Runx1 promotes scar deposition and inhibits myocardial proliferation and survival during zebrafish heart regeneration.

Runx1 is a transcription factor that plays a key role in determining the proliferative and differential state of multiple cell types, during both development and adulthood. Here, we report how Runx1 is specifically upregulated at the injury site during zebrafish heart regeneration, and that absence of runx1 results in increased myocardial survival and proliferation, and overall heart regeneration, accompanied by decreased fibrosis. Using single cell sequencing, we found that the wild-type injury site consists of Runx1-positive endocardial cells and thrombocytes that induce expression of smooth muscle and collagen genes. Both these populations cannot be identified in runx1 mutant wounds that contain less collagen and fibrin. The reduction in fibrin in the mutant is further explained by reduced myofibroblast formation and upregulation of components of the fibrin degradation pathway, including plasminogen receptor annexin 2A as well as downregulation of plasminogen activator inhibitor serpine1 in myocardium and endocardium, resulting in increased levels of plasminogen. Our findings suggest that Runx1 controls the regenerative response of multiple cardiac cell types and that targeting Runx1 is a novel therapeutic strategy for inducing endogenous heart repair.BHF, Wellcome, MR

Evaluation of prognostic risk models for postoperative pulmonary complications in adult patients undergoing major abdominal surgery: a systematic review and international external validation cohort study

Background Stratifying risk of postoperative pulmonary complications after major abdominal surgery allows clinicians to modify risk through targeted interventions and enhanced monitoring. In this study, we aimed to identify and validate prognostic models against a new consensus definition of postoperative pulmonary complications. Methods We did a systematic review and international external validation cohort study. The systematic review was done in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched MEDLINE and Embase on March 1, 2020, for articles published in English that reported on risk prediction models for postoperative pulmonary complications following abdominal surgery. External validation of existing models was done within a prospective international cohort study of adult patients (≥18 years) undergoing major abdominal surgery. Data were collected between Jan 1, 2019, and April 30, 2019, in the UK, Ireland, and Australia. Discriminative ability and prognostic accuracy summary statistics were compared between models for the 30-day postoperative pulmonary complication rate as defined by the Standardised Endpoints in Perioperative Medicine Core Outcome Measures in Perioperative and Anaesthetic Care (StEP-COMPAC). Model performance was compared using the area under the receiver operating characteristic curve (AUROCC). Findings In total, we identified 2903 records from our literature search; of which, 2514 (86·6%) unique records were screened, 121 (4·8%) of 2514 full texts were assessed for eligibility, and 29 unique prognostic models were identified. Nine (31·0%) of 29 models had score development reported only, 19 (65·5%) had undergone internal validation, and only four (13·8%) had been externally validated. Data to validate six eligible models were collected in the international external validation cohort study. Data from 11 591 patients were available, with an overall postoperative pulmonary complication rate of 7·8% (n=903). None of the six models showed good discrimination (defined as AUROCC ≥0·70) for identifying postoperative pulmonary complications, with the Assess Respiratory Risk in Surgical Patients in Catalonia score showing the best discrimination (AUROCC 0·700 [95% CI 0·683–0·717]). Interpretation In the pre-COVID-19 pandemic data, variability in the risk of pulmonary complications (StEP-COMPAC definition) following major abdominal surgery was poorly described by existing prognostication tools. To improve surgical safety during the COVID-19 pandemic recovery and beyond, novel risk stratification tools are required. Funding British Journal of Surgery Society