Biology subjects

Khan, Y.

Publications and source records attributed to Khan, Y..

3 recordsLinked to original sources

bioRxiv2021

Design considerations of a wearable electronic-skin for mental health and wellness: balancing biosignals and human factors

Chronic stress has been associated with a variety of pathophysiological risks including developing mental illness. Conversely, appropriate stress management, can be used to foster mental wellness proactively. Yet, there is no existing method that accurately and objectively monitors stress. With recent advances in electronic-skin (e-skin) and wearable technologies, it is possible to design devices that continuously measure physiological parameters linked to chronic stress and other mental health and wellness conditions. However, the design approach should be different from conventional wearables due to considerations like signal-to-noise ratio and the risk of stigmatization. Here, we present a multi-part study that combines user-centered design with engineering-centered data collection to inform future design efforts. To assess human factors, we conducted an n=24 participant design probe study that examined perceptions of an e-skin for mental health and wellness as well as preferred wear locations. We complement this with an n=10 and n=16 participant data collection study to measure physiological signals at several potential wear locations. By balancing human factors and biosignals, we conclude that the upper arm and forearm are optimal wear locations.

bioengineering↗

bioRxiv2020

Insect wing extract: A novel source for green synthesis of nanoparticles of antioxidant and antimicrobial potential

Silver nanoparticles (AgNPs) are among the most widely synthesized and used nanoparticles (NPs). AgNPs have been traditionally synthesized from plant extracts, cobwebs, microorganisms, etc. However, their synthesis from wing extracts of common insect; Mang mao which is abundantly available in most of the Asian countries has not been explored yet. We report the synthesis of AgNPs from M. mao wings extract and its antioxidant and antimicrobial activity. The synthesized AgNPs were spherical, 40-60 nm in size and revealed strong absorption plasmon band around at 430 nm. Highly crystalline nature of these particles as determined by Energy-dispersive X-ray analysis and X-ray diffraction further confirmed the presence of AgNPs. Hydrodynamic size and zeta potential of AgNPs were observed to be 43.9 nm and -7.12 mV, respectively. Fourier-transform infrared spectroscopy analysis revealed the presence of characteristic amide proteins and aromatic functional groups. Thin-layer chromatography (TLC) and Gas chromatography-mass spectroscopy (GC-MS) analysis revealed the presence of fatty acids in the wings extract that may be responsible for biosynthesis and stabilization of AgNPs. Further, SDS-PAGE of the insect wing extract protein showed the molecular weight of 49 kDa. M. mao silver nanoparticles (MMAgNPs) exhibit strong antioxidant, broad-range antibacterial and antifungal activities, which signifies their biomedical and agricultural potential.

microbiology↗

bioRxiv2020

Metastatic Site Prediction in Breast Cancer usingOmics Knowledge Graph and Pattern Mining withKirchhoff's Law Traversal

Predicting the anatomical site of metastasis from a primary tumour remains an unsolved problem in breast cancer (BRCA) and metastatic disease more broadly. The difficulty is structural: metastatic biology is multi-site (bone, lung, liver, brain), multi-omics (genomics, proteomics, methylomics, drug response), and multi-modal (CNV, gene expression, DNA methylation, pathways, clinical associations). Existing classifiers either collapse this heterogeneity into a single feature vector or rely on a single omics layer, both of which discard the mechanistic structure that drives metastatic tropism. We introduce Kirchhoff Knowledge Graphs (K-KG), a framework that imports the conservation laws of electrical-circuit theory into knowledge graph reasoning. Our contributions are: (1) a layered RDF Cancer Decision Network integrating 36 polyomics datasets across mutations, pathways, drugs, diseases, and reactions; (2) two novel conservation laws--the Knowledge-Graph Voltage Law (KGVL) and Knowledge-Graph Current Law (KGCL)--that govern information flow during traversal and yield a principled measure of graph completeness; (3) topological motif mining on the conserved graph, replacing expression-based feature selection by identifying triangular sub-structures whose rewiring marks metastatic transition; (4) a Graph Convolutional Neural Network whose hidden layers are the omics layers themselves, predicting site-specific metastasis as a continuous percentage rather than a binary label. On TCGA-BRCA training plus one validation and four independent test cohorts from GEO, K-KG achieves 83.8% AUC for relapse prediction and up to 0.87 AUC / 0.91 F1 for Brain-site-specific prediction, outperforming Random Forest, Neural Network, and SVM baselines by 8-20 AUC points. To our knowledge this is the first application of Kirchhoffs laws (1845, 1847) to graph-based machine learning, and the first metastasis predictor that returns a per-site contribution profile rather than a single label.

bioinformatics↗