A Pacific Oyster-Derived Antioxidant, DHMBA, Protects Renal Tubular HK-2 Cells against Oxidative Stress via Reduction of Mitochondrial ROS Production and Fragmentation.

The kidney contains numerous mitochondria in proximal tubular cells that provide energy for tubular secretion and reabsorption. Mitochondrial injury and consequent excessive reactive oxygen species (ROS) production can cause tubular damage and play a major role in the pathogenesis of kidney diseases, including diabetic nephropathy. Accordingly, bioactive compounds that protect the renal tubular mitochondria from ROS are desirable. Here, we aimed to report 3,5-dihydroxy-4-methoxybenzyl alcohol (DHMBA), isolated from the Pacific oyster (Crassostrea gigas) as a potentially useful compound. In human renal tubular HK-2 cells, DHMBA significantly mitigated the cytotoxicity induced by the ROS inducer L-buthionine-(S, R)-sulfoximine (BSO). DHMBA reduced the mitochondrial ROS production and subsequently regulated mitochondrial homeostasis, including mitochondrial biogenesis, fusion/fission balance, and mitophagy; DHMBA also enhanced mitochondrial respiration in BSO-treated cells. These findings highlight the potential of DHMBA to protect renal tubular mitochondrial function against oxidative stress.

Automated Classification of Inherited Retinal Diseases in Optical Coherence Tomography Images Using Few-shot Learning.

Objective: To develop a few-shot learning (FSL) approach for classifying optical coherence tomography (OCT) images in patients with inherited retinal disorders (IRDs). Methods: In this study, an FSL model based on a student-teacher learning framework was designed to classify images. 2,317 images from 189 participants were included. Of these, 1,126 images revealed IRDs, 533 were normal samples, and 658 were control samples. Results: The FSL model achieved a total accuracy of 0.974-0.983, total sensitivity of 0.934-0.957, total specificity of 0.984-0.990, and total F1 score of 0.935-0.957, which were superior to the total accuracy of the baseline model of 0.943-0.954, total sensitivity of 0.866-0.886, total specificity of 0.962-0.971, and total F1 score of 0.859-0.885. The performance of most subclassifications also exhibited advantages. Moreover, the FSL model had a higher area under curves (AUC) of the receiver operating characteristic (ROC) curves in most subclassifications. Conclusion: This study demonstrates the effective use of the FSL model for the classification of OCT images from patients with IRDs, normal, and control participants with a smaller volume of data. The general principle and similar network architectures can also be applied to other retinal diseases with a low prevalence.