Kejing Wang
6 Papers
Kejing Wang is an academic researcher. The author has contributed to research in topics: Medicine & Internal medicine. The author has an hindex of 1, co-authored 6 publications.
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Papers
Myelodysplastic Syndrome/Acute Myeloid Leukemia Following the Use of Poly-ADP Ribose Polymerase (PARP) Inhibitors: A Real-World Analysis of Postmarketing Surveillance Data
TL;DR: Olaparib appeared to have a stronger association with MDS and AML than did other PARP inhibitors, and the risk of MDS was much higher than that of AML.
Genetic engineering of pigs for xenotransplantation to overcome immune rejection and physiological incompatibilities: The first clinical steps
TL;DR: In this article , a review of genetically modified pigs and comprehensively summarize the immunological mechanism of xenograft rejection and recent progress in preclinical and clinical studies is presented. And the authors conclude that both genetically engineered pig-based and technological breakthroughs in the biomedical field provide a promising foundation for pig-to-human xenotransplantation in the future.
Corrigendum: Pre-transplant CRP–albumin ratio as a biomarker in patients receiving haploidentical allogeneic hematopoietic transplantation: developing a novel DRCI-based nomogram
TL;DR: In this paper , the authors correct the article DOI: 10.3389/fimmu.2023.1128982, and present a new version of the article.
Corrigendum: Myelodysplastic syndrome/acute myeloid leukemia following the use of poly-ADP ribose polymerase inhibitors: A real-world analysis of postmarketing surveillance data
TL;DR: In this article , the authors correct the article DOI: 10.3389/fphar.2022.912256 and 10.3489/FphAR.
Development and validation of nomograms for predicting the risk probability of carbapenem resistance and 28-day all-cause mortality in gram-negative bacteremia among patients with hematological diseases
TL;DR: In this article , the least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate logistic regression analysis were conducted to select potential characteristic predictors of plotting nomograms.