Significance of changes in TNF-α and IL-10 levels in the progression of heart failure subsequent to myocardial infarction
Bibliographic record
Abstract
We tested whether a decrease in the ratio of interleukin-10 (IL-10) to tumor necrosis factor-alpha (TNF-alpha) correlates with the decrease in cardiac function in heart failure. It has been suggested that TNF-alpha plays a role in the progression of heart failure, and the effect of TNF-alpha in many tissues is modulated by IL-10. Any relation of these two cytokines to heart failure has never been examined. Cardiac function was assessed by echocardiographic and hemodynamic techniques in coronary artery-ligated rats at 1, 4, 8, and 16 wk after myocardial infarction (MI). Membrane-bound and soluble fractions of TNF-alpha and IL-10 proteins, the ratio of TNF-alpha to IL-10, and TNF-alpha and IL-10 mRNA levels were analyzed. Losartan was used to modify cardiac function in rats 4 wk after MI to further validate the relation between the IL-10-to-TNF-alpha ratio and cardiac function. Cardiac function deteriorated with time in all coronary artery-ligated groups, with severe failure at 16 wk after MI. Membrane-bound and soluble TNF-alpha protein fractions were increased 1 and 4 wk after MI, whereas TNF-alpha mRNA was increased 4 and 8 wk after MI. Membrane-bound IL-10 protein and mRNA levels were decreased 4, 8, and 16 wk after MI. The decrease in the IL-10-to-TNF-alpha protein ratio in all coronary artery-ligated groups correlated with the depressed cardiac function. Losartan improved cardiac function, membrane-bound and soluble TNF-alpha and IL-10 protein levels, the ratio of IL-10 to TNF-alpha, and IL-10 mRNA. This study suggests that a decrease in IL-10 and IL-10-to-TNF-alpha ratio correlates with depressed cardiac function.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".