RESUMEN
Purpose: This study aims to develop and assess an infrastructure using Python-based deep learning code for future diagnostic and management purposes related to dry eye disease (DED) utilizing smartphone images. Design: Cross-sectional study using data which was gathered in Vision Health Research Clinic. Participants: One thousand twenty-one eye images from 734 patients were included in this article that categorizes into 70% females and 30% males, with no sex and age limit. Methods: One specialist captured eye images using Samsung A71 (601 images) and iPhone 11 (420 images) cell phones with the flashlight on and direct gaze to the camera. These images include the area of only 1 eye (left/right). Main Outcome Measures: First, our specialist did 3 different segmentations for every eye image separately for 80% of the training data. This part contains eye, lower eyelid, and iris segmentation. In 20% of test data after automated cropping of the lower eyelid margin and upscaling by 8×, the appropriate tear meniscus height segmentation will be chosen and measured using a deep learning algorithm. Results: The model was trained on 80% of the data and 20% of the data used for validation from both phones with different resolutions. The dice coefficient of the trained model for validation data is 98.68%, and the accuracy of the overall model is 95.39%. Conclusions: It appears that this algorithm holds the potential to herald an evolution in the future of diagnosis and management of DED by homecare devices solely through smartphones. Financial Disclosures: The author(s) have no proprietary or commercial interest in any materials discussed in this article.
RESUMEN
5-Fluorouracil (5-FU), which is one of the most widely used chemotherapy drugs, has various side effects on the heart. Thymoquinone (TMQ), the main bioactive component of Nigella sativa, has antioxidant and protective effects against toxicity. In this study, we investigated the protective effect of thymoquinone against cardiotoxicity caused by 5-FU in vitro and in vivo models. H9C2 cells were exposed to 5-FU and TMQ, and cell viability was evaluated in their presence. Also, 25 male Wistar rats were divided into five control groups, 5-FU, 2.5, and 5 mg TMQ in nanoemulsion form (NTMQ) + 5-FU and 5 mg NTMQ. Cardiotoxicity was assessed through electrocardiography, cardiac enzymes, oxidative stress markers, and histopathology. 5-FU induced cytotoxicity in H9c2 cells, which improved dose-dependently with NTMQ cotreatment. 5-FU caused body weight loss, ECG changes (increased ST segment, prolonged QRS, and QTc), increased cardiac enzymes (aspartate aminotransferase [AST], creatine kinase-myocardial band [CK-MB], and lactate dehydrogenase [LDH]), oxidative stress (increased malondialdehyde, myeloperoxidase, nitric acid; decreased glutathione peroxidase enzyme activity), and histological damage such as necrosis, hyperemia, and tissue hyalinization in rats. NTMQ ameliorated these 5-FU-induced effects. Higher NTMQ dose showed greater protective effects. Thus, the results of our study indicate that NTMQ protects against 5-FU cardiotoxicity likely through antioxidant mechanisms. TMQ warrants further research as an adjuvant to alleviate 5-FU chemotherapy side effects.
