The University of Tokyo · 의학
Yohei Hashimoto 교수의 연구실은 주로 신경생물학과 망막 질환의 분자 기전을 중심으로 연구를 진행하고 있습니다. 특히 칼슘 신호전달 분자인 칼시네우린과 CaM-kinase II 간의 상호작용 메커니즘을 규명하며 뇌 기능과 신경세포 기능 조절의 분자 기초를 탐구하고 있습니다. 또한, 고해상도 산란광학단층촬영(HD-OCT)과 시신경망막도자검사(HFA) 데이터를 기반으로 한 인공지능 기반 망막 질환 예측 모델 개발을 통해 진단의 정밀도를 향상시키는 데에도 기여하고 있습니다. 최근에는 출산 중 약물 사용이 태아 발달에 미치는 영향에 대한 임상적 분석도 수행하고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
The Ca2+/calmodulin (CaM)-dependent protein phosphatase calcineurin is rapidly phosphorylated (0.8 mol of 32PO4 per mol of 60-kDa subunit of calcineurin) by brain Ca2+/CaM-dependent protein kinase II (CaM-kinase II). This reaction requires the autophosphorylated, Ca2+-independent form of CaM-kinase II since Ca2+/CaM binding to calcineurin inhibits phosphorylation. However, the phosphorylation reaction does require Ca2+, presumably acting through the 19-kDa subunit of calcineurin. Calcineurin is
DL model showed considerably accurate prediction of HFA 10-2 VF from SD-OCT.
Propensity score analysis has been widely used in observational studies to make a causal inference. This study introduces three assumptions for causal inferences-conditional exchangeability, positivity, and consistency-and five steps for propensity score (PS) analysis-1) construct appropriate PS models, 2) check overlap in PS, 3) apply appropriate weighting (inverse probability of treatment weighting, standardized mortality ratio weighting, matching weights, and overlap weights) or matching meth
This model can reduce the burden of additional HFA 10-2 by making the best use of routinely performed HFA 24-2/30-2 and macular OCT.
Several types of IgG-dependent phagocytic stimuli independent of complement were investigated for their property to induce human polymorphonuclear neutrophil leucocyte (PMN) aggregation and adherence to human endothelial cells (EC) in culture. A Coulter counter method was employed for the detection of cell aggregation. Aggregated IgG, ovalbumin-anti-ovalbumin (OV anti-OV) immune complexes (both insoluble and soluble) and opsonized latex particles induced a significant degree of PMN aggregation w
IOP-lowering medications during the first trimester were not significantly associated with increase in CA, PB or LBW.
It is expected but unknown whether machine-learning models can outperform regression models, such as a logistic regression (LR) model, especially when the number and types of predictor variables increase in electronic health records (EHRs). We aimed to compare the predictive performance of gradient-boosted decision tree (GBDT), random forest (RF), deep neural network (DNN), and LR with the least absolute shrinkage and selection operator (LR-LASSO) for unplanned readmission. We used EHRs of patie
