Biology subjects

Charu, V.

Publications and source records attributed to Charu, V..

3 recordsLinked to original sources

bioRxiv2024

Electrical Coordinated Reset Stimulation Induces Network Desynchronization in an in Vivo Model of Status Epilepticus

Epilepsy, a neurological disorder characterized by recurrent seizures, profoundly impacts individuals worldwide. Various electrical stimulation protocols have been investigated to mitigate epileptic seizures, among which Coordinated Reset (CR) stimulation may have potential for inducing long-lasting neural desynchronization. This study explores the acute effects of CR stimulation on synchronization dynamics during Status Epilepticus (SE) in an in vivo animal model. An electrographically sustained seizure-state was induced via 4-aminopyridine (4AP) administration to CA3. Custom-designed electrode probes were implanted to facilitate simultaneous recording and electrical stimulation. Analytical univariate and bivariate features were constructed from the LFP time-series recording. Feature metrics focused on spike synchronization metrics and continuous signal analysis of amplitude, spectral power and phase synchronization across electrode pairs and frequency bands. Significance of modulation was assessed through permutation testing of the observed differences between the CR-stimulated group (N=5) compared to the control (no stimulation) group (N=3) during SE. Results showed overall decrease in amplitude and power univariate features, and a significant modulation of bivariate synchronization and connectivity measures across the spectrum between the CR stimulation and control group. Our findings underscore the potential effectiveness of CR stimulation in attenuating excessive neural synchronization, paving the way for further exploration of CR stimulation as a viable intervention for network desynchronization of epileptiform activity and subsequently treatment of seizures.

neuroscience↗

bioRxiv2023

Characterizing tissue structures from spatial omics with spatial cellular graph partition

Spatial transcriptomic and proteomic measurements enable high-dimensional characterization of tissues. However, understanding organizations of cells at different spatial scales and extracting tissue structures of interest remain challenging tasks that require extensive human annotations. To address this need for consistent identification of tissue structures, in this work, we present a novel annotation method Spatial Cellular Graph Partitioning (SCGP) that allows unsupervised identification of tissue structures that reflect the anatomical and functional units of human tissues. We further present a reference-query extension pipeline SCGP-Extension that enables the generalization of existing reference tissue structures to previously unseen samples. Our experiments demonstrate reliable and robust partitionings of both spatial transcriptomics and proteomics datasets encompassing different tissue types and profiling techniques. Downstream analysis on SCGP-identified tissue structures reveals disease-relevant insights regarding diabetic kidney disease and skin disorder, underscoring its potential in facilitating spatial analysis and driving new discoveries.

bioinformatics↗

bioRxiv2022

Sampling bias in large healthcare claims databases

Healthcare claims databases that aggregate claims from multiple commercial insurers are increasingly being used to generate real-world evidence. These databases represent a non-random sample of the underlying population, but often little attention is paid to the inherent sampling bias within the data, and how it might affect results. As an illustrative example, we characterize variation in sampling in Optum's de-identified Clinformatics Data Mart Database (CDM) at the zip-code level in 2018, and identify socioeconomic and demographic factors associated with inclusion.

bioinformatics↗