of China; 2Department of Breast Surgery, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, 030032, People’s Republic of China; 3Department of Ultrasound, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, 030032, People’s Republic of China; 4Department of Pathology, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, 030032, People’s Republic of ChinaCorrespondence: Shan Wu, Department of Radiology, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, No. 99, Longcheng Street, Taiyuan, Shanxi Province, 030032, People’s Republic of China, Tel +86 03518368398, Email drshanwu@outlook.comBackground: Young onset breast cancer, diagnosed in women under 50, is known for its aggressive nature and challenging prognosis. Precisely forecasting axillary lymph node metastasis (ALNM) is essential for customizing treatment plans and enhancing patient results.Objective: This research sought to create and verify a clinical-radiomics nomogram that combines radiomic features from Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) with standard clinical predictors to improve the accuracy of predicting ALNM in young breast cancer patients.Methods: We performed a retrospective analysis at one facility, involving the creation and validation of a nomogram in two stages.At first, a medical model was developed utilizing conventional indicators like tumor dimensions, molecular classifications, multifocal presence, and MRI-determined ALN status.A more detailed clinical-radiomics model was subsequently developed by integrating radiomic characteristics derived from DCE-MRI images.These models were created using logistic regression analyses on a training dataset, and their effectiveness was assessed by measuring the area under the receiver operating characteristic curve (AUC) in a separate validation dataset.Results: The clinical-radiomics nomogram surpassed the clinical-only model, recording an AUC of 0.892 in the training dataset and 0.877 in the validation dataset.Significant predictors included MRI-reported ALN status and select radiomic features, which markedly enhanced the model’s predictive capacity.Conclusion: Integrating radiomic features with clinical predictors in a nomogram significantly improves ALNM prediction in young onset breast cancer, providing a valuable tool for personalized treatment planning. …”
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