Selected Grantee Publications
Deep Learning Is Widely Applicable to Phenotyping Embryonic Development and Disease
Naert et al., Development. 2021.
https://pubmed.ncbi.nlm.nih.gov/34739029/
Genome editing simplifies the generation of new animal models for congenital disorders. The authors illustrate how deep learning (U-Net) automates segmentation tasks in various imaging modalities. They demonstrate this approach in embryos with polycystic kidneys (pkd1 and pkd2) and craniofacial dysmorphia (six1). They provide a library of pre-trained networks and detailed instructions for applying deep learning to datasets and demonstrate the versatility, precision, and scalability of deep neural network phenotyping on embryonic disease models. Supported by ORIP (P40OD010997, R24OD030008), NICHD, NIDDK, and NIMH.
The High Affinity Dopamine D2 Receptor Agonist MCL-536: A New Tool for Studying Dopaminergic Contribution to Neurological Disorders
Subburaju et al., ACS Chemical Neuroscience. 2021.
https://pubs.acs.org/doi/full/10.1021/acschemneuro.1c00094
The dopamine D2 receptor exists in two different states, D2high and D2low; the former is the functional form of the D2 receptor and associates with intracellular G-proteins. The D2 agonist [3H]MCL-536 has high affinity for the D2 receptor (Kd 0.8 nM) and potently displaces the binding of (R-(-)-N-n-propylnorapomorphine (NPA; Ki 0.16 nM) and raclopride (Ki 0.9 nM) in competition binding assays. The authors characterized [3H]MCL-536. [3H]MCL-536 as metabolically stable. In vitro autoradiography on transaxial and coronal brain sections showed specific binding of [3H]MCL-536. [3H]MCL-536's unique properties make it a valuable tool for research on neurological disorders like Parkinson's disease or schizophrenia. Supported by ORIP (R43OD020186, R44OD024615) and NIMH.