Health and Medicine

This is a 3D volume rendering of an MRI of a post-mortem heart obtained from a patient with COVID-19, acquired at ultra-high 500 micron spatial resolution.

Team uses imaging to study ways the heart is affected by coronavirus

Researchers are using imaging and diagnostic pathology to examine postmortem hearts donated by victims of COVID-19 to gain a better understanding of how the coronavirus that causes COVID-19 affects the heart.

Research assistant Mahsa Majedi loads reagent used in DNA sample preparation in the genomics lab. She is part of a team of more than a dozen people at VUMC who are “sprinting” to develop — within 90 days — an antibody-based treatment to stop the spread of the Zika virus.

VUMC Research Enterprise begins ramping up

As Nashville cautiously begins to emerge from its two-month-long COVID-19 Safer at Home response, so too are the labs and facilities at Vanderbilt University Medical Center.

Probing innate immunity

Manuel Ascano team validates an inhibitor of the cGAS-STING signaling pathway, which is important for cellular innate immunity against bacteria, viruses, and our own damaged DNA.

Implant one day may replace dialysis

Vanderbilt researchers used pharmacological manipulations to increase salt and water transport by kidney cells grown in culture, a step necessary for realizing an implantable artificial kidney device.

Antibodies eye Pacific Island “fever”

Vanderbilt Vaccine Center team isolates monoclonal antibodies against Ross River virus, which causes rash, fever and debilitating muscle and joint pain lasting three to six months.

The adaptable anthrax bacterium

Vanderbilt researchers discover how anthrax bacterium defends itself against structural damage and resists the toxicity of the antimicrobial drug targocil.

May 7, 2020

Study finds AI can categorize cancer risk of lung nodules

by Tom Wilemon Computed tomography scans for people at risk for lung cancer lead to earlier diagnoses and improve survival rates, but they can also lead to overtreatment when suspicious nodules turn out to be benign. A study published in American Journal of Respiratory and Critical Care Medicine indicates that an artificial intelligence strategy can correctly assess and categorize these indeterminate pulmonary nodules (IPNs). When compared to the conventional risk models clinicians currently use, the algorithm developed by the team of researchers in a very large dataset (15,693 nodules) reclassified IPNs into low-risk or high-risk categories in over a third of cancers and benign nodules. “These results suggest the potential clinical utility of this deep learning algorithm to revise the probability of cancer among IPNs aiming to decrease invasive procedures and shorten time to diagnosis,” said Pierre Massion, MD, Cornelius Vanderbilt Chair in Medicine at Vanderbilt University, the study’s lead author. Currently, clinicians refer to guidelines issued by the American College of Radiology and the American College of Chest Physicians. Adherence to these guidelines can be variable, and how patient cases are classified can be subjective. With the goal of providing clinicians with an unbiased assessment tool, the researchers developed an algorithm based on datasets from the National Lung Screening Trial, Vanderbilt University Medical Center and Oxford University Hospital. Their study is the first to validate a risk stratification tool on multiple independent cohorts and to show reclassification performance that is significantly superior to existing risk models. With IPNs, clinicians are often faced with the dilemma of weighing whether to advise a patient to undergo an invasive surgical procedure, which may be unnecessary, against a watch-and-wait strategy, which may result in delaying needed cancer treatment. A definitive diagnosis of an IPN can take up to two years. Better assessment tools are needed by clinicians as screenings for patients at risk for lung cancer increase. Lung cancer is the leading cause of cancer-related death in the United States and globally. The overall five-year survival rate is 21.7%, but it is much greater (92%) for those patients who receive an early diagnosis of stage IA1 non-small cell cancer. n
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