Meet Our Latest KL2 Career Development Awardees
The UR CTSI has awarded four new KL2 Career Development Awards to early-career clinical and translational scientists across the University of Rochester Medical Center. These awards provide two years of mentored research support to help awardees advance their careers and obtain further K- or R- awards. This year’s awardees will adapt a behavioral therapy for use with Deaf individuals, test a new treatment regimen for patients with breast cancer that has spread to the brain, map brain pathways to aid surgery, and validate the use of artificial intelligence to support clinical decision making.
Learn more about this year’s KL2 cohort:
Cognitive Behavioral Therapy for Treatment Seeking with Deaf Individuals
Aileen Aldalur, Ph.D.
Postdoctoral Associate in Emergency Medicine
The U.S. Deaf community experiences higher rates of several mental health issues, but are much less likely to receive behavioral health treatment. During her postdoctoral training, Aldalur adapted a Cognitive Behavioral Therapy for Treatment Seeking (CBT-TS) intervention for use with Deaf individuals.
CBT-TS typically involves a telephone interview with people who screen positive for mental health issues, like alcohol use disorder, post-traumatic stress disorder, depression or anxiety, and encourages them to seek behavioral treatment. Aldalur’s adapted CBT-TS intervention uses technologies that are more accessible for Deaf people – video phone or Zoom – and factors in the unique linguistic and cultural concerns of the Deaf population, such as fear and mistrust of the health care system and lack of accessible information.
With KL2 funding, Aldalur will test the feasibility and efficacy of her adapted CBT-TS intervention with 30 Deaf adults from across the country.
Testing Treatments for Breast Cancer with Brain Metastases
Ajay Dhakal, M.B.B.S.
Assistant Professor of Medicine, Hematology/Oncology
In a subset of breast cancer patients, cancer cells have an excess of human epidermal growth factor receptor 2 (HER2) protein, which is normally involved in cell growth and contributes to tumor growth when overexpressed. These patients are typically treated with a combination of chemotherapy and HER2-targeted therapies, which have been shown to also act in the brain and prolong survival of patients whose HER2-positive breast cancer has spread to the brain.
Unfortunately, therapies that are typically used for patients who have HER2-negative breast cancer are not effective against brain metastases. But preliminary evidence suggests that HER2-targeted therapies may be able to help some of these patients. While they may appear to be HER2-negative because they have normal amounts of HER2 in their cells, their underlying HER2 signaling may be overactive.
Dhakal will test whether HER2-targeted therapies neratinib and capecitabine can shrink brain metastases in a small set of patients who have HER2-negative breast cancer and hyperactive HER2 signaling. In the process, he will assess the feasibility of a larger clinical trial.
An Integrated Approach to Personalized & Shareable Clinical Decision Support
Adam Dziorny, M.D., Ph.D.
Assistant Professor of Pediatrics, Critical Care and Biomedical Engineering
With expanding use of electronic health records, machine learning and artificial intelligence have increasingly been applied to health care. But these big health data projects haven’t produced meaningful outcomes for patients. Some of the trouble may lie in a failure to validate algorithms using broad data sets from multiple centers as well as poorly designed decision support, which increases physician alert fatigue.
With KL2 funding, Dziorny will test a novel approach to optimize, validate and implement an existing algorithm to develop a clinical decision support tool that is designed with the user (health care providers) in mind. His practical test case will center around acute kidney injury (AKI), a deadly condition that is common among critically ill children and is typically cared for reactively.
Dziorny will validate and refine an existing algorithm that predicts early AKI among critically ill children using two large multi-center datasets. The validated algorithm will then be used to identify children at high risk of developing AKI at Golisano Children’s Hospital. Guided by user-centered design principles, an alert will be added to the patients’ electronic health records to help inform physicians and improve proactive care.
High Definition Fiber Tracking for Neurosurgical Planning
Frank E. Garcea, Ph.D.
Postdoctoral Associate in Neurosurgery
Surgical management of brain tumors originating in the white matter areas of the brain can involve a stark choice: Does one leave a portion of a tumor, sparing adjacent white matter fibers that may be partially infiltrated with tumor, or does one fully remove a tumor and in so doing, potentially cause post-operative deficits.
For his KL2-funded project, Garcea will use high definition fiber tracking to identify white matter fiber pathways that support language production and tool use in 40 patients who have brain tumors originating in the white matter. Mapping the location of white matter fiber pathways in relation to a brain tumor will help neurosurgeons avoid these critical structures when removing a tumor. The long-term goal of this study is to understand how the protection of white matter fiber tracts can help preserve patients’ language and motor function after surgery.
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The projects described in this article are supported by the University of Rochester CTSA award KL2 TR001999 from the National Center for Advancing Translational Sciences of the National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Michael Hazard | 4/26/2022
