SUMMARYThe aim of this study was to determine the expression levels of p53 and TATA binding protein (TBP) and the presence of autoantibodies to these antigens in Asian Indian patients with systemic sclerosis (SSc), overlap syndromes (OS) and systemic lupus erythematosus (SLE). Fifty patients with SSc, 20 with OS, including mixed connective tissue diseases (MCTD), 20 with SLE, 10 disease controls (DC) and 25 controls (C) were studied. The over-expression of p53 and TBP antigen was determined quantitatively by sandwich enzyme-linked immunosorbent assay (ELISA), varies between four-and sevenfold higher in patients with SSc, OS and SLE, in comparison to DC and C. The expressed protein antigens were not present as free antigens but as immune-complexes. Autoantibodies to p53 were detected by ELISA in 78% subjects with SSc, 100% with OS and 80% with SLE. Autoantibodies to TBP were observed in 28% patients with SSc, 25% with OS and 15% with SLE. In comparison to healthy controls, the titre of antibodies to p53 was significantly higher in patients with SSc ( P = 0·00001) than the patients with OS ( P = 0·00279) and SLE ( P = 0·00289), whereas the titre of antibodies to TBP was higher in patients with OS ( P = 0·00185) than the SLE ( P = 0·00673) and the SSc ( P = 0·00986) patients. Autoantibodies to p53 and TBP were detected in all these patients and the levels of these two autoantibodies showed weak negative correlation with each other. We propose that the over-expression of these antigens might be due to hyperactive regulatory regions in the p53 and TBP gene.

No abstract

No abstract

Background Network medicine is an emerging area of research that focuses on delving into the molecular complexity of the disease, leading to the discovery of network biomarkers and therapeutic target discovery. Amyotrophic lateral sclerosis (ALS) is a complicated rare disease with unknown pathogenesis and no available treatment. In ALS, network properties appear to be potential biomarkers that can be beneficial in disease-related applications when explored independently or in tandem with machine learning (ML) techniques. Objective This systematic literature review explores recent trends in network medicine and implementations of network-based ML algorithms in ALS. We aim to provide an overview of the identified primary studies and gather details on identifying the potential biomarkers and delineated pathways. Methods The current study consists of searching for and investigating primary studies from PubMed and Dimensions.ai, published between 2018 and 2022 that reported network medicine perspectives and the coupling of ML techniques. Each abstract and full-text study was individually evaluated, and the relevant studies were finally included in the review for discussion once they met the inclusion and exclusion criteria. Results We identified 109 eligible publications from primary studies representing this systematic review. The data coalesced into two themes: application of network science to identify disease modules and promising biomarkers in ALS, along with network-based ML approaches. Conclusion This systematic review gives an overview of the network medicine approaches and implementations of network-based ML algorithms in ALS to determine new disease genes, and identify critical pathways and therapeutic target discovery for personalized treatment.