An instrumental and computational pipeline for visualizing free viral particles

We are presenting the application of an optical and computational pipeline FAMOUS for revealing the presence of free viral particles named “virions”. The idea of such a protocol is to give rise to images of virions in their environment with a soft solution for recording the native image, contrary to the standard solution of imaging virions with electron microscopy (EM) for visualizing viral particles. The final aim of the current work is to observe free viral particles of SARS-CoV-2, the virions responsible for the worldwide pandemic of Covid-19. But such particles have diameters between 80 and 120 nm, a dimension smaller than the resolution limit of optical-only microscopy solutions. We have chosen to start with the biggest free virions, cytomegalovirus (CMV), a virus from the herpesvirus family also named “Human Herpes Virus 5”. Two kinds of cultures were involved: a fluorescent culture (BAD) and a label-free one (VHLE), both being collected from infected cell culture. VHLE virions were first observed after secondary immunostaining and concentrated with magnetic nanoparticles and then without labelling. The optical protocol rests on a standard solution of multiphoton microscopy combined with a computational strategy based on the point-spread-function (PSF) recordings, its mathematical modeling and the restauration of the image resting on the PSF model. A test with free viral particles of SARS-CoV-2 is led, delivering an optical visualization of the free-viral particles. The visualization of objects aggregates obtained in both situations confirm the relevance of the pipeline FAMOUS for imaging free virions. © 2022 SPIE.

A DEEP LEARNING APPROACH FOR IMPROVED SEGMENTATION OF LESIONS RELATED TO COVID-19 CHEST CT SCANS

The current coronavirus pandemic (COVID-19) became a world-wide threat, infecting more than 42 million people since its outbreak in early 2020. Recent studies show that analyzing chest CT scans plays an essential role in assessing disease progression and facilitates early diagnosis. Automatic lesion segmentation constitutes a useful tool to complement more traditional healthcare system strategies to address the COVID-19 crisis. We introduce MASC-Net, a novel deep neural network that automatically detects COVID-19 related infected lung regions from chest CT scans. The proposed architecture consists of a multi-input encoder-decoder that aggregates high-level features extracted with variable-size receptive fields.