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We are pleased to announce that 42 investigators, from across BMC and the BU Medical & Charles River campuses, applied to the Integrated Pilot Award Program, sponsored by the BU Clinical and Translational Science Institute. From our pool of applications, 14 outstanding pilot projects were selected for funding, totaling an allocation of $645,000. We congratulate all of the recipients and look forward to your contributions to translational research.
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Co-funded by BMC & BU CTSI
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Naltrexone for the Treatment of Cannabis Use Disorder in Pregnancy
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The primary objective of this study is to gain pilot data on the relative safety and efficacy of the use of Naltrexone (extended release; trade name: Vivitrol) (NTX) in pregnant persons with cannabis use disorder. The outcome measure of this study will be quantity of cannabis use, defined by survey and laboratory data assessing cannabis consumption. All key outcome measures are as follows: 1) participant outcomes [cannabis consumption, retention in care, preterm birth, and side effects / adverse events); 2) fetal outcomes (intrauterine growth, fetal anomalies).
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Assessing and Addressing Implicit Bias within Healthcare-acquired Infections (AAIm HI)
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Implicit bias training is becoming more prevalent across healthcare but has not been used to specifically improve a targeted healthcare outcome. This proposal aims to reduce implicit bias in healthcare and innovate the use of implicit bias training to improve specific patient outcomes (i.e., HAIs) impacted by racial bias.
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Association between Hip Shape and Hip Symptoms
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The group will capitalize on the hip structure and symptom data previously collected as part of the Multicenter Osteoarthritis (MOST) study. The group will analyze long-limb radiographs using both standard measures and statistical shape modeling (SSM) to quantify femoral and acetabular shape.
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Determining Contributions of Hepatocyte Heterogeneity
to ZZ AATD-Associated Liver Disease
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This proposal will apply the first-ever integrated advanced
RNA sequencing approaches to these rare AATD clinical samples using complementary spatial transcriptomics and single nuclear sequencing to facilitate future therapeutic discovery, with an emphasis on improving cell therapy for this large patient population.
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Individual and Neighborhood-Level Disparities in Buprenorphine Treatment Among BMC Patients
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The proposed research aims to describe individual and neighborhood-level disparities in buprenorphine receipt for BMC patients using D4E. This proposal will generate the preliminary work needed for a larger proposal involving more robust geo-spatial analyses of disparities in buprenorphine treatment at BMC.
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Pilot Feasibility Study of an In-Home, Body Weight Harness Mobility System for Infants with Down syndrome
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This project will set the stage for the first clinical trial of a mobility-related intervention specifically tailored for infants with DS by testing the feasibility of harness systems with infants and families and identifying measures that will serve as primary outcome variables.
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Risk Assessment of Lung Squamous Premalignant Lesions
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The group is currently developing deep learning frameworks using digitized H&E whole slide images (WSIs) of PMLs to predict histologic features and outcomes. In this proposal, the group will develop deep learning models using PML WSIs and clinical covariates to predict the risk of a lesion progressing to LUSC and a patient-level risk of developing lung cancer.
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Target Deconvolution Of Host-Directed Antiviral Rocaglates
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In this research, a two-phased approach will be used to identify rocaglate host targets that are important for SARS-CoV-2 infection. The approach involves the use of a novel thermal proteome profiling assay to identify candidate targets of antiviral rocaglates, followed by a targeted CRISPR screen to disrupt candidate targets and evaluate the impact on infection efficiency.
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Big Data and Team Science Approach to Identify Novel Molecular
Pathway and Mechanism of Action of a New Drug for Patients with Chronic Kidney Disease
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The project will leverage pathological and single-cell transcriptome data from animal models to gain a deeper understanding of the underlying mechanisms of potential candidate drugs. Furthermore, it will validate and translate these findings using CKD patient data and samples collected as part of the KPMP and BKBC studies. Successful completion of this project has the potential to yield new therapeutics targeting novel drug targets for CKD and proteinuria.
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Funded by BU CTSI Informatics
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Understanding Racial Disparities, Genetic Risk and Phenotypes in Patients with Hypertrophic Cardiomyopathy using Machine Learning Algorithms
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The current research interests include studying the interplay between genetics, race, social determinants of health and outcomes in patients with hypertrophic cardiomyopathy followed at Boston Medical Center, the largest safety net hospital in New England. The group aims to study the use of novel machine-learning algorithms to help improve the diagnosis of hypertrophic cardiomyopathy using echocardiography.
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Profiling of Vulnerable Neurons During the Prodromal Stage
of Alzheimer’s Disease
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Dr. Roussarie, Dr. Stein, and Dr. Zhang propose obtaining brain samples from
the BU Alzheimer's disease research center, specifically from individuals with Alzheimer's lesions who passed away before experiencing any symptoms. These samples will be analyzed using single-nucleus RNA-sequencing to identify very early disease-associated molecular events. Focusing on the presymptomatic stage of the disease will enhance their understanding of disease onset and help identify new therapeutic intervention points.
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Benchmarking Intra-tumor Heterogeneity Approaches in Aggressive
Breast Cancer
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To evaluate the significance and limitations of current state-of-the-art intra-tumor modeling approaches, we propose the following aims: 1) Map and document intra-tumor breast cancer heterogeneity using an ultra-resolved spatial multiplexing technology (MERFISH); and 2) Determine how existing patient-derived organoid models recapitulate the original TME diversity at the single-cell level.
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Defining a Molecular Signature of Cardiac Dysfunction in Systemic Immunoglobulin Light Chain Amyloidosis
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Understanding early mechanisms of cardiac dysfunction is key to developing screening methods and treatments critical to improving these outcomes. Therefore, Dr. Edward and her team seek to identify and validate a molecular signature for early mechanisms of LC-mediated cardiac dysfunction using patient-derived LCs in a novel disease model of induced pluripotent stem cells.
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The Relationship of Intra-articular Mineralization to Synoviti
in Knee Osteoarthritis
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This proposal aims to investigate the relationship between IA mineralization and inflammation (synovitis on MRI) in Knee OA. The study will provide valuable insights into how IA mineralization contributes to inflammation, potentially leading to improved therapies, such as anti-inflammatory treatments, for specific groups of people with Knee OA.
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For information about our awardees and their Integrated Pilot Grant Award Projects
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