Biotechnology Research at Amrita Vishwa Vidyapeetham

The widespread use of organophosphates in agricultural farms for pest control has raised serious concerns over the quality of food and water available to the common public. As an effort to fabricate a sensitive, selective, cost-effective and non-toxic sensor to detect nitro-group containing organophosphates in and vegetable washings, a simple manganese dioxide based sensor was developed. α-Manganese dioxide nano-rods were electrodeposited on platinum disk electrode (MnO2/Pt) and is employed to detect 4-nitrophenyl phosphate (4-NPP). 4-NPP is a model compound that well represents widely used nitro-group containing organophosphates such methyl parathion, parathion, fenitrothion, methyl paraoxon and paraoxon in aqueous medium. Determination of 4-NPP at nanomolar levels was achieved using the fabricated sensor using cyclic voltammetry. The developed sensor was found to show a linear response in the concentration range 100 nM to 900 nM with a Limit of Detection (LOD) of 10 nM and a high sensitivity of 11.68 μA μM−1. The sensor showed good selectivity against many of common inorganic ions and two of the major organophosphates: Quinalphos and Dimethoate but the selectivity is poor among other nitro-group containing aromatics.

Outbreaks due to multidrug-resistant Candida auris have emerged as a large threat to modern medicine. Since skin colonization and environmental contamination have been identified as a precursor for outbreaks, we evaluated the antifungal activity of ultraviolet-C light using mercury vapour lamp with a peak emission of 254 ± 2 nm and octenidine dihydrochloride against C. auris clinical isolates. Octenidine dihydrochloride was found effective at significantly lower concentrations (0.00005-0.0004%) than those currently used in the clinical setting (0.05-0.1%). Scanning electron microscopy images show destruction of the organism within 6 h of exposure to 0.0005% octenidine dihydrochloride. Ultraviolet-C light could kill all C. auris with 15 min exposure.

With the rapid growth in the numbers of scientific publications in domains such as neuroscience and medicine, visually interlinking documents in online databases such as PubMed with the purpose of indicating the context of a query results can improve the multi-disciplinary relevance of the search results. Translational medicine and systems biology rely on studies relating basic sciences to applications, often going through multiple disciplinary domains. This paper focuses on the design and development of a new scientific document visualization platform, which allows inferring translational aspects in biosciences within published articles using machine learning and natural language processing (NLP) methods. From online databases, this software platform effectively extracted relationship connections between multiple subdomains within neuroscience derived from abstracts related to user query. In our current implementation, the document visualization platform employs two clustering algorithms namely Suffix Tree Clustering (STC) and LINGO. Clustering quality was improved by mapping top-ranked cluster labels derived from an UMLS-Metathesaurus using a scoring function. To avoid non-clustered documents, an iterative scheme, called auto-clustering was developed and this allowed mapping previously uncategorized documents during the initial grouping process to relevant clusters. The efficacy of this document clustering and visualization platform was evaluated by expert-based validation of clustering results obtained with unique search terms. Compared to normal clustering, auto-clustering demonstrated better efficacy by generating larger numbers of unique and relevant cluster labels. Using this implementation, a Parkinson's disease systems theory model was developed and studies based on user queries related to neuroscience and oncology have been showcased as applications. © 2019 Kassel University Press GmbH.