The temporal pattern of immune and inflammatory proteins prior to a recurrent coronary event in post-acute coronary syndrome patients

Purpose: We assessed the temporal pattern of 29 immune and inflammatory proteins in post-acute coronary syndrome (ACS) patients, prior to the development of recurrent ACS. Methods: High-frequency blood sampling was performed in 844 patients admitted for ACS during one-year follow-up. We conducted a case-control study on the 45 patients who experienced reACS (cases) and two matched event-free patients (controls) per case. Olink Proteomics’ immunoassay was used to obtain serum levels of the 29 proteins, expressed in an arbitrary unit on the log2-scale (Normalized Protein eXpression, NPX). Linear mixed-effects models were applied to examine the temporal pattern of the proteins, and to illustrate differences between cases and controls. Results: Mean age was 66 ± 12 years and 80% were men. Cases and controls had similar baseline clinical characteristics. During the first 30 days, and after multiple testing correction, cases had significantly higher serum levels of CXCL1 (difference of 1.00 NPX, p ¼ 0.002), CD84 (difference of 0.64 NPX, p ¼ 0.002) and TNFRSF10A (difference of 0.41 NPX, p < 0.001) than controls. After 30 days, serum levels of all 29 proteins were similar in cases and controls. In particular, no increase was observed prior to reACS. Conclusions: Among 29 immune and inflammatory proteins, CXCL1, CD84 and TNFRSF10A were associated with early reACS after initial ACS-admission

Applying Computerized-Scoring Models of Written Biological Explanations across Courses and Colleges: Prospects and Limitations

Our study explored the prospects and limitations of using machine-learning software to score introductory biology students’ written explanations of evolutionary change. We investigated three research questions: 1) Do scoring models built using student responses at one university function effectively at another university? 2) How many human-scored student responses are needed to build scoring models suitable for cross-institutional application? 3) What factors limit computer-scoring efficacy, and how can these factors be mitigated? To answer these questions, two biology experts scored a corpus of 2556 short-answer explanations (from biology majors and nonmajors) at two universities for the presence or absence of five key concepts of evolution. Human- and computer-generated scores were compared using kappa agreement statistics. We found that machine-learning software was capable in most cases of accurately evaluating the degree of scientific sophistication in undergraduate majors’ and nonmajors’ written explanations of evolutionary change. In cases in which the software did not perform at the benchmark of “near-perfect” agreement (kappa > 0.80), we located the causes of poor performance and identified a series of strategies for their mitigation. Machine-learning software holds promise as an assessment tool for use in undergraduate biology education, but like most assessment tools, it is also characterized by limitations