A bioinformatic analysis: Previous allergen exposure may support anti- SARS-CoV-2 immune response.

COVID-19, caused by infection with the SARS-CoV-2 has become a global health problem due to significant mortality rates; the exact pathophysiological mechanism remains uncertain. Articles reporting patient data are quite heterogeneous and have several limitations. Surviving patients develop a CD4 and CD8 T-cell response to the virus SARS-CoV-2 during COVID-19. Interestingly, pre-existing virus-reactive T-cells have been found in patients that were not infected before, suggesting some form of cross-reactivity or immunological mimicry. To better understand this phenomenon, we performed a bioinformatic study, which was aimed to identify antigenic structures that may explain the presence of such "reactive" T-cells, which may support or modulate the immune response to SARS-CoV-2 infections. Seven different common environmental allergen epitopes identical to the SARS-CoV-2 S-protein were identified that share affinity to 8 MHCI-specific epitope regions. Pollen showed the greatest similarity with the S protein epitope. In the epitope similarity analysis between the S protein and MHC-II / T helper epitopes, the highest similarity was determined for mites. When S-protein that stimulates B cells and identical epitope antigens are examined, the most common allergens were hornbeam and wheat. The high epitope similarity observed for the allergens examined and S protein epitopes suggest that these allergens may be a reason for pre-existing SARS-CoV-2 - reactive T-cells in previously non-infected subjects and such a previous exposure may affect the course of the disease in COVID-19 infection. It remains to be determined whether such a previous existence of SARS-CoV-2 reactive cells can support the clearance of the virus or if they, in contrast, may even aggravate the disease course. (Table 4, Ref 54).

Evaluation of the relationship between diabetic retinopathy and left atrial deformation parameters.

BACKGROUND: Left ventricular systolic dysfunction (LVSD) may develop without coronary artery disease, hypertension (HT), or valvular pathologies in patients with diabetes mellitus (DM), which is defined as diabetic cardiomyopathy (DCM) and its pathophysiology is still unclear. Diabetic retinopathy (DR) is a microvascular complication of DM, and patients with DR have increased risk for the development of heart failure (HF). Two-dimensional speckle tracking echocardiography (2D-STE) evaluates longitudinal deformation in left atrium (LA) myocardium and previous studies utilizing 2D-STE have revealed the detrimental effects of DM on LA functions. Although some studies have shown the association between DR and left ventricle (LV) systolic functions, as far as the researchers of this study investigated, there is no study evaluating the relationship between LA deformation parameters and DR. Hence, we aimed to investigate the relationship between the presence and the degree of DR and LA deformation parameters. RESULTS: LA deformation parameters were analyzed in terms of LA reservoir, conduit, and contractile functions according to the degree of DR. LA reservoir strain value was 14.2 ± 3.6 in normal retina group, 12.2 ± 4.1 in non-proliferative diabetic retinopathy (NPDR) group, and 13 ± 3.7 in proliferative diabetic retinopathy (PDR) group (P = 0.04). LA contractile strain was 15.9 ± 6.8 in normal retina group, 13.1 ± 47.4 in NPDR group, and 9.9 ± 4.7 in PDR group (P < 0.001). LA conduit strain was 30.1 ± 6.6 in normal retina group, 25.3 ± 6.5 in NPDR group, and 22.9 ± 4.9 in PDR group (P < 0.001). Proportional odds regression for association between clinical data, echocardiographic parameters, and LA contractile strain function showed that increasing creatinine (from 0.7 to 1.0; OR 0.71; 95% CI 0.51-0.99; P = 0.04), DR presence (OR 0.24; 95% CI 0.11-0.50; P = 0.001), and increasing left atrial volume index (LAVI) (from 33.5 to 52.6; OR 0.62; 95% CI 0.43-0.89; P = 0.01) were associated with decreasing LA function; however, other variables indicated no association. CONCLUSIONS: Our results showed the relationship between LA deformation parameters and DR, although microvascular involvement is not a certainly defined cardiovascular risk factor. Further prospective studies are needed to determine the clinical importance of DR presence and its degree for deformation parameters.

Machine Learning Can Predict Anti-VEGF Treatment Demand in a Treat-and-Extend Regimen for Patients with Neovascular AMD, DME, and RVO Associated Macular Edema.

PURPOSE: To assess the potential of machine learning to predict low and high treatment demand in real life in patients with neovascular age-related macular degeneration (nAMD), retinal vein occlusion (RVO), and diabetic macular edema (DME) treated according to a treat-and-extend regimen (TER). DESIGN: Retrospective cohort study. PARTICIPANTS: Three hundred seventy-seven eyes (340 patients) with nAMD and 333 eyes (285 patients) with RVO or DME treated with anti-vascular endothelial growth factor agents (VEGF) according to a predefined TER from 2014 through 2018. METHODS: Eyes were grouped by disease into low, moderate, and high treatment demands, defined by the average treatment interval (low, ≥10 weeks; high, ≤5 weeks; moderate, remaining eyes). Two random forest models were trained to predict the probability of the long-term treatment demand of a new patient. Both models use morphological features automatically extracted from the OCT volumes at baseline and after 2 consecutive visits, as well as patient demographic information. Evaluation of the models included a 10-fold cross-validation ensuring that no patient was present in both the training set (nAMD, approximately 339; RVO and DME, approximately 300) and test set (nAMD, approximately 38; RVO and DME, approximately 33). MAIN OUTCOME MEASURES: Mean area under the receiver operating characteristic curve (AUC) of both models; contribution to the prediction and statistical significance of the input features. RESULTS: Based on the first 3 visits, it was possible to predict low and high treatment demand in nAMD eyes and in RVO and DME eyes with similar accuracy. The distribution of low, high, and moderate demanders was 127, 42, and 208, respectively, for nAMD and 61, 50, and 222, respectively, for RVO and DME. The nAMD-trained models yielded mean AUCs of 0.79 and 0.79 over the 10-fold crossovers for low and high demand, respectively. Models for RVO and DME showed similar results, with a mean AUC of 0.76 and 0.78 for low and high demand, respectively. Even more importantly, this study revealed that it is possible to predict low demand reasonably well at the first visit, before the first injection. CONCLUSIONS: Machine learning classifiers can predict treatment demand and may assist in establishing patient-specific treatment plans in the near future.

