Role for Carboxylic Acid Moiety in NSAIDs: Favoring the Binding at Site II of Bovine Serum Albumin.

The molecular structures of nonsteroidal anti-inflammatory drugs (NSAIDs) vary, but most contain a carboxylic acid functional group (RCOOH). This functional group is known to be related to the mechanism of cyclooxygenase inhibition and also causes side effects, such as gastrointestinal bleeding. This study proposes a new role for RCOOH in NSAIDs: facilitating the interaction at the binding site II of serum albumins. We used bovine serum albumin (BSA) as a model to investigate the interactions with ligands at site II. Using dansyl-proline (DP) as a fluorescent site II marker, we demonstrated that only negatively charged NSAIDs such as ibuprofen (IBP), naproxen (NPX), diflunisal (DFS), and ketoprofen (KTP) can efficiently displace DP from the albumin binding site. We confirmed the importance of RCOO by neutralizing IBP and NPX through esterification, which reduced the displacement of DP. The competition was also monitored by stopped-flow experiments. While IBP and NPX displaced DP in less than 1 s, the ester derivatives were ineffective. We also observed a higher affinity of negatively charged NSAIDs using DFS as a probe and ultrafiltration experiments. Molecular docking simulations showed an essential salt bridge between the positively charged residues Arg409 and Lys413 with RCOO-, consistent with the experimental findings. We performed a ligand dissociation pathway and corresponding energy analysis by applying molecular dynamics. The dissociation of NPX showed a higher free energy barrier than its ester. Apart from BSA, we conducted some experimental studies with human serum albumin, and similar results were obtained, suggesting a general effect for other mammalian serum albumins. Our findings support that the RCOOH moiety affects not only the mechanism of action and side effects but also the pharmacokinetics of NSAIDs.

A Survey on Deep-Learning-Based Diabetic Retinopathy Classification.

The number of people who suffer from diabetes in the world has been considerably increasing recently. It affects people of all ages. People who have had diabetes for a long time are affected by a condition called Diabetic Retinopathy (DR), which damages the eyes. Automatic detection using new technologies for early detection can help avoid complications such as the loss of vision. Currently, with the development of Artificial Intelligence (AI) techniques, especially Deep Learning (DL), DL-based methods are widely preferred for developing DR detection systems. For this purpose, this study surveyed the existing literature on diabetic retinopathy diagnoses from fundus images using deep learning and provides a brief description of the current DL techniques that are used by researchers in this field. After that, this study lists some of the commonly used datasets. This is followed by a performance comparison of these reviewed methods with respect to some commonly used metrics in computer vision tasks.

GAN-Based Approach for Diabetic Retinopathy Retinal Vasculature Segmentation.

Most diabetes patients develop a condition known as diabetic retinopathy after having diabetes for a prolonged period. Due to this ailment, damaged blood vessels may occur behind the retina, which can even progress to a stage of losing vision. Hence, doctors advise diabetes patients to screen their retinas regularly. Examining the fundus for this requires a long time and there are few ophthalmologists available to check the ever-increasing number of diabetes patients. To address this issue, several computer-aided automated systems are being developed with the help of many techniques like deep learning. Extracting the retinal vasculature is a significant step that aids in developing such systems. This paper presents a GAN-based model to perform retinal vasculature segmentation. The model achieves good results on the ARIA, DRIVE, and HRF datasets.