ILDgenDB: integrated genetic knowledge resource for interstitial lung diseases (ILDs)

Recent Advances of Artificial Intelligence Applications in Interstitial Lung Diseases

Interstitial lung diseases (ILDs) comprise a rather heterogeneous group of diseases varying in pathophysiology, presentation, epidemiology, diagnosis, treatment and prognosis. Even though they have been recognized for several years, there are still areas of research debate. In the majority of ILDs, imaging modalities and especially high-resolution Computed Tomography (CT) scans have been the cornerstone in patient diagnostic approach and follow-up. The intricate nature of ILDs and the accompanying data have led to an increasing adoption of artificial intelligence (AI) techniques, primarily on imaging data but also in genetic data, spirometry and lung diffusion, among others. In this literature review, we describe the most prominent applications of AI in ILDs presented approximately within the last five years. We roughly stratify these studies in three categories, namely: (i) screening, (ii) diagnosis and classification, (iii) prognosis.

Network analysis of transcriptomics data for the prediction and prioritization of membrane-associated biomarkers for idiopathic pulmonary fibrosis (IPF) by bioinformatics approach

Idiopathic pulmonary fibrosis (IPF) is a rare yet crucial persistent lung disorder that actuates scarring of lung tissues, which makes breathing difficult. Smoking, environmental pollution, and certain viral infections could initiate lung scarring. However, the molecular mechanism involved in IPF remains elusive. To develop an efficient therapeutic arsenal against IPF, it is vital to understand the pathology and deviations in biochemical pathways that lead to disorder. In this study, we availed network analysis and other computational pipelines to delineate the prominent membrane proteins as diagnostic biomarkers and therapeutic targets for IPF. This study yielded a significant role of glycosaminoglycan binding, endothelin, and GABA-B receptor signaling pathway in IPF pathogenesis. Furthermore, ADCY8, CRH, FGB, GPR17, MCHR1, NMUR1, and SAA1 genes were found to be immensely involved with IPF, and the enrichment pathway analysis suggests that most of the pathways were corresponding to membrane transport and signal transduction functionalities. This analysis could help in better understanding the molecular mechanism behind IPF to develop an efficient therapeutic target or biomarkers for IPF.