Publications by authors named "K Manikanda Bharath"

Objective: The purpose of this research is to estimate the effectiveness of non-laser surgery and laser surgery in the management of periodontitis.

Methods: One hundred participants with a chronic periodontitis diagnosis participated in a randomized controlled experiment. Two cohorts of patients were created: Cohort B underwent non-laser surgery and Cohort A underwent laser surgery.

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Topological data analysis provides a set of tools to uncover low-dimensional structure in noisy point clouds. Prominent amongst the tools is persistence homology, which summarizes birth-death times of homological features using data objects known as persistence diagrams. To better aid statistical analysis, a functional representation of the diagrams, known as persistence landscapes, enable use of functional data analysis and machine learning tools.

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Roadside soil contamination is mostly caused by human-caused pollutant deposition. PTEs are among the many substances that are harmful for both humans and the environment. PTE concentrations in roadside soil in Chennai, southern India, have been determined in this study.

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Article Synopsis
  • The study introduces a statistical framework for analyzing MRI and genomic data in lower grade gliomas (LGG), aiming to uncover radiogenomic associations.
  • A new imaging phenotype is created by dividing tumor regions into concentric spherical layers, using voxel-intensity-based probability densities to capture tumor heterogeneity.
  • The framework utilizes Bayesian variable selection models, demonstrating superior performance through simulations, and identifies cancer driver genes that may act as early-stage diagnostic markers, complemented by an R package for practical application.
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Accurate identification of synergistic treatment combinations and their underlying biological mechanisms is critical across many disease domains, especially cancer. In translational oncology research, preclinical systems such as patient-derived xenografts (PDX) have emerged as a unique study design evaluating multiple treatments administered to samples from the same human tumor implanted into genetically identical mice. In this paper, we propose a novel Bayesian probabilistic tree-based framework for PDX data to investigate the hierarchical relationships between treatments by inferring treatment cluster trees, referred to as treatment trees (R-tree).

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