Rodents have brownish‐yellow incisors whose colour represents their iron content. Iron is deposited into the mature enamel by ameloblasts that outline enamel surface of the teeth. Nrf2 is a basic region‐leucine zipper type transcription factor that regulates expression of a range of cytoprotective genes in response to oxidative and xenobiotic stresses. We found that genetically engineered Nrf2‐deficient mice show decolourization of the incisors. While incisors of wild‐type mice were brownish yellow, incisors of Nrf2‐deficient mice were greyish white in colour. Micro X‐ray imaging analysis revealed that the iron content in Nrf2‐deficient mouse incisors were significantly decreased compared to that of wild‐type mice. We found that iron was aberrantly deposited in the papillary layer cells of enamel organ in Nrf2‐deficient mouse, suggesting that the iron transport from blood vessels to ameloblasts was disturbed. We also found that ameloblasts of Nrf2‐null mouse show degenerative atrophy at the late maturation stage, which gives rise to the loss of iron deposition to the surface of mature enamel. Our results thus demonstrate that the enamel organ of Nrf2‐deficient mouse has a reduced iron transport capacity, which results in both the enamel cell degeneration and disturbance of iron deposition on to the enamel surface.
Ovarian cancer is a leading cause of deaths among gynecological cancers, and a method to detect early-stage epithelial ovarian cancer (EOC) is urgently needed. We aimed to develop an artificial intelligence (AI)-based comprehensive serum glycopeptide spectra analysis (CSGSA-AI) method in combination with convolutional neural network (CNN) to detect aberrant glycans in serum samples of patients with EOC. We converted serum glycopeptide expression patterns into two-dimensional (2D) barcodes to let CNN learn and distinguish between EOC and non-EOC. CNN was trained using 60% samples and validated using 40% samples. We observed that principal component analysis-based alignment of glycopeptides to generate 2D barcodes significantly increased the diagnostic accuracy (88%) of the method. When CNN was trained with 2D barcodes colored on the basis of serum levels of CA125 and HE4, a diagnostic accuracy of 95% was achieved. We believe that this simple and low-cost method will increase the detection of EOC.
