PILOT AND FEASIBILITY PROGRAM ABSTRACT The Pilot and Feasibility program will be a central and cohesive piece of the Pediatric Center of Excellence. The overall goal of the Pilot and Feasibility Program is to develop the science of investigators who need to obtain preliminary data to inform the design of future clinical trials in children with kidney disease. The Pilot and Feasibility Program will work closely with the Administrative Core, the Design/Analysis Core, the Clinical Phenotyping Core, and the Learning Health System Core to publicize, elicit, review, and fund proposals. The Pilot and Feasibility program will promote collaboration with investigators outside The Children's Hospital of Philadelphia and our regional academic partners at Johns Hopkins University. We will fund 2-4 pilot projects during the award period, with each project receiving $50,000 in direct support per year for a maximum of two years. At least one proposal will be funded from outside the research base. Additionally, the Department of Pediatrics at the Children?s Hospital of Philadelphia has generously agreed to fund an extra Pilot study at $50,000 per year for two years. All pilot and feasibility projects will be chosen meritoriously through peer review by a Scientific Advisory Committee. The Pilot and Feasibility Program will offer mentoring, through the formation of sub-committees, for those investigators who need input on their research projects throughout the submission and review process. To achieve the following specific aims and accomplish the overall research goals of the Pediatric Center of Excellence, each Pilot and Feasibility study will make use of the resources provided by at least one of the Center?s three Cores.
The Specific Aims of the Pilot and Feasibility Program are: 1) To stimulate and solicit new project ideas that support the overall research goal of the Pediatric Center of Excellence?namely to decrease the barriers to implementing clinical trials in children with kidney disease; 2) To encourage submission of pilot project proposals by junior investigators and to mentor these investigators in the revision of their proposals, as needed; 3) To review pilot project submissions for their scientific merit, level of innovation, and cohesion with the overall research goals and three Cores of the Pediatric Center of Excellence; and 4) To promote the development of new research directions, acquiring preliminary data that will be the foundation for applications for future independent research support in clinical trials. The Pilot and Feasibility Program will build upon the strong pediatric nephrology research experience at the Children?s Hospital of Philadelphia, the outstanding talent from the broader local scientific community at the University of Pennsylvania, and the regional expertise offered by Johns Hopkins University. We anticipate that the selected pilot projects will provide investigators the opportunity to generate preliminary data that will form the basis for future, competitive funding proposals in clinical trials designed to prevent, treat, or slow the progression of kidney disease in children, to identify novel targets for future therapies, and to better understanding the mechanisms of kidney disease. 1

Agency
National Institute of Health (NIH)
Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type
Specialized Center (P50)
Project #
1P50DK114786-01
Application #
9380710
Study Section
Special Emphasis Panel (ZDK1)
Project Start
Project End
Budget Start
2017-07-01
Budget End
2018-06-30
Support Year
1
Fiscal Year
2017
Total Cost
Indirect Cost
Name
Children's Hospital of Philadelphia
Department
Type
DUNS #
073757627
City
Philadelphia
State
PA
Country
United States
Zip Code
19104
Zheng, Qiang; Tasian, Gregory; Fan, Yong (2018) TRANSFER LEARNING FOR DIAGNOSIS OF CONGENITAL ABNORMALITIES OF THE KIDNEY AND URINARY TRACT IN CHILDREN BASED ON ULTRASOUND IMAGING DATA. Proc IEEE Int Symp Biomed Imaging 2018:1487-1490
Zheng, Qiang; Warner, Steven; Tasian, Gregory et al. (2018) A Dynamic Graph Cuts Method with Integrated Multiple Feature Maps for Segmenting Kidneys in 2D Ultrasound Images. Acad Radiol 25:1136-1145
Li, Hongming; Galperin-Aizenberg, Maya; Pryma, Daniel et al. (2018) Unsupervised machine learning of radiomic features for predicting treatment response and overall survival of early stage non-small cell lung cancer patients treated with stereotactic body radiation therapy. Radiother Oncol 129:218-226
Li, Hongming; Zhu, Xiaofeng; Fan, Yong (2018) Identification of Multi-scale Hierarchical Brain Functional Networks Using Deep Matrix Factorization. Med Image Comput Comput Assist Interv 11072:223-231
Li, Hongming; Fan, Yong (2018) Brain Decoding from Functional MRI Using Long Short-Term Memory Recurrent Neural Networks. Med Image Comput Comput Assist Interv 11072:320-328
Li, Hongming; Fan, Yong (2018) Identification of Temporal Transition of Functional States Using Recurrent Neural Networks from Functional MRI. Med Image Comput Comput Assist Interv 11072:232-239
Li, Hongming; Fan, Yong (2018) NON-RIGID IMAGE REGISTRATION USING SELF-SUPERVISED FULLY CONVOLUTIONAL NETWORKS WITHOUT TRAINING DATA. Proc IEEE Int Symp Biomed Imaging 2018:1075-1078
Li, Hongming; Satterthwaite, Theodore D; Fan, Yong (2018) BRAIN AGE PREDICTION BASED ON RESTING-STATE FUNCTIONAL CONNECTIVITY PATTERNS USING CONVOLUTIONAL NEURAL NETWORKS. Proc IEEE Int Symp Biomed Imaging 2018:101-104
Zhu, Xiaofeng; Zhang, Weihong; Fan, Yong et al. (2018) A Robust Reduced Rank Graph Regression Method for Neuroimaging Genetic Analysis. Neuroinformatics 16:351-361