Yueying Yang
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Construction and validation of a machine learning model for predicting anastomotic leak following radical esophagectomy for esophageal cancer
Abstract Background: Early identification of patients at high risk of anastomotic leak (AL) following esophagectomy is essential for improving surgical outcomes. However, reliable preoperative risk stratification remains challenging. This study aimed to predict AL risk in the esophageal cancer (EC) population by developing and validating a machine learning (ML)-based model using exclusively preoperative and baseline clinical data.
Wheat NRAMP3 Transporters Govern Grain Cadmium Concentration
Conflicts of Interest The authors declare no conflicts of interest. Data Availability Statement Data sharing not applicable to this article as no datasets were generated or analysed during the current study. Supporting Information Filename Description pce70348-sup-0001-Supporting_figures_and_tables_20250928_mark_f.docx7.4 MB Supporting Figure 1: Gene structures and sequences of TpNRAMP3-7A and TpNRAMP3-7B.
The Relationship Between Magnetic Resonance Diffusion Tensor Imaging Parameters and Muscle Dysfunction in Patients with Osteoporosis: A Cross-Sectional Retrospective Study
Back to Journals » International Journal of General Medicine » Volume 19 Original Research General Medicine Authors Wang H, Yang Y, Cui J, Wang Y, Zhang C, Guo Z, Zheng D Received 29 August 2025 Accepted for publication 10 April 2026 Published 23 April 2026 Volume 2026:19 560521 DOI https://doi.org/10.2147/IJGM.S560521 Checked for plagiarism Yes Review by Single anonymous peer review Peer reviewer comments 2 Editor who approved publication: Dr Woon-Man Kung Hui Wang,1,* Yueying...
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