<b><i>Unc93b1</i></b>-Dependent Endosomal Toll-Like Receptor Signaling Regulates Inflammation and Mortality during Coxsackievirus B3 Infection
Bibliographic record
Abstract
Coxsackievirus strain B serotype 3 (CVB3)-induced myocarditis is an important human disease that causes permanent tissue damage and can lead to death from acute infection or long-term morbidity caused by chronic inflammation. The timing and magnitude of immune activation following CVB3 infection can mediate a positive host outcome or increase tissue pathology. To better elucidate the role of endosomal Toll-like receptor (TLR) signaling in acute CVB3 infection, we studied mice with a loss-of-function mutation, known as Letr for 'loss of endosomal TLR response', in Unc93b1, which is a chaperone protein for TLR3, TLR7 and TLR9. Using Unc93b1(Letr/)(Letr) mice, we determined that Unc93b1-dependent TLR activation was essential for the survival of acute CVB3-induced myocarditis. We also determined that a lack of endosomal TLR signaling was associated with a higher viral load in target organs and that it increased inflammation, necrosis and fibrosis in cardiac tissue. Loss of Unc93b1 function was also associated with increased cardiac expression of Ifn-b and markers of tissue injury and fibrosis including Lcn2 and Serpina3n early after CVB3 infection. These observations establish a significant role for Unc93b1 in the regulation of the host inflammatory response to CVB3 infection and also reveal potential mediators of host tissue damage that merit further investigation in