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Two-step generation of induced pluripotent stem cells from mouse fibroblasts using Id3 and Oct4

Journal of Molecular Cell Biology 2012年第1期4卷 59-62页

作者： Jai-Hee Moon June Seok Heo Suhyun Kwon Jihyun Kim Jihye Hwang Phil Jun Kang Aeree Kim Hyun Ok Kim Kwang youn Whang Byung Sun Yoon seungkwon you Laboratory of Cell Function Regulation College of Life Sciences and BiotechnologyKorea UniversitySeoulRepublic of Korea Cell Therapy Center College of MedicineYonsei UniversitySeoulRepublic of Korea Department of Pathology College of MedicineKorea University Guro HospitalSeoulRepublic of Korea Division of Biotechnology College of Life Sciences and BiotechnologyKorea UniversitySeoulRepublic of Korea

Dear Editor,Somatic cells can be reprogrammed into pluripotent stem cells,called induced pluripotent stem cells(iPSCs),by defined transcription factors(Takahashi et al.,2006).The reprogramming of somatic cells may be a continuous stochastic process in which nearly all somatic donor cells have the ability to give rise to iPSCs with continuous passaging and the expression of defined factors(Hanna et al.,2009).However,the reprogramming process can be divided into discrete stages,including the generation of dedifferentiated pre-pluripotent cells followed by authentic pluripotent cells(Silva et al.,2009).

关键词： pluripotent programming

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Establishment of a prediction tool for ocular trauma patients with machine learning algorithm

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International Journal of Ophthalmology(English edition) 2021年第12期14卷 1941-1949页

作者： seungkwon Choi Jungyul Park Sungwho Park Iksoo Byon Hee-young Choi Department of Ophthalmology Pusan National University HospitalBusan 49241Republic of Korea Biomedical Research Institute of Pusan National University Hospital Busan 49241Republic of Korea Department of Ophthalmology School of MedicinePusan National UniversityBusan 49241Republic of Korea

AIM:To predict final visual acuity and analyze significant factors influencing open globe injury ***:Prediction models were built using a supervised classification algorithm from Microsoft Azure Machine Learning *** best algorithm was selected to analyze the predicted final visual *** retrospectively reviewed the data of 171 patients with open globe injury who visited the Pusan National University Hospital between January 2010 and July *** then applied cross-validation,the permutation feature importance method,and the synthetic minority over-sampling technique to enhance tool ***:The two-class boosted decision tree model showed the best predictive *** accuracy,precision,recall,F1 score,and area under the receiver operating characteristic curve were 0.925,0.962,0.833,0.893,and 0.971,*** increase the efficiency and efficacy of the prognostic tool,the top 14 features were finally selected using the permutation feature importance method:(listed in the order of importance)retinal detachment,location of laceration,initial visual acuity,iris damage,surgeon,past history,size of the scleral laceration,vitreous hemorrhage,trauma characteristics,age,corneal injury,primary diagnosis,wound location,and lid ***:Here we devise a highly accurate model to predict the final visual acuity of patients with open globe *** tool is useful and easily accessible to doctors and patients,reducing the socioeconomic *** further multicenter verification using larger datasets and external validation,we expect this model to become useful worldwide.

关键词： machine learning ocular trauma open globe injury predictive model vision preservation

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