Diagnostics of genetic cardiac diseases using stem cell-derived cardiomyocytes

A new study by Professors Martti Juhola and Katriina Aalto-Setälä of the University of Tampere in Finland demonstrates that with the use of artificial intelligence and machine learning, it is possible not only to accurately sort sick cardiac cell cultures from healthy ones, but also to differentiate between genetic cardiac diseases. iPSC-derived cardiomyocytes can be derived from a blood sample […]

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Using AI to detect heart disease

Heart disease is the leading cause of death for both men and women, according to the Centers for Disease Control and Prevention (CDC). In the U.S., one in every four deaths is a result of heart disease, which includes a range of conditions from arrhythmias, or abnormal heart rhythms, to defects, as well as blood vessel diseases, more commonly known […]

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New rapid-fire method using pathology images, tumor data may help guide cancer therapies

By combining data on pathology images of 13 types of cancer and correlating that with clinical and genomic data, a Stony Brook University-led team of researchers are able to identify tumor-infiltrating lymphocytes (TILs), called TIL maps, which will enable cancer specialists to generate tumor-immune information from routinely gathered pathology slides. Published in Cell Reports , the paper details how TIL […]

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Using mathematical models to determine the best chemotherapy schedules

Can mathematical models predict how can cancer cells respond to varied chemotherapy schedules? In other words, should cancers associated with fast-growing tumors, like brain cancer, be treated using a low drug dose administered continuously, as opposed to a high drug dose given periodically? Professor Paul Newton and Ph.D. recipient Jeffrey West have developed a tool that can predict the best […]

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