The significance of IL‐10 stimulation in cardiac innate responses (1095.18)
Bibliographic record
Abstract
It is known that the cardioprotective role of IL‐10 may involve innate signaling via the activation of patterns recognition receptors, such as Toll‐like receptor 4 (TLR4). We have previously demonstrated that myeloid differentiation gene factor 88 (MyD88) plays a key role in this innate response to IL‐10. In the present study, we further characterized downstream details in IL‐10 stimulation of innate signaling using IL‐10 knockout (KO) mice. A significant increase in the expression of TLR2 was noticed in the KO mice hearts whereas TLR4 expression was not different from the wildtype (WT). There was a significant downregulation in MyD88 expression. The expression of interleukin‐1 receptor associated kinases ‐1 (IRAK‐1) was slightly but significantly less in IL‐10 KO hearts with almost no difference in IRAK‐4 expression. On the contrary, IRAK‐M (an inhibitor of MyD88‐ dependent TLR4 signaling pathway) and IRAK‐2 levels were higher in the KO mice compared to WT. Levels of TNF‐α in IL‐10 KO mice were higher both in the heart and blood. IL‐1β was elevated in the blood of IL‐10 KO mice; however, there was no change in cardiac expression. TLR2‐ mediated TNF‐α activation in IL‐10 KO mice led to an increase in apoptosis as evident from increased expression of pro‐apoptotic protein, Bax and Bax/Bcl‐xL ratio. In the KO hearts, the increased proteolytic enzyme activity of caspase3/7 as well as fibrosis in the heart suggested that TLR2‐mediated IRAK‐M/IRAK‐2 activation might enhance TGF‐β and TNF‐α. Thus an increase apoptosis as well as fibrosis in the IL‐10 KO mice suggest that this cytokine is an important player in restoring cardiac cells from damage. Grant Funding Source : Supported by the Canadian Institutes of Health Research
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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) | 0.000 | 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) | 0.004 | 0.001 |
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".