Biology subjects

Bouziane, A.

Publications and source records attributed to Bouziane, A..

3 recordsLinked to original sources

bioRxiv2025

Strain-dependent differences in the capacity of Peste des Petits Ruminants virus to infect antigen presenting cells

The ability of morbilliviruses to infect antigen-presenting cells (APCs) plays a major role in infection outcome, because these cells transport peste des petits ruminants virus (PPRV) from the respiratory tract to the lymph nodes before spreading to lymphoid and epithelial tissues. This study used two cell models, -- monocyte-derived macrophages (MoMs) and monocyte-derived dendritic cells (MoDCs), -- from three host species (goat, sheep and cattle) to evaluate the differential ability of APCs to be infected by two strains of PPRV with contrasting virulence in goats. Infection was analysed using flow cytometry, infectious titers (TCID/mL), RT-qPCR and immunofluorescence. Goat and sheep MoDCs proved highly permissive to both strains, with viral titers at 48 hpi in the range of 10-10 TCID/mL and a high percentage of infected cells. Cattle MoDCs were weakly infected and had no detectable viral titer, as expected for this dead-end host. In comparison, MoMs showed reduced permissiveness to PPRV in goats but not in sheep. Post-infection supernatants in goat MoMs infected with the low-virulent strain showed low or undetectable TCID/mL, despite persistence of detectable viral genome up to 96 hpi. Immunofluorescence revealed double-stranded RNA but no nucleoprotein of the low-virulent strain in goat MoMs, suggesting a partial engagement of the viral cycle, resulting in a restricted or semi-permissive infection. Overall, these results suggest that this combined in vitro model of MoDC and MoM infections would be useful in anticipating host susceptibility and PPRV strain virulence, providing complementary tools for PPR control efforts. IMPORTANCEPeste des petits ruminants (PPR) disease affects goats, sheep and multiple other hosts across the globe, with major impacts on economies, food security and biodiversity. The sensitivity of host species to PPRV infection varies considerably, with clinical manifestations and disease progression being shaped by the viral strain, host species and breed infected. In vitro models focusing on immune cells, which are the primary target of PPRV could provide a relevant tool for studying the mechanisms of viral adaptation to the host and cellular antiviral responses, while contributing to surveillance and control strategies. The results obtained here suggest that a dual-cell model focusing on macrophages and dendritic cells could be useful for pre-screening studies aimed at assessing the susceptibility of a given host species to a PPRV strain and estimating its relative virulence. Testing this in vitro approach in additional host species and a wider range of strains is required to confirm its relevance.

microbiology↗

bioRxiv2024

Micro to macro scale anatomical analysis of the human hippocampal arteries with synchrotron Hierarchical Phase-Contrast Tomography

PurposeTo date, no non-invasive imaging modality has been employed to profile the structural intricacies of the hippocampal arterial microvasculature in humans. We hypothesised that synchrotron-based imaging of the human hippocampus would enable precise characterisation of the arterial microvasculature. MethodsTwo preserved human brains from, a 69-year-old female and a 63-year-old male body donors were imaged using hierarchical phase-contrast tomography (HiP-CT) with synchrotron radiation at multiple voxel resolutions from 25.08 m down to 2.45 m. Subsequent manual and semi-automatic artery segmentation were performed followed by morphometric analyses. These data were compared to published data from alternative methodologies. ResultsHiP-CT made it possible to segment in context the arterial architecture of the human hippocampus. Our analysis identified anterior, medial and posterior hippocampal arteries arising from the P2 segment of the posterior cerebral artery on the image slices. We mapped arterial branches with external diameters greater than 50 m in the hippocampal region. We visualised vascular asymmetry and quantified arterial structures with diameters as small as 7 m. ConclusionsThrough the application of HiP-CT, we have provided the first imaging visualisation and quantification of the arterial system of the human hippocampus at high resolution in the context of whole brain imaging. Our results bridge the gap between anatomical and histological scales.

neuroscience↗

bioRxiv2022

DeepLUAD: An efficient approach for lung adenocarcinoma pattern classification

Histopathological analysis of whole-slide images is the gold standard technique for diagnosis of lung cancer and classifying it into types and subtypes by specialized pathologists. This labor-based approach is time and effort consuming, which led to development of automatic approaches to assist in reducing the time and effort. Deep learning is a supervised classification approach that is well adapted for automatic classification of histopathological images. We aimed to develop a deep learning-based approach for lung adenocarcinoma pattern classification and generalize the proposed approach to the classification of the major non-small cell lung cancer types. Three publicly available datasets were used in this study. A deep learning approach for histopathological image analysis using convolutional neural networks was developed and incorporated into automatic pipelines to accurately classify the predominant patterns on the whole-slide images level and non-small cell lung cancer types on patch-level. The models were evaluated using the confusion matrix to perform an error analysis and the classification report to compute F1-score, recall and precision. As results, the three models have shown an excellent performance with best combination of hyper-parameters for training models. First and second models predicted adenocarcinoma predominant patterns on two different datasets with an accuracy, respectively, of 96.15% and 89.51%. The third model has exceeded an accuracy of 99.72% in classifying major non-small cell lung cancer types. The proposed deep learning-based lung cancer classification approach can be used to assist pathologists in identifying of lung adenocarcinomas patterns.

bioinformatics↗