Biology subjects

Nair, B.

Publications and source records attributed to Nair, B..

3 recordsLinked to original sources

bioRxiv2022

Parchment Glutamine Index (PQI): A novel method to estimate glutamine deamidation levels in parchment collagen obtained from low-quality MALDI-TOF data

Parchment was used as a writing material in the Middle Ages and was made using animal skins by liming them with Ca(OH)2. During liming, collagen peptides containing Glutamine (Q) undergo deamidation resulting in a mass shift of 0.984 Da. Assessing the extent of deamidation can inform us about parchment production patterns and quality. In this study, we propose a simple three-step workflow, developed as an R package called MALDIpqi(), to estimate deamidation in parchment derived collagen using low-resolution MALDI-TOF spectra. After pre-processing raw spectra, we used weighted least-squares linear regression to estimate Q deamidation levels from the convoluted isotopic envelope for seven collagen-peptide markers. Finally, we employed a linear mixed effect model to predict the overall deamidation level of a parchment sample termed Parchment Glutamine Index (PQI). To test the robustness of the workflow, we applied MALDIpqi() to previously published ZooMS data generated from almost an entire library of the Cistercian monastery at Orval Abbey, Belgium. In addition to reliably predicting PQI, we observed interesting patterns pertaining to parchment production. MALDIpqi() holds excellent potential for biocodicological and other archaeological studies involving collagen, such as bone, but we also foresee its application in the food and biomedical industry.

bioinformatics↗

bioRxiv2018

E74 like ETS transcription factor 3 (ELF3) is a negative regulator of epithelial-mesenchymal transition in bladder carcinoma

Transcription factors are known to be commonly deregulated in various cancers. The E74 like ETS transcription factor 3 (ELF3) expression is restricted to epithelial tissue. In the present study, we evaluated the role of ELF3 in the pathogenesis of bladder carcinoma (BCa) using cell line model. The cell lines with low expression of ELF3 showed increased expression of mesenchymal markers and decreased expression of epithelial markers. Immunofluorescence and immunohistochemical analysis of ELF3 showed selective expression in low-grade BCa cell lines and tumor tissues, respectively. We demonstrated that overexpression of ELF3 in UMUC3, a mesenchymal BCa cell line resulted in reduced invasion and decreased expression of mesenchymal markers. Furthermore, using publicly available data, we found that low expression of ELF3 was associated with increased risk and poor overall survival rate in BCa. In conclusion, ELF3-modulated reversal of EMT might be a useful strategy in the treatment of bladder cancer.

cancer biology↗

bioRxiv2017

Explainable machine learning predictions to help anesthesiologists prevent hypoxemia during surgery

Hypoxemia causes serious patient harm, and while anesthesiologists strive to avoid hypoxemia during surgery, anesthesiologists are not reliably able to predict which patients will have intraoperative hypoxemia. Using minute by minute EMR data from fifty thousand surgeries we developed and tested a machine learning based system called Prescience that predicts real-time hypoxemia risk and presents an explanation of factors contributing to that risk during general anesthesia. Prescience improved anesthesiologists performance when providing interpretable hypoxemia risks with contributing factors. The results suggest that if anesthesiologists currently anticipate 15% of events, then with Prescience assistance they could anticipate 30% of events or an estimated additional 2.4 million annually in the US, a large portion of which may be preventable because they are attributable to modifiable factors. The prediction explanations are broadly consistent with the literature and anesthesiologists prior knowledge. Prescience can also improve clinical understanding of hypoxemia risk during anesthesia by providing general insights into the exact changes in risk induced by certain patient or procedure characteristics. Making predictions of complex medical machine learning models (such as Prescience) interpretable has broad applicability to other data-driven prediction tasks in medicine.

bioinformatics↗