Effective multi-class lungdisease classification using the hybridfeature engineering mechanism

The utilization of computational models in the field of medical image classification is an ongoing and unstoppable trend, driven by the pursuit of aiding medical professionals in achieving swift and precise diagnoses. Post COVID-19, many researchers are studying better classification and diagnosis of lung diseases particularly, as it was reported that one of the very few diseases greatly affecting human beings was related to lungs. This research study, as presented in the paper, introduces an advanced computer-assisted model that is specifically tailored for the classification of 13 lung diseases using deep learning techniques, with a focus on analyzing chest radiograph images. The work flows from data collection, image quality enhancement, feature extraction to a comparative classification performance analysis. For data collection, an open-source data set consisting of 112,000 chest X-Ray images was used. Since, the quality of the pictures was significant for the work, enhanced image quality is achieved through preprocessing techniques such as Otsu-based binary conversion, contrast limited adaptive histogram equalization-driven noise reduction, and Canny edge detection. Feature extraction incorporates connected regions, histogram of oriented gradients, gray-level co-occurrence matrix and Haar wavelet transformation, complemented by feature selection via regularized neighbourhood component analysis. The paper proposes an optimized hybrid model, improved Aquila optimization convolutional neural networks (CNN), which is a combination of optimized CNN and DENSENET121 with applied batch equalization, which provides novelty for the model compared with other similar works. The comparative evaluation of classification performance among CNN, DENSENET121 and the proposed hybrid model is also done to find the results. The findings highlight the proposed hybrid model's supremacy, boasting 97.00% accuracy, 94.00% precision, 96.00% sensitivity, 96.00% specificity and 95.00% F1-score. In the future, potential avenues encompass exploring explainable machine learning for discerning model decisions and optimizing performance through strategic model restructuring

Effects of regulatory T cells on macrophage inflammatory responses to Streptococcus pneumoniae

Streptococcus pneumoniae infection remains a major cause of morbidity and mortality worldwide. An effective inflammatory response is crucial for clearance of the pathogen, but excessive inflammation causes serious complications and host damage. Macrophages are the initiators of inflammation and exhibit plasticity in their responses ranging from highly pro-inflammatory to anti-inflammatory actions. Forkhead box p3 (Foxp3)-expressing Regulatory T (Treg) cells can dampen the inflammatory effects of various cell types. Co-culture of monocyte-derived macrophages (MDM) with Treg cells prior to or during S. pneumoniae infection and measurement of tumour necrosis factor α (TNFα), interleukin (IL)-6 and IL-1β in the supernatant was used to identify anti-inflammatory effects of Treg cells on MDMs. Treg cells potently reduced MDM pro-inflammatory cytokine production when co-cultured prior to infection, in a manner requiring direct Treg-MDM cell contact. This anti-inflammatory effect did not occur upon infection with Acinetobacter baumannii, indicating a degree of pathogen-specificity. Treg cells also reduced MDM responses to S. pneumoniae when added to the MDMs during infection, but to a lesser extent than pre-infection co-culture. A human intradermal S. pneumoniae challenge model was used to examine T cell recruitment, and a potential Treg population was identified. Using normal human lung sections, Foxp3+ and Foxp3- cells were identified by immunofluorescent (IF) staining, indicating the potential presence of lung-resident Treg cells. Overall, the data demonstrate that Treg cells can reduce macrophage pro-inflammatory cytokine production to S. pneumoniae. Provisional data indicate that Treg cells may recruit in response to S. pneumoniae in a human model by 48 hours post-challenge, and Foxp3+ cell are present in normal human lung

Mechanisms of myeloid cell recruitment and biomarker potential in interstitial lung diseases

