Myeloid Differentiation Factor-88 Plays a Crucial Role in the Pathogenesis of Coxsackievirus B3–Induced Myocarditis and Influences Type I Interferon Production
Bibliographic record
Abstract
BACKGROUND: Myeloid differentiation factor (MyD)-88 is a key adaptor protein that plays a major role in the innate immune pathway. How MyD88 may regulate host response in inflammatory heart disease is unknown. METHODS AND RESULTS: We found that the cardiac protein level of MyD88 was significantly increased in the hearts of wild-type mice after exposure to Coxsackievirus B3 (CVB3). MyD88(-/-) mice showed a dramatic higher survival rate (86%) in contrast to the low survival (35%) in the MyD88(+/+) mice after CVB3 infection (P<0.0001). Pathological examination showed a significant decrease of cardiac and pancreatic inflammation in the MyD88(-/-) mice. Viral concentrations in the hearts were significantly decreased in the MyD88(-/-) mice. Cardiac mRNA levels for interleukin (IL)-1beta, tumor necrosis factor (TNF)-alpha, interferon (IFN)-gamma, and IL-18 were significantly decreased in the MyD88(-/-) mice. Similarly, serum levels of T-helper 1 cytokines were significantly decreased in the MyD88(-/-) mice. In contrast, cardiac protein levels of the activated interferon regulatory factor (IRF)-3 and IFN-beta were significantly increased in the MyD88(-/-) mice but not other usual upstream signals to IRF-3. The cardiac expression of coxsackie-adenoviral receptor and p56(lck) were also significantly decreased. CONCLUSIONS: MyD88 appears to be a key contributor to cardiac inflammation, mediating cytokine production and T-helper-1/2 cytokine balance, increasing coxsackie-adenoviral receptor and p56(lck) expression and viral titers after CVB3 exposure. Absence of MyD88 confers host protection possibly through novel direct activation of IRF-3 and IFN-beta.
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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) | 0.000 | 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 | 0.000 |
| 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".