While research has consistently shown the positive effects of having a teacher of the same race on various student outcomes, the literature has not examined how racial match affects the everyday interactions within classrooms. This research article by Dan Battey, Luis A. Leyva, Immanuel Williams, Victoria A. Belizario, Rachel Greco, and Roshni Shah addresses this underexplored area by documenting relational interactions in classrooms to find one mechanism that could be producing racialized effects on learning. Using a dataset from a study of twenty-five mathematics classrooms across predominantly white and black US middle schools, they examine the quality of relational interactions when teachers and students are racially matched and mismatched, as well as the effects on student achievement in mathematics. Their analysis shows how various dimensions of relational interactions significantly predict increases and decreases in achievement due to racial match.
Susceptibility to atrial fibrillation (AF) is determined by well-recognized risk factors such as diabetes mellitus or hypertension, emerging risk factors such as sleep apnea or inflammation, and increasingly well-defined genetic variants. As discussed in detail in a companion article in this series, studies in families and in large populations have identified multiple genetic loci, specific genes, and specific variants increasing susceptibility to AF. Since it is becoming increasingly inexpensive to obtain genotype data and indeed whole genome sequence data, the question then becomes to define whether using emerging new genetics knowledge can improve care for patients both before and after development of AF. Examples of improvements in care could include identifying patients at increased risk for AF (and thus deploying increased surveillance or even low-risk preventive therapies should these be available), identifying patient subsets in whom specific therapies are likely to be effective or ineffective or in whom the driving biology could motivate the development of new mechanism-based therapies or identifying an underlying susceptibility to comorbid cardiovascular disease. While current guidelines for the care of patients with AF do not recommend routine genetic testing, this rapidly increasing knowledge base suggests that testing may now or soon have a place in the management of select patients. The opportunity is to generate, validate, and deploy clinical predictors (including family history) of AF risk, to assess the utility of incorporating genomic variants into those predictors, and to identify and validate interventions such as wearable or implantable device-based monitoring ultimately to intervene in patients with AF before they present with catastrophic complications like heart failure or stroke.
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