Rapid identification of inflammatory arthritis and associated adverse events following immune checkpoint therapy: a machine learning approach.

Introduction: Immune checkpoint inhibitor-induced inflammatory arthritis (ICI-IA) poses a major clinical challenge to ICI therapy for cancer, with 13% of cases halting ICI therapy and ICI-IA being difficult to identify for timely referral to a rheumatologist. The objective of this study was to rapidly identify ICI-IA patients in clinical data and assess associated immune-related adverse events (irAEs) and risk factors.

Methods: We conducted a retrospective study of the electronic health records (EHRs) of 89 patients who developed ICI-IA out of 2451 cancer patients who received ICI therapy at Northwestern University between March 2011 to January 2021.

Heat adaptation of phage T7 under an extended genetic code.

While bacteriophages have previously been used as a model system to understand thermal adaptation, most adapted genomes observed to date contain very few modifications and cover a limited temperature range. Here, we set out to investigate genome adaptation to thermal stress by adapting six populations of T7 bacteriophage virions to increasingly stringent heat challenges. Further, we provided three of the phage populations' access to a new genetic code in which Amber codons could be read as selenocysteine, potentially allowing the formation of more stable selenide-containing bonds.