Efficacy of chimeric antigen receptor engineered natural killer cells in the treatment of hematologic malignancies: a systematic review and meta-analysis of preclinical studies.

Background: Chimeric antigen receptor (CAR) engineered NK cells (CAR-NK) are a novel approach to the immunotherapy of hematologic malignancies which seeks to overcome some of the challenges faced by CAR-T cells (CAR-T). With few published clinical studies, preclinical studies can identify strategies to accelerate clinical translation. We conducted a systematic review on the preclinical in vivo use of CAR-NK for the treatment of hematologic malignancies to assess these therapies in a holistic and unbiased manner.

Hypovolaemic phlebotomy in patients undergoing hepatic resection at higher risk of blood loss (PRICE-2): a randomised controlled trial.

Background: Blood loss and subsequent red blood cell transfusions are common in liver surgery. Hypovolaemic phlebotomy is associated with decreased red blood cell transfusion in observational studies. This trial aimed to investigate whether hypovolaemic phlebotomy is superior to usual care in reducing red blood cell transfusions in patients undergoing liver resection.

Liberal or Restrictive Transfusion Strategy in Aneurysmal Subarachnoid Hemorrhage.

Background: The effect of a liberal red-cell transfusion strategy as compared with a restrictive strategy in patients during the critical care period after an aneurysmal subarachnoid hemorrhage is unclear.

Methods: We randomly assigned critically ill adults with acute aneurysmal subarachnoid hemorrhage and anemia to a liberal strategy (mandatory transfusion at a hemoglobin level of ≤10 g per deciliter) or a restrictive strategy (optional transfusion at a hemoglobin level of ≤8 g per deciliter). The primary outcome was an unfavorable neurologic outcome, defined as a score of 4 or higher on the modified Rankin scale (range, 0 to 6, with higher scores indicating greater disability) at 12 months.

Roadmap for the evolution of monitoring: developing and evaluating waveform-based variability-derived artificial intelligence-powered predictive clinical decision support software tools.

Background: Continuous waveform monitoring is standard-of-care for patients at risk for or with critically illness. Derived from waveforms, heart rate, respiratory rate and blood pressure variability contain useful diagnostic and prognostic information; and when combined with machine learning, can provide predictive indices relating to severity of illness and/or reduced physiologic reserve. Integration of predictive models into clinical decision support software (CDSS) tools represents a potential evolution of monitoring.