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Biology subjects

Hazarika, D.

Publications and source records attributed to Hazarika, D..

2 recordsLinked to original sources

Multi Omics Driven Polypharmacology for Osteoarthritis Joint Regeneration

Osteoarthritis (OA) is a common degenerative condition of major joints with no effective cure. Yograj Guggulu (YG) is a well-established polyherbal formulation in Indian traditional medicine for the management of joint disorders. However, its mode of action remains unclear. We integrated multi omics network analysis with a rat model to investigate the effects of oral administration of YG on osteoarthritic joints. YG administration significantly alleviated pain and improved joint function. Imaging and histological analyses reveal that YG protects against structural degeneration in the joint with OA. Proteomic and metabolomic profiling, along with network analysis, uncovered that multiple components of YG alter protein expression in key pathways related to inflammation and cartilage degradation, thus validating the observed clinical effects. The efficacy of YG seems to be the result of the synergistic action of its diverse constituents. Our findings support the use of YG as a multitarget therapy for OA and encourage efforts for the potential repurposing of its active components.

systems biology↗

CameraEEG: Synchronized Recording of Video with Electroencephalogram data on an Android Application

We present a novel android application named CameraEEG that enables synchronized acquisition of Electroencephalogram(EEG) and camera data using a smartphone. Audio-visual events of interest experienced by the subject were also recorded using a button press on the CameraEEG app. Unlike lab-restricted experiments, which usually constrain the subjects mobility, this wearable solution enables monitoring of the human brain during everyday life activities. The app was built using Android SDK version 28 and Smarting mobi SDK from mbraintrain. It works on all android devices having a minimum Android OS - Lollipop. We successfully recorded thirty minutes of synchronized Video and EEG during eyes closed and walking tasks using the app. Event markers enabled by the subject using the app during walking tasks were also recorded. Timing tests showed that temporal synchronization of video and EEG data was good. We analysed the recorded data and were able to identify the task performed by the subject from the event markers. The power spectrum density of the two tasks showed different power spectrums with a peak in the alpha band for eyes closed task. We also provide android studio codes for download and detailed help documentation for the community to test the developed application.

bioengineering↗