Congratulations to Dr. Bernard Kuhn and Dr. Dennis Kostka on their recently funded CHP proposal, "Discovering Fibrosis Genes by Gene Expression Analysis in Single Heart Cells". The goal of the proposed research is sequence RNA of single heart cells in order to identify and characterize human fibrosis genes.
Congratulations to Dr. Carlton Bates and Co-Investigator Dr. Dennis Kostka on their recently funded NIH proposal, "Critical Roles for Fibroblast Growth Factor Receptors in Bladder Development". The broad long-term objective of this project is to elucidate the molecular control of bladder development to develop effective therapies for structural bladder disease.
Congratulations to Dr. Dennis Kostka and Dr. John Capra on their recently funded R01 proposal, "Modeling the Dynamics of Genome-Scale Data Across Trees". The objective of this project is to model genome-wide activity profiles in a statistical framework that accounts for interactions and dependencies in profiles from related cell types; it will enable researchers to characterize shifts in genomic activity that are associated with the creation of healthy cells, and identify how genomic regulation goes awry in disease.
Congratulations to Dr. Cecilia Lo on her recently funded DoD Investigator Initiative Proposal, "Respiratory ciliary dysfunction and pulmonary outcome in CHD patients". The goal of this project is to improve the prognosis for patients with CHD, many of whom must undergo high-risk cardiac surgeries. The findings from this study may provide the basis for future change in the standard of care to include pre-surgical screening of CHD patients for airway ciliary dysfunction and instituting pulmonary therapy perioperatively to improve outcome.
Congratulations to Dr. Cecilia Lo and Dr. Michael Tsang on their recently funded NIH administrative supplement, "Assaying Heterotaxy Patient Genes in a Cilia Motility and Left-Right Patterning". This project will examine whether expression of the RCV can rescue the HTX phenotype elicited by MO gene knockdown in the zebrafish embryo. Also, it will establish genotype-phenotype correlation in ciliary motion defects and develop software for quantitative classification of ciliary motion defects using a computational approach with computer vision and machine learning algorithms for visual pattern recognition. Using this software, we will determine whether different RCVs are associated with different ciliary motion defects. This will provide insights into structure-function relationships in the regulation of cilia motility.
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