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ÌýÌýCU Anschutz PI: (Biomedical Informatics;ÌýRadiology;ÌýApplied Mathematics)
ÌýÌýCU ºù«ÍÞÊÓƵ PI: Stephen Becker (Applied Mathematics)
​ÌýÌýExisting collaboration ($125K)
ÌýÌýTrack 2: AI, Advanced Computing, Quantum & Human Health

Magnetic Resonance Imaging (MRI) is limited by its long scan time and the requirement that the subject remain still; this precludes the usage of 3D MRI of the abdomen during pregnancy due to the ever-present motions of the fetus. With the AB Nexus award, we will develop deep-learning based methods to generate high-quality and anatomically accurate images from data collected with a faster MRI scan, which will mitigate the effects of fetal motion.

ÌýÌýCU Anschutz PI: (Medicine)
ÌýÌýCU ºù«ÍÞÊÓƵ PI: David Ciplet (Environmental Studies)
​ÌýÌýNew collaboration ($75K)
ÌýÌýTrack 1: Climate Change and Human Health

Incarceration creates the perfect storm for mental health crises and contributes to deteriorating physical health. Health risks in carceral environments are increasingly compounded by climate hazards including extreme temperatures, a lack of policies to protect incarcerated individuals from these temperatures, and pre-existing poor carceral infrastructure. The objective of this proposal is to identify the infrastructural, institutional, and epidemiological drivers of health risks related to extreme temperatures in Colorado prisons.

ÌýÌýCU Anschutz PI: (Medical Oncology)
ÌýÌýCU ºù«ÍÞÊÓƵ PI: Sabrina Spencer (Biochemistry; BioFrontiers Institute)
​ÌýÌýNew collaboration ($75K)
ÌýÌýTrack 0: Broad Initiative (Cancer)

The success of targeting these oncogenic drivers with oral tyrosine kinase inhibitors (TKIs) has resulted in significant improvements in objective response rates, progression free survival and overall survival, but unfortunately, acquired resistance to TKIs is inevitable and remains a major clinical challenge. Re-entering the cell cycle is a critical step for cancer cells to escape the effects of TKIs and form acquired resistance. This proposal will leverage novel techniques using patient derived samples to better characterize how lung cancer cells re-enter the cell cycle on treatment and develop acquired resistance.