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They say everyone has a double, which may be very good news for some heart patients. A researcher at Carle Illinois College of Medicine and his team are developing tools to create “digital twins” to help identify the cause and track the progression of heart failure in heart failure patients. The team’s research could ultimately identify treatments that best fit each patient’s disease profile, offering new hope for patients whose diseases do not respond to existing treatments.

<em>Pranav Dolbala</em>” width=”251″ data-fancy-caption=”<p><em>Pranav Dorbala</em></p>”/><figcaption><em>Pranav Dolbala</em></figcaption></figure>
<p>Pranav Dorbala, MD/PhD candidate at CI MED, leverages his background in computer science and the latest medical research to create personalized digital twins, or virtual representations of patients’ hearts.  “Through this ‘digital twin’ approach, we can leverage prior knowledge from science and the data-fitting capabilities of machine learning to create a more powerful model of the human heart,” Doruvala explained.</p>
<p>Drubala will work with an interdisciplinary team in the Institute for Regulatory Sciences’ DEPEND group (under the leadership of electrical and computer engineering professor Ravi Shankar Iyer) to explore the implications of genetics, proteins, and heart failure. We use data on other known factors to create personalized data. Digital replica.  “Once we model the heart on a computer, we can adjust the digital model to match the patient’s physical heart,” he said. Also, unlike physical models, digital twins are more flexible in responding to conditions that change over time.  “Through this digital model, we leverage machine learning, deep learning, and reinforcement learning (ML) to simulate a variety of factors relevant to individual patients, cardiac structure and function, and the impact of treatments, including heart failure and emerging diseases. We can identify the risk of developing the disease. Treatment options.”</p>
<p>“Heart failure is primarily caused by changes in the heart that occur to compensate for loss of heart function due to certain injuries, such as a heart attack, or due to the aging process, including the effects of high blood pressure, diabetes, and other factors.” explained Dolbara.  “When the heart overcorrects for this loss of function, heart failure develops.” This cardiac “remodeling” process is one of the key pathways in the development of heart failure, and is one of the main pathways in the development of heart failure, leading to possible causes and potential treatments. The transformative ability of digital doubles becomes even more valuable in identifying the law.</p>
<p>Druvala’s digital twin research builds on earlier work focused on understanding the different mechanistic pathways the heart uses to rebuild itself. This study investigated protein systems and pathways in the development of a broadly defined heart disease known as heart failure with preserved ejection fraction (HFpEF), for which there is no standard effective treatment.  “If we can identify these pathways, we can group patients with similar pathway-based changes to better predict who is at risk for heart failure and who may benefit from specific medications.” ” said Drubala.</p>
<p>The team’s model will be tested on large-scale clinical data from real patients and will be tracked over time to see which patients develop heart failure.  “Our next step is to identify how to output translational clinical metrics from the digital twin to maximize the benefits of this innovation in the clinical setting,” said Dorvala.  “We hope to integrate proteomic pathway data to understand how the enhancement of specific biological pathways leads to specific remodeling patterns in the heart.”</p>
<p>The final phase of the digital twin research project will integrate clinical trial data to identify treatments, including drug treatments, that will provide the greatest benefit and best outcomes for heart failure patients who have failed existing treatments, Dorvala said. He said it would happen.</p>
<p>Dorbala’s mentors on the digital twin project include project leader Ravishankar Iyer, professor of electrical and computer engineering and CI MED, and co-investigator Dr. Amil Shah of the University of Texas Southwestern Medical Center. The team is also working closely with collaborators at Brigham and Women’s Hospital (Harvard Medical School) in Boston.</p>
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