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A machine-learning approach to human ex vivo lung perfusion predicts transplantation outcomes and promotes organ utilization

Ex vivo perfusion is a unique platform to study isolated human lungs. Here, authors show that a machine learning model, InsighTx, derived from data generated during ex vivo lung perfusion can accurately predict transplant outcomes and increase organ utilization rates.

Andrew T. Sage
Laura L. Donahoe
Shaf Keshavjee
ResearchOpen Access09 Aug 2023
Nature Communications

Volume: 14, P: 1-8
Inactivating hepatitis C virus in donor lungs using light therapies during normothermic ex vivo lung perfusion

Organs from donors with transmissible viral infections, such as hepatitis C virus (HCV), are not offered for transplantation due to a high risk of transmission. Here, Galasso et al. develop a method for treatment of HCV-infected human donor lungs that is safe and prevents HCV transmission in the pig model.

Marcos Galasso
Jordan J. Feld
Marcelo Cypel
ResearchOpen Access29 Jan 2019
Nature Communications

Volume: 10, P: 1-12
Transferred monolayer MoS₂ onto GaN for heterostructure photoanode: Toward stable and efficient photoelectrochemical water splitting

Mostafa Afifi Hassan
Min-Woo Kim
Sang-Wan Ryu
ResearchOpen Access27 Dec 2019
Scientific Reports

Volume: 9, P: 1-11

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