Four Research Teams Awarded Johns Hopkins Institute for Clinical and Translational Research (ICTR) Accelerated Translational Incubator Pilot (ATIP) Program Grants
Funded by a Clinical and Translational Science Award (CTSA) from the NIH National Center for Advancing Translational Sciences (NCATS), ATIP seeks to support projects that focus on the science of science and ultimately promote NCATS’ mission to accelerate translational research by exploring more efficient strategies for overcoming barriers to investigational studies. Grants are awarded annually and the theme for this years’ funding cycle was improving the process of data integration. The research proposals selected for support included 2 projects at Johns Hopkins and 2 projects from the ICTR’s CTSA partner, the University of Maryland Baltimore ICTR. Each awardee receives a 12-month, $50,000 grant.
2024 ATIP Recipients
Harrison Bai, MD
“Enhancing Large Vision-Language Model Efficacy through Representative Slice Selection from 3D Medical Imaging Data”
This project seeks to overcome limitations encountered in the ability of vision-language models (VLM) to transition from analyzing 2D to 3D medical images. The primary objective of this work will be to adapt large VLMs to interpret and analyze 3D medical imaging effectively. This will be achieved by developing a multimodal dataset with annotated 3D MRI brain scans; implementing as well as evaluating an unsupervised method for selecting representative 2D slices from 3D scans; and creating, a benchmarking system for assessing the clinical efficacy of VLMs.
Casey Rebholz, PhD, MPH
“Plasma Protein Biomarkers of Healthy Dietary Patterns, Chronic Kidney Disease Progression, and All-Cause Mortality”
This research proposal is designed to integrate untargeted proteomics data with dietary data, clinical characteristics, and health outcomes in the Chronic Renal Insufficiency Cohort (CRIC) study. With a focus on making the rich proteomics data available in the CRIC study accessible to more investigators, the major goals of this work include quantifying the quality of the broad and untargeted proteomics dataset; developing and implementing a processing pipeline for the proteomics data; and finally integrating the datasets with the clinical data in preparation for analysis.
Chikiang Chen, PhD, MS
University of Maryland
“A New Framework for Information Integration and Its Application to Study Risk Factors Associated with Alzheimer’s Disease Onset”
A statistically informed data integration framework will be developed to allow for the inclusion of information from diverse and large cohorts that can accommodate heterogeneity in the data (i.e., different outcome measures; predictor sets) and does not require accessing raw data from external sources that may include sensitive participant information.
2023 ATIP Recipients
Fernando Goes, MD
“EEG Gamma Activity as a Biomarker of Rapid Antidepressant Action”
Scott Krummey, MD, PhD
“HLA Epitope Mismatch Analysis in Transplantation: Development of an Immunoassay to Detect Pathogenic HLA Class II Antibodies in Transplant Patients”
Ying Zou, MD, PhD, FACMG
“Rapid CRISPR/Cas9-mediated Nanopore Flongle Sequencing to Detect PML::RARA Fusion in Acute Promyelocytic Leukemia”
2022 ATIP Recipients
Michael Koldobskiy, MD, PhD
“Novel Anthracycline Analogues for Therapy of Histone-mutant Pediatric Glioma”
Jonathan Webster, MD
“Advancing Decitabine Genomic Incorporation as A Novel Pharmacodynamic and Response-predictive Biomarker in Myeloid Malignancies: A Focus on DNMT Inhibition Combined with Agents Targeting the p53/MDM2 Pathway and A Corollary to NCT03041688”
Jonathan Webster, MD
“CRISPRi/a Screen to Identify Molecular Pathways and Targets that Modulate RPE-EMT: An Approach for the Treatment of Atrophic (Dry) AMD”
2021 ATIP Recipients
Chad Gordon, DO
“MRI-Compatible Skull-Embedded Implant for Chronic & Direct Medicine Delivery to Treat Neurologic Pathology”
Robert Stevens, MD
“Computational Subphenotype Discovery and Treatment Response Prediction in Traumatic Brain Injury”
Trish Simner, PhD
“Clinical Translation of Nanopore Metagenomic Next-Generation Sequencing for Pathogen Identification”
