Yohei Hashimoto

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