Interstitial lung diseases (ILDs) are fibrotic disorders with chronic inflammation and fibrinogenesis leading to lung scaring and lung function decline. Ultimately, progressive pulmonary fibrosis results in altered pulmonary physiology, abnormal gas exchange, and organ failure. ILDs include known causes and idiopathic causes, as it is the case of idiopathic pulmonary fibrosis (IPF) and non-specific interstitial pneumonia (NSIP). The most detrimental type of ILD is IPF in which anti-fibrotic drugs (nintedanib and pirfenidone) only decrease disease progression. For other ILD types, corticoid treatment helps to decrease exacerbation. Currently, clinical trials are evaluating the applicability of anti-fibrotic drugs for treating non-IPF ILDs. Therefore, mechanistic insights and in-depth cell characterization during tissue injury and remodeling in ILD are of great interest in the respiratory medical field. Circulating immune cell populations have been suggested to play a critical role in ILDs. For instance, mononuclear phagocytes are involved in the initiation, repair and regeneration of pulmonary fibrosis. Moreover, the close interaction between circulating and lung tissue-resident immune cells is critical to contribute to tissue homeostasis or lead to disease. However, precise myeloid phenotypes (e.g. myeloid-derived suppressor cells and monocytes) and their mechanisms of recruitment in ILDs have not yet been explored. In the first results chapter of this thesis, myeloid-derived suppressor cells (MDSC) abundance and function were investigated for the first time in IPF patients. For that, peripheral blood of 170 patients including IPF, non-IPF ILD, chronic obstructive pulmonary diseases (COPD) and controls were collected to characterize and quantify MDSC by flow cytometry. Circulating MDSC in IPF and non-IPF ILD were increased when compared with control. Moreover, cross sectional and longitudinal analysis of the abundance of MDSC inversely correlated with pulmonary function test in IPF only. IPF patients with high number of MDSC showed downregulation of co-stimulatory T cells signals quantified by qRT-PCR. Furthermore, MDSC were able to suppress lymphocytes CD4+ and CD8+ cells proliferation in vitro. Last, CD33 CD11b double positive cells, suggestive of MDSC, were found in neighboring fibrotic niches of the IPF lungs. Taking together, these results show that MDSC are potential biomarker for IPF and are suppressing T cell responses. In the second results chapter, we aimed at analyzing monocyte phenotype and recruitment from the blood to the lung tissue in ILD. Importantly, CX3CR1 expression on immune cells has been demonstrated to increase fibrosis features. For that, flow cytometry analysis of circulating monocytes was performed in 105 subjects (83 ILD, and 22 controls). Monocyte localization and abundance in the lung was assessed by immunofluorescence and flow cytometry analysis. For receptor-ligand function and transmigration pattern, monocytes were isolated from blood and cultured either alone or with endothelial cells. Here, we showed that classical monocytes (CM) were increased, while non-classical monocytes (NCM) were decreased in ILD: NSIP, hypersensitivity pneumonitis (HP) and connective tissue disease associated with ILD (CTD-ILD) compared with controls. Monocytes abundance positively correlated with lung function. Fractalkine levels, the ligand of CX3CR1, were higher in lung tissue than in plasma in ILD and also co-localized with bronchial ciliated cells. Fractalkine enhanced endothelial transmigration of NCM in ILD only. Flow cytometry and immunofluorescence staining showed increased NCM in ILD. NCM-derived cells in the ILD lungs co-stained with CX3CR1, M2-like and phagocytic markers. In summary, we show that epithelial-derived fractalkine drives the migration of NCM-CX3CR1 which provides an interstitial scavenger and phagocytic myeloid cells population in fibrotic ILD lungs

Developing methods to understand intra-host evolution and the effect of antiviral drugs on RNA viruses

Viral infections are common and are particularly problematic in immunocompromised individuals. However, other than for HIV, Hepatitis B, Hepatitis C, Influenza, and more recently SARS-CoV-2, there have been few approved drugs available for treating viral infections. Instead, repurposed drugs are often used, especially at the beginning of the current pandemic, for treating SARS-CoV-2. It remains unclear how these repurposed drugs act on the viral population and whether the suppression of viral load we observe is attributed to the drug or the immune response or a combination of both. The research presented in this thesis primarily focuses on the study of two RNA viruses, SARS-CoV-2 and Norovirus. A mixture of viral load data and viral genomic data were analysed to understand the course of infection within individuals. First, we presented a meta-analysis on SARS-CoV-2 viral load dynamics where we investigated the changes of viral dynamics over time, with and without the presence of antiviral drugs. Then, we presented an evolutionary model used for reconstructing haplotypes in mixed infections. Finally, we demonstrated the use of viral deep sequencing to study the within-host evolution of RNA viruses. We identified mutagenic signatures and consensus level changes associated with antiviral treatments. We developed unique methods to analyse viral sequences which allow us to understand the within-host genomic variations and hence inform our understanding of the heterogeneous efficacy of a drug between patients. Overall, this thesis provides insights into how the efficacy of a drug can be evaluated by monitoring the within-host viral dynamics and evolution