Comparison between Statistical Models and Machine Learning Methods on Classification for Highly Imbalanced Multiclass Kidney Data.

This study aims to compare the classification performance of statistical models on highly imbalanced kidney data. The health examination cohort database provided by the National Health Insurance Service in Korea is utilized to build models with various machine learning methods. The glomerular filtration rate (GFR) is used to diagnose chronic kidney disease (CKD). It is calculated using the Modification of Diet in Renal Disease method and classified into five stages (1, 2, 3A and 3B, 4, and 5). Different CKD stages based on the estimated GFR are considered as six classes of the response variable. This study utilizes two representative generalized linear models for classification, namely, multinomial logistic regression (multinomial LR) and ordinal logistic regression (ordinal LR), as well as two machine learning models, namely, random forest (RF) and autoencoder (AE). The classification performance of the four models is compared in terms of accuracy, sensitivity, specificity, precision, and F1-Measure. To find the best model that classifies CKD stages correctly, the data are divided into a 10-fold dataset with the same rate for each CKD stage. Results indicate that RF and AE show better performance in accuracy than the multinomial and ordinal LR models when classifying the response variable. However, when a highly imbalanced dataset is modeled, the accuracy of the model performance can distort the actual performance. This occurs because accuracy is high even if a statistical model classifies a minority class into a majority class. To solve this problem in performance interpretation, we not only consider accuracy from the confusion matrix but also sensitivity, specificity, precision, and F-1 measure for each class. To present classification performance with a single value for each model, we calculate the macro-average and micro-weighted values for each model. We conclude that AE is the best model classifying CKD stages correctly for all performance indices.

Oral diabetes medication and risk of dementia in elderly patients with type 2 diabetes.

AIM: To examine the effect of oral diabetes medication on the risk of dementia in an elderly cohort with type 2 diabetes. METHODS: This was a population-based cohort study using the Korean National Health Insurance claims data from 2002 to 2013. Elderly subjects (60 years of age or older) with and without type 2 diabetes were included; patients with new-onset type 2 diabetes were further divided into the oral diabetes medication group and no-medication group. RESULTS: Among 278,290 patients with type 2 diabetes, 56,587 developed dementia (20.3%) over 11 years of follow-up. Type 2 diabetes was associated with a 1.69-fold increased risk of dementia (95% CI 1.66-1.72). Among patients with newly diagnosed type 2 diabetes, the risk of dementia was lower in the oral diabetes medication group than in the no-medication group (adjusted hazard ratio [aHR], 0.79; 95% CI 0.77-0.81). Lower risk of dementia was particularly noticeable in all of the combination therapy groups and especially lower in the combination therapy group treated with dipeptidyl peptidase 4 inhibitor (aHR 0.48, 95% CI 0.45-0.51). CONCLUSION: Overall, the use of oral diabetes medication in type 2 diabetes patients significantly decreased the risk of dementia.

Brassinosteroids facilitate xylem differentiation and wood formation in tomato.

MAIN CONCLUSION: BR signaling pathways facilitate xylem differentiation and wood formation by fine tuning SlBZR1/SlBZR2-mediated gene expression networks involved in plant secondary growth. Brassinosteroid (BR) signaling and BR crosstalk with diverse signaling cues are involved in the pleiotropic regulation of plant growth and development. Recent studies reported the critical roles of BR biosynthesis and signaling in vascular bundle development and plant secondary growth; however, the molecular bases of these roles are unclear. Here, we performed comparative physiological and anatomical analyses of shoot morphological growth in a cultivated wild-type tomato (Solanum lycopersicum cv. BGA) and a BR biosynthetic mutant [Micro Tom (MT)]. We observed that the canonical BR signaling pathway was essential for xylem differentiation and sequential wood formation by facilitating plant secondary growth. The gradual retardation of xylem development phenotypes during shoot vegetative growth in the BR-deficient MT tomato mutant recovered completely in response to exogenous BR treatment or genetic complementation of the BR biosynthetic DWARF (D) gene. By contrast, overexpression of the tomato Glycogen synthase kinase 3 (SlGSK3) or CRISPR-Cas9 (CR)-mediated knockout of the tomato Brassinosteroid-insensitive 1 (SlBRI1) impaired BR signaling and resulted in severely defective xylem differentiation and secondary growth. Genetic modulation of the transcriptional activity of the tomato Brassinazole-resistant 1/2 (SlBZR1/SlBZR2) confirmed the positive roles of BR signaling pathways for xylem differentiation and secondary growth. Our data indicate that BR signaling pathways directly promote xylem differentiation and wood formation by canonical BR-activated SlBZR1/SlBZR2.