Identification and characterization of a hydrophobin Vmh3 from Pleurotus ostreatus.

Hydrophobins (HPs) are relatively small surface-active proteins of fungal origin. Being an industrially important protein, isolation of new molecules from GRAS (Generally Regarded as Safe) strains like mushrooms is the need of the time. In the present work, hydrophobin Vmh3-1 is isolated, purified, and identified from a culture broth and vegetative mycelia of Pleurotus ostreatus grown in a Potato dextrose broth (PDB) in static culture conditions. Purified proteins from the broth and the cell wall showed bands of 11 kDa and 17 kDa when analyzed on SDS-PAGE. Hydrophobin Vmh3-1 was identified in purified protein samples by the Orbitrap-HR-LC-MS/MS analysis with a maximum of 66% sequence coverage. The amphipathic nature of the protein was revealed by an increase in the water contact angle (WCA) of the hydrophilic surface of glass by 87% as well as a decrease in the WCA of the hydrophobic surface of Teflon by 19%. The emulsification property was tested with food-grade oils and Hexane. A maximum activity (EI 24) of 87.64% was recorded for Sunflower oil. In CD (Circular dichroism) spectra, Vmh3-1 showed the typical spectra of hydrophobin with a dominance of ß-sheets (51%) in the secondary structure and a minimum percentage of the α-helix (2%). The protein did not show a self-aggregating property on vigorous shaking making it suitable for numerous industrial applications. The identification of Vmh3-1 with detailed amino acid sequencing and the characterization of the protein to evaluate its potential in surface modifications for various industrial applications is demonstrated herein for the first time.

Repurposing pharmaceutical excipients as an antiviral agent against SARS-CoV-2.

The limited time indorsed to face the COVID-19 emergency and large number of deaths across the globe, poses an unrelenting challenge to find apt therapeutic approaches. However, lead candidate selection to phase III trials of new chemical entity is a time-consuming procedure, and not feasible in pandemic, such as the one we are facing. Drug repositioning, an exploration of existing drug for new therapeutic use, could be an effective alternative as it allows fast-track estimation in phase II-III trials, or even forthright compassionate use. Although, drugs repurposed for COVID-19 pandemic are commercially available, yet the evaluation of their safety and efficacy is tiresome and painstaking. In absence of any specific treatment the easy alternatives such as over the counter products, phytotherapies and home remedies have been largely adopted for prophylaxis and therapy as well. In recent years, it has been demonstrated that several pharmaceutical excipients possess antiviral properties making them prospective candidates against SARS-CoV-2. This review highlights the mechanism of action of various antiviral excipients and their propensity to act against SARs-CoV2. Though, repurposing of pharmaceutical excipients against COVID-19 has the edge over therapeutic agents in terms of safety, cost and fast-track approval trial burdened, this hypothesis needs to be experimentally verified for COVID-19 patients.

Deep Learning Classifiers for Automated Detection of Gonioscopic Angle Closure Based on Anterior Segment OCT Images.

PURPOSE: To develop and test deep learning classifiers that detect gonioscopic angle closure and primary angle closure disease (PACD) based on fully automated analysis of anterior segment OCT (AS-OCT) images. METHODS: Subjects were recruited as part of the Chinese-American Eye Study (CHES), a population-based study of Chinese Americans in Los Angeles, California, USA. Each subject underwent a complete ocular examination including gonioscopy and AS-OCT imaging in each quadrant of the anterior chamber angle (ACA). Deep learning methods were used to develop 3 competing multi-class convolutional neural network (CNN) classifiers for modified Shaffer grades 0, 1, 2, 3, and 4. Binary probabilities for closed (grades 0 and 1) and open (grades 2, 3, and 4) angles were calculated by summing over the corresponding grades. Classifier performance was evaluated by 5-fold cross-validation and on an independent test dataset. Outcome measures included area under the receiver operating characteristic curve (AUC) for detecting gonioscopic angle closure and PACD, defined as either 2 or 3 quadrants of gonioscopic angle closure per eye. RESULTS: A total of 4036 AS-OCT images with corresponding gonioscopy grades (1943 open, 2093 closed) were obtained from 791 CHES subjects. Three competing CNN classifiers were developed with a cross-validation dataset of 3396 images (1632 open, 1764 closed) from 664 subjects. The remaining 640 images (311 open, 329 closed) from 127 subjects were segregated into a test dataset. The best-performing classifier was developed by applying transfer learning to the ResNet-18 architecture. For detecting gonioscopic angle closure, this classifier achieved an AUC of 0.933 (95% confidence interval, 0.925-0.941) on the cross-validation dataset and 0.928 on the test dataset. For detecting PACD based on 2- and 3-quadrant definitions, the ResNet-18 classifier achieved AUCs of 0.964 and 0.952, respectively, on the test dataset. CONCLUSION: Deep learning classifiers effectively detect gonioscopic angle closure and PACD based on automated analysis of AS-OCT images. These methods could be used to automate clinical evaluations of the ACA and improve access to eye care in high-risk populations.