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Biology subjects

Abbasi, R.

Publications and source records attributed to Abbasi, R..

2 recordsLinked to original sources

Mucosal and systemic responses to SARS-CoV-2 vaccination in infection naive and experienced individuals

With much of the world infected with or vaccinated against SARS-CoV-2, understanding the immune responses to the SARS-CoV-2 spike (S) protein in different situations is crucial to controlling the pandemic. We studied the clinical, systemic, mucosal, and cellular responses to two doses of SARS-CoV-2 mRNA vaccines in 62 individuals with and without prior SARS-CoV-2 exposure that were divided into three groups based on serostatus and/or degree of symptoms: Antibody negative, Asymptomatic, and Symptomatic. In the previously SARS-CoV-2-infected (SARS2-infected) Asymptomatic and Symptomatic groups, symptoms related to a recall response were elicited after the first vaccination. Anti-S trimer IgA and IgG levels peaked after 1st vaccination in the SARS2-infected groups, and were higher that the in the SARS2-naive group in the plasma and nasal samples at all time points. Neutralizing antibodies titers were also higher against the WA-1 and B.1.617.2 (Delta) variants of SARS-CoV-2 in the SARS2-infected compared to SARS2-naive vaccinees. After the first vaccination, differences in cellular immunity were not evident between groups, but the AIM+ CD4+ cell response correlated with durability of humoral immunity against the SARS-CoV-2 S protein. In those SARS2-infected, the number of vaccinations needed for protection, the durability, and need for boosters are unknown. However, the lingering differences between the SARS2-infected and SARS2-naive up to 10 months post-vaccination could explain the decreased reinfection rates in the SARS2-infected vaccinees recently reported and suggests that additional strategies (such as boosting of the SARS2-naive vaccinees) are needed to narrow the differences observed between these groups.

immunology↗

Capturing the songs of mice with an improved detection and classification method for ultrasonic vocalizations (BootSnap)

House mice communicate through ultrasonic vocalizations (USVs), which are above the range of human hearing (>20 kHz), and several automated methods have been developed for USV detection and classification. Here we evaluate their advantages and disadvantages in a full, systematic comparison. We compared the performance of four detection methods, DeepSqueak (DSQ), MUPET, USVSEG, and the Automatic Mouse Ultrasound Detector (A-MUD). Moreover, we compared these to human-based manual detection (considered as ground truth), and evaluated the inter-observer reliability. All four methods had comparable rates of detection failure, though A-MUD outperformed the others in terms of true positive rates for recordings with low or high signal-to-noise ratios. We also did a systematic comparison of existing classification algorithms, where we found the need to develop a new method for automating the classification of USVs using supervised classification, bootstrapping on Gammatone Spectrograms, and Convolutional Neural Networks algorithms with Snapshot ensemble learning (BootSnap). It successfully classified calls into 12 types, including a new class of false positives used for detection refinement. BootSnap provides enhanced performance compared to state-of-the-art tools, it has an improved generalizability, and it is freely available for scientific use.

neuroscience↗