<scp>MK2</scp> deficiency decreases mortality in male mice during the inflammatory phase after myocardial infarction
Bibliographic record
Abstract
Abstract Altering inflammation can impact the recovering heart's structure and function following myocardial infarction (MI). MAP kinase‐activated protein kinase 2 (MK2) regulates the stability of several pro‐inflammatory cytokines. Hence, this study was to determine if MK2 deficiency impaired the inflammatory phase of post‐MI wound repair. Myocardial infarctions were induced by permanent ligation of the left anterior descending coronary artery in 12‐week‐old male MK2+/+ and MK2−/− mice. Five days post‐MI, survival was 100% in MI‐MK2−/− (n = 20) and 79% in MI‐MK2+/+ mice (n = 29; Mandel‐Cox test: p < 0.05). Systolic and diastolic LV diameters were greater in MI‐MK2+/+ than MI‐MK2−/− mice. Infiltration of neutrophils or monocytes did not differ significantly. Cytokine and chemokine transcripts were quantified in infarcted and non‐infarcted LV tissue using qPCR arrays. Three days post‐MI, Ifna2 was increased and Il16 was decreased in infarcted tissue from MK2−/− hearts, compared with infarcted MK2+/+ tissue, whereas in the non‐infarcted MK2−/− myocardium Il27 increased and Tnfsf11, Ccl3, and Il1rn were decreased. Five days post‐MI, Ctf16 and Il10 increased in infarcted MK2−/− tissue whereas in the non‐infarcted MK2−/− myocardium Ccl9, Nodal, and Xcl2 increased and Il15 decreased. These findings suggest MK2 deficiency is an advantage during the inflammatory phase of cardiac wound repair post‐MI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".