Relation Between Platelet Reactivity Levels and Diabetic Retinopathy Stage in Patient with Type 2 Diabetes Mellitus by Using Multiplate Whole Blood Aggregometry.

Purpose: To test the hypothesis of a possible association between platelet reactivity and the severity of diabetic retinopathy using Multiplate whole blood aggregometry in type 2 diabetes mellitus patients. Methods: Of 157 patients were divided to three groups based on the severity of diabetic retinopathy (normal, non-proliferative and proliferative [ordinal among group 1-2-3]). Platelet reactivity was measured using arachidonic acid response to the ASPI and ADP platelet test. The association between DR stage and the degree of platelet reactivity (predictor variable) ASPI, ADP, systolic blood pressure, age, hypertension, body mass index (BMI), HbA1c, creatinine, Microalbumin, platelet, triglyceride/HDL and Hscrp variables were evaluated using ordinal logistic regression models (Model 1). The association between DR presence (outcome variable (group 1 vs group 2 and 3)) and the presence of variables was evaluated using binary logistic regression models (Model 2). Results: A comparison of the laboratory parameters of the three groups revealed that the ASPI, ADP, glucose and HbA1c values were significantly higher in Group-3 than Group-1. ASPI (odds-ratio OR: 1.044[1.021-1.09], p < .001], ADP (OR: 1.033[1.010-1.10], p: 0.002] and HbA1c (OR: 2.42(1.22, 4.94), p < .001) were demonstrated to be associated with stage of DR while the other variables were not. In binary logistic regression (model-2) analysis; ASPI (OR: 1.061[1.031-1.1], p < .001], ADP (OR: 1.03(1.01, 1.06), p: 0.045] and HbA1c (OR: 4.37 (1.67, 11.36)], p: 0.002) were associated with DR while the other variables were not. Conclusion: Herewith, we demonstrated that higher platelet reactivity measured by multiplate ASPI and ADP was significantly associated with stages of DR. Therefore, these measurements may be useful to predict the severity of DR in the clinical practice of physicians.

Using bioinformatic protein sequence similarity to investigate if SARS CoV-2 infection could cause an ocular autoimmune inflammatory reactions?

Although severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) infection have emerged globally, findings related to ocular involvement and reported cases are quite limited. Immune reactions against viral infections are closely related to viral and host proteins sequence similarity. Molecular Mimicry has been described for many different viruses; sequence similarities of viral and human tissue proteins may trigger autoimmune reactions after viral infections due to similarities between viral and human structures. With this study, we aimed to investigate the protein sequence similarity of SARS CoV-2 with retinal proteins and retinal pigment epithelium (RPE) surface proteins. Retinal proteins involved in autoimmune retinopathy and retinal pigment epithelium surface transport proteins were analyzed in order to infer their structural similarity to surface glycoprotein (S), nucleocapsid phosphoprotein (N), membrane glycoprotein (M), envelope protein (E), ORF1ab polyprotein (orf1ab) proteins of SARS CoV-2. Protein similarity comparisons, 3D protein structure prediction, T cell epitopes-MHC binding prediction, B cell epitopes-MHC binding prediction and the evaluation of the antigenicity of peptides assessments were performed. The protein sequence analysis was made using the Pairwise Sequence Alignment and the LALIGN program. 3D protein structure estimates were made using Swiss Model with default settings and analyzed with TM-align web server. T-cell epitope identification was performed using the Immune Epitope Database and Analysis (IEDB) resource Tepitool. B cell epitopes based on sequence characteristics of the antigen was performed using amino acid scales and HMMs with the BepiPred 2.0 web server. The predicted peptides/epitopes in terms of antigenicity were examined using the default settings with the VaxiJen v2.0 server. Analyses showed that, there is a meaningful similarities between 6 retinal pigment epithelium surface transport proteins (MRP-4, MRP-5, RFC1, SNAT7, TAUT and MATE) and the SARS CoV-2 E protein. Immunoreactive epitopic sites of these proteins which are similar to protein E epitope can create an immune stimulation on T cytotoxic and T helper cells and 6 of these 9 epitopic sites are also vaxiJen. These result imply that autoimmune cross-reaction is likely between the studied RPE proteins and SARS CoV-2 E protein. The structure of SARS CoV-2, its proteins and immunologic reactions against these proteins remain largely unknown. Understanding the structure of SARS CoV-2 proteins and demonstration of similarity with human proteins are crucial to predict an autoimmune response associated with immunity against host proteins and its clinical manifestations as well as possible adverse effects of vaccination.