Biology subjects

Kameyama, M.

Publications and source records attributed to Kameyama, M..

3 recordsLinked to original sources

bioRxiv2019

Estimation of the Hemoglobin Glycation Rate Constant

AimIn a previous study, a method of obtaining mean erythrocyte age (MRBC) from HbA1c and average plasma glucose (AG) was proposed. However, the true value of the hemoglobin glycation constant (kg dL/mg/day), required for this model has yet to be well characterized. Another study also proposed a method of deriving MRBC from erythrocyte creatine (EC). Utilizing these formulae, this study aimed to determine a more accurate estimate of kg.\n\nMethods107 subjects including 31 patients with hemolytic anemia and 76 subjects without anemia were included in this study. EC and HbA1c data were analyzed, and MRBC using HbA1c, AG and the newly-derived constant, kg were compared to MRBC using traditional 51Cr in three patients whose data were taken from previous case studies.\n\nResultA value of 7.0 x 10-6 dL/mg/day was determined for kg. MRBC using HbA1c, AG and kg were found to no be significantly different (paired t-test, p = 0.45) to MRBC using traditional 51Cr.\n\nConclusionskg enables the estimation of MRBC from HbA1c and AG.

physiology↗

bioRxiv2019

A Novel Method for Calculating Mean Erythrocyte Age Using Erythrocyte Creatine

BackgroundsErythrocyte creatine (EC) decreases reflecting erythrocyte age.\n\nMethodsWe developed an EC model, which showed a bi- or mono-exponential relationship between mean erythrocyte age (MRBC) and EC. We reanalyzed the previously published data of 21 patients with hemolytic anemia which included EC and 51Cr half-life.\n\nResultsMRBC and loge EC showed excellent significant linearity (r = -0.9475, p < 0.001), showing that it can be treated as a mono-exponential relationship within the studied range (EC: 1.45 - 11.76 {micro}mol/g Hb). We established an equation to obtain MRBC (days) from EC ({micro}mol/g Hb), MRBC = -22.84 loge EC + 65.83.\n\nConclusionThis equation allows calculation of MRBC based on EC which has practical applications such as the diagnosis of anemia.

physiology↗

bioRxiv2019

Deep learning-based imaging classification identified cingulate island sign in dementia with Lewy bodies

The differentiation of dementia with Lewy bodies (DLB) from Alzheimers disease (AD) using brain perfusion single photon emission tomography is important but has been a challenge because these conditions have common features. The cingulate island sign (CIS) is the most recently identified specific feature of DLB for a differential diagnosis. The present study aimed to examine the usefulness of deep learning-based imaging classification for the diagnoses of DLB and AD. We also investigated whether CIS was focused by the deep convolutional neural network (CNN) during differentiation.\n\nBrain perfusion single photon emission tomography images were acquired from 80 patients each with DLB and with AD and 80 individuals with normal cognition (NL). The CNN was trained on brain surface perfusion images. Gradient-weighted class activation mapping (Grad-CAM) was applied to the CNN for visualization of the features that the trained CNN focused on.\n\nBinary classifications between DLB and NL, DLB and AD and AD and NL were 94.69%, 87.81% and 94.38% accurate, respectively. The CIS ratios closely correlated with softmax output scores for DLB-AD discrimination (DLB/AD scores). The Grad-CAM highlighted CIS in the DLB discrimination. Visualization of learning process by guided Grad-CAM revealed that CIS became more focused by the CNN as the training progressed. DLB/AD score was significantly associated with three core-features of DLB.\n\nDeep learning-based imaging classification was useful not only for objective and accurate differentiation of DLB from AD but also for predicting clinical features of DLB. The CIS was identified as a specific feature during DLB classification. The visualization of specific features and learning process could have important implications for the potential of deep learning to discover new imaging features.

neuroscience↗