Association of the p85 regulatory subunit of phosphatidylinositol 3- kinase with an essential erythropoietin receptor subdomain

Using an active, HAI epitope-tagged form of the murine erythropoietin (EPO) receptor and via direct coimmunoprecipitation, the p85 regulatory subunit of phosphatidyl inositol-3 kinase (p85/PI3-K) is shown to associate with the EPO receptor in transfected FDC-P1 cell lines. Coimmunoprecipitation of p85 with epitope-tagged EPO receptors was observed initially in FDC-HER cells labeled metabolically with [32P]orthophosphate, and association of these factors was confirmed by Western analyses of receptor immunoprecipitates using p85 antiserum. Interestingly, this association occurred in the absence of ligand, and exposure of FDC-HER cells to EPO did not detectably affect levels of receptor-associated p85 or overall levels of p85 phosphorylation. However, EPO was observed to stimulated the rapid formation of phosphatidylinositol 32P-phosphate in FDC-HER and FDC-ER cells. Through baculovirus-mediated expression of epitope-tagged EPO receptor forms in SF9 cells, domains for p85 association were mapped. Analyses of receptor forms with cytosolic truncations and deletions delineated a candidate subdomain for p85 binding to an essential extended box-2 region (P329-E374; including a putative motif for SH2 binding, Y343LVL). These findings extend a mechanistic alignment between the EPO receptor and protein tyrosine kinase-encoding receptors that likewise activate PI3-K, and expand the importance of further defining pathways to PI3-K activation.     

Narrative review of artificial intelligence in diabetic macular edema: Diagnosis and predicting treatment response using optical coherence tomography

Diabetic macular edema (DME), being a frequent manifestation of DR, disrupts the retinal symmetry. This event is particularly triggered by vascular endothelial growth factors (VEGF). Intravitreal injections of anti-VEGFs have been the most practiced treatment but an expensive option. A major challenge associated with this treatment is determining an optimal treatment regimen and differentiating patients who do not respond to anti-VEGF. As it has a significant burden for both the patient and the health care providers if the patient is not responding, any clinically acceptable method to predict the treatment outcomes holds huge value in the efficient management of DME. In such situations, artificial intelligence (AI) or machine learning (ML)-based algorithms come useful as they can analyze past clinical details of the patients and help clinicians to predict the patient''s response to an anti-VEGF agent. The work presented here attempts to review the literature that is available from the peer research community to discuss solutions provided by AI/ML methodologies to tackle challenges in DME management. Lastly, a possibility for using two different types of data has been proposed, which is believed to be the key differentiators as compared to the similar and recent contributions from the peer research community.