Xiaomin Yin
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Development and validation of a machine learning-based risk prediction model for PICC-associated bloodstream infections in preterm infants: a retrospective multicenter study
Abstract Objective: To develop and validate a machine learning–based model for predicting the risk of peripherally inserted central catheter (PICC)–associated bloodstream infection (CRBSI) in preterm infants. Methods: This retrospective multicenter study included 151 preterm infants with CRBSI and 302 matched controls from a tertiary hospital (2017–2024), randomly divided into a training set (n = 317) and an internal validation set (n = 136).
Jasmonic Acid-Mediated Antioxidant Defense Confers Chilling Tolerance in Okra (Abelmoschus esculentus L.)
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Interferon Regulatory Factors as a Potential Therapeutic Target for Neuroinflammation: A Focus on Alzheimer’s Disease
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