Biology subjects

Priyanka, S.

Publications and source records attributed to Priyanka, S..

2 recordsLinked to original sources

bioRxiv2024

The structural basis for the selective antagonism of soluble TNF-alpha by shark variable new antigen receptors

The pro-inflammatory cytokine tumor necrosis factor-alpha (TNF)- is synthesized as transmembrane TNF- (tmTNF-) where proteolytic processing releases soluble TNF- (sTNF-). tmTNF- can act as either a ligand by activating TNF receptors, or a receptor that transmits outside-to-inside signals (reverse signalling) after binding to native receptors. All TNF- therapies bind tmTNF- and induce reverse signalling which can result in immunosuppression leading to infection. We present crystal structures of two anti-TNF- Variable New Antigen Receptors (VNAR) in complex with sTNF- via two distinct epitopes. The VNAR-D1 recognized an epitope that selectively engaged sTNF- while VNAR-C4 bound an epitope that overlapped with other biologic therapies. In activated CD4+ T cells, our VNARs did not bind tmTNF- in contrast to commercially available therapies that demonstrated induction of reverse signalling. Our findings suggest that neutralisation through a unique mechanism may lead to anti-TNF- agents with an improved safety profile that will benefit high-risk patients.

pharmacology and toxicology↗

bioRxiv2018

Weakly supervised classification of rare aortic valve malformations using unlabeled cardiac MRI sequences

Biomedical repositories such as the UK Biobank provide increasing access to prospectively collected cardiac imaging, however these data are unlabeled which creates barriers to their use in supervised machine learning. We develop a weakly supervised deep learning model for classification of aortic valve malformations using up to 4,000 unlabeled cardiac MRI sequences. Instead of requiring highly curated training data, weak supervision relies on noisy heuristics defined by domain experts to programmatically generate large-scale, imperfect training labels. For aortic valve classification, models trained with imperfect labels substantially outperform a supervised model trained on hand-labeled MRIs. In an orthogonal validation experiment using health outcomes data, our model identifies individuals with a 1.8-fold increase in risk of a major adverse cardiac event. This work formalizes a learning baseline for aortic valve classification and outlines a general strategy for using weak supervision to train machine learning models using unlabeled medical images at scale.

bioinformatics↗