Inflammatory Cytokines and Postmyocardial Infarction Remodeling
Bibliographic record
Abstract
Inflammatory response and cytokine elaboration are particularly active after myocardial infarction and contribute to cardiac remodeling and eventual host outcome. The triggers of cytokine release in the acute postinfarction period include mechanical deformation, ischemic stimulus, reactive oxygen species (ROS), and cytokine self-amplification pathways. Acutely, the elaboration of tumor necrosis factor, IL-1 and IL-6, transforming growth factor families of cytokines, contribute to survival or deaths of myocytes, modulation of cardiac contractility, alterations of vascular endothelium, and recruitment of additional circulating cells of inflammation to the injured myocardium. This leads to further local oxidative stress and remodeling but also initiates the processes of wound healing. Chronically, sustained presence of cytokines leads to myocyte phenotype transition and activation of matrix metalloproteinases that modifies interstitial matrix, augmenting further the remodeling process. This in turn alters the local collagen composition and also the integrins that constitute the interface between myocytes and the matrix. These processes ultimately, when favorable, pave the way for angiogenesis and cellular regeneration. Thus, the insightful modulation of cytokines through current and future therapies could promote improved healing and cardiac remodeling postmyocardial infarction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".