Nrf2 Is Involved in Hyperglycemia-induced Abnormal Lung Development through both Antioxidation-Dependent and Antioxidation-Independent Activation.

Accumulating evidence has shown that hyperglycemia during pregnancy negatively affects lung development. However, the pathological mechanism of lung dysplasia caused by hyperglycemia remains unclear. In this study, we demonstrated the phenotypes of the impaired lung epithelial cell differentiation of mouse lungs in pregestational diabetes mellitus (PGDM) and gestational diabetes mellitus (GDM), and increased levels of oxidative stress and activation of the nuclear factor erythroid 2-related factor 2 (Nrf2) signaling pathways occurred. Nrf2 deficiency during pregnancy led to the aforementioned similar and aggravated phenotypes of the poor saccular process as in diabetes, implying the Nrf2 signaling pathway played a very important role in both physiological and pathological conditions. Based on RNA sequencing and luciferase reporter gene analysis, we revealed that Nrf2 could regulate Wnt signaling by targeting Ctnnd2. In summary, we revealed the pathological mechanism of how diabetes affected late lung development during embryogenesis, especially elucidating the bilateral roles of Nrf2-mediated oxidative stress responses and Wnt signaling. This finding also indicated that Nrf2 could potentially be used in preventing or treating pulmonary anomalies induced by hyperglycemia during pregnancy.

Adaptive Fusion Based Method for Imbalanced Data Classification.

The imbalance problem is widespread in real-world applications. When training a classifier on the imbalance datasets, the classifier is hard to learn an appropriate decision boundary, which causes unsatisfying classification performance. To deal with the imbalance problem, various ensemble algorithms are proposed. However, conventional ensemble algorithms do not consider exploring an effective feature space to further improve the performance. In addition, they treat the base classifiers equally and ignore the different contributions of each base classifier to the ensemble result. In order to address these problems, we propose a novel ensemble algorithm that combines effective data transformation and an adaptive weighted voting scheme. First, we utilize modified metric learning to obtain an effective feature space based on imbalanced data. Next, the base classifiers are assigned different weights adaptively. The experiments on multiple imbalanced datasets, including images and biomedical datasets verify the superiority of our proposed ensemble algorithm.