Biology subjects

Yousif, M. D.

Publications and source records attributed to Yousif, M. D..

3 recordsLinked to original sources

bioRxiv2025

Design and development of a SARS and MERS Combination Vaccine

This work was conducted during the COVID-19 pandemic prior to the licensing of any vaccines against COVID-19. Although several COVID-19 vaccines are now commercially available, this research on the development of a combination Severe Acute Respiratory Syndrome-associated Coronavirus (SARS-CoV), SARS-coronavirus 2 (SARS-CoV-2), and Middle Eastern Respiratory Syndrome (MERS) is still relevant and shows how a combination vaccine can be designed, produced and rapidly tested in the laboratory. We present the development of a combination vaccine designed to provide immunity against Severe Acute Respiratory Syndrome-associated Coronavirus (SARS-CoV), SARS-coronavirus 2 (SARS-CoV-2), and Middle Eastern Respiratory Syndrome (MERS). The primary objective of this vaccine design is twofold: to mitigate the burden of coronavirus and to address the specific vulnerability of regions prone to recurrent MERS outbreaks. Our combination vaccine incorporates antigenic components from zoonotic sources, specifically SARS-CoV, SARS-CoV-2, and MERS. We assess the impact of combining different Spike proteins S1 subunit antigens, due to its recognised immunogenic potential, and adjuvants on serum antibody titres, virus neutralizing capabilities, and inter-antigen immune responses. We report a robust and broad antibody response against SARS-CoV-2 and related coronaviruses, which was amplified by different adjuvant formulations, including alum, MPLA, CpG, and Squalene-in-Water Emulsion.

immunology↗

bioRxiv2023

Novel insights into hippocampal perfusion using high-resolution, multi-modal 7T MRI

We present a comprehensive study on the non-invasive measurement of hippocampal perfusion. Using high-resolution 7 Tesla arterial spin labelling data, we generated robust perfusion maps and observed significant variations in perfusion among hippocampal subfields, with CA1 exhibiting the lowest perfusion levels. Notably, these perfusion differences were robust and detectable even within five minutes and just fifty perfusion-weighted images per subject. To understand the underlying factors, we examined the influence of image quality metrics, various tissue microstructure and morphometry properties, macrovasculature and cytoarchitecture. We observed higher perfusion in regions located closer to arteries, demonstrating the influence of vascular proximity on hippocampal perfusion. Moreover, ex vivo cytoarchitectonic features based on neuronal density differences appeared to correlate stronger with hippocampal perfusion than morphometric measures like gray matter thickness. These findings emphasize the interplay between microvasculature, macrovasculature, and metabolic demand in shaping hippocampal perfusion. Our study expands the current understanding of hippocampal physiology and its relevance to neurological disorders. By providing in vivo evidence of perfusion differences between hippocampal subfields, our findings have implications for diagnosis and potential therapeutic interventions. In conclusion, our study provides a valuable resource for extensively characterising hippocampal perfusion.

neuroscience↗

bioRxiv2021

HippUnfold: Automated hippocampal unfolding, morphometry, and subfield segmentation

Like neocortical structures, the archicortical hippocampus differs in its folding patterns across individuals. Here, we present an automated and robust BIDS-App, HippUnfold, for defining and indexing individual-specific hippocampal folding in MRI, analogous to popular tools used in neocortical reconstruction. Such tailoring is critical for inter-individual alignment, with topology serving as the basis for homology. This topological framework enables qualitatively new analyses of morphological and laminar structure in the hippocampus or its subfields. It is critical for refining current neuroimaging analyses at a meso- as well as micro-scale. HippUnfold uses state-of-the-art deep learning combined with previously developed topological constraints to generate uniquely folded surfaces to fit a given subjects hippocampal conformation. It is designed to work with commonly employed sub-millimetric MRI acquisitions, with possible extension to microscopic resolution. In this paper we describe the power of HippUnfold in feature extraction, and highlight its unique value compared to several extant hippocampal subfield analysis methods.

neuroscience↗