DYRK1A interacts with the tuberous sclerosis complex and promotes mTORC1 activity.

DYRK1A, a ubiquitously expressed kinase is linked to the dominant intellectual developmental disorder, microcephaly, and Down syndrome in humans. It regulates numerous cellular processes such as cell cycle, vesicle trafficking, and microtubule assembly. DYRK1A is a critical regulator of organ growth; however, how it regulates organ growth is not fully understood.

Adjusting for Congenital Heart Surgery Risk Using Administrative Data.

Background: Congenital heart surgery (CHS) encompasses a heterogeneous population of patients and surgeries. Risk standardization models that adjust for patient and procedural characteristics can allow for collective study of these disparate patients and procedures.

Objectives: We sought to develop a risk-adjustment model for CHS using the newly developed Risk Stratification for Congenital Heart Surgery for ICD-10 Administrative Data (RACHS-2) methodology.

Association Between Remote Monitoring and Interstage Morbidity and Death in Patients With Single-Ventricle Heart Disease Across Socioeconomic Groups.

Background: Despite improvements in survival over time, the mortality rate for infants with single-ventricle heart disease remains high. Infants of low socioeconomic status (SES) are particularly vulnerable. We sought to determine whether use of a novel remote monitoring program, the Cardiac High Acuity Monitoring Program, mitigates differences in outcomes by SES.

Direct Oral Anticoagulants vs Low-Molecular-Weight Heparin and Recurrent VTE in Patients With Cancer: A Randomized Clinical Trial.

Importance: In patients with cancer who have venous thromboembolism (VTE) events, long-term anticoagulation with low-molecular-weight heparin (LMWH) is recommended to prevent recurrent VTE. The effectiveness of a direct oral anticoagulant (DOAC) compared with LMWH for preventing recurrent VTE in patients with cancer is uncertain.

Objective: To evaluate DOACs, compared with LMWH, for preventing recurrent VTE and for rates of bleeding in patients with cancer following an initial VTE event.

Machine Learning to Predict Interstage Mortality Following Single Ventricle Palliation: A NPC-QIC Database Analysis.

There is high risk of mortality between stage I and stage II palliation of single ventricle heart disease. This study aimed to leverage advanced machine learning algorithms to optimize risk-prediction models and identify features most predictive of interstage mortality. This study utilized retrospective data from the National Pediatric Cardiology Quality Improvement Collaborative and included all patients who underwent stage I palliation and survived to hospital discharge (2008-2019).