Researchers in the Baker Institute for Animal Health have created a genetically engineered mouse model that could shed light on the causes of human infertility and allow researchers to explore other areas of reproduction.
Deer hunters were more likely to be swayed by social media messages about the potential risks of chronic wasting disease if they came from a source they believed aligned with their own views and values.
A new study found that patients with non-small cell lung cancer treated with a combination of low-dose radiation and immunotherapy had higher progression-free survival compared to patients who received immunotherapy alone.
Openly gay men were more likely than those who conceal their sexual orientation to seek care for mpox last year during a global outbreak that disproportionately affected their community, researchers from Cornell and the University of Toronto found.
The inflammatory response from adaptive immune cells – such as B and T lymphocytes – clears the body of the SARS-CoV-2 virus, but at the same time, it also causes the characteristic symptoms of COVID-19, a new study finds.
With new funding from the National Institutes of Health (NIH), Cornell faculty will investigate how SBHCs are not only leaving a positive impact on students, but also on the wider community’s well-being and public services across four counties in upstate New York.
A new study – using lab mice genetically modified with a human gene to shed light on a potential link between arsenic exposure and diabetes – revealed that while the male mice exposed to arsenic in drinking water developed diabetes, the female mice did not.
Researchers at Weill Cornell Medicine have catalogued the cellular response to stroke in a preclinical model, identifying the immune cells involved and the roles they may play in the days and weeks following a stroke.
Weill Cornell Medicine researchers received a $2.4 million grant from the U.S. Department of Defense Breast Cancer Research Program to evaluate a test that uses an artificial intelligence algorithm to determine whether a patient is positive for cancer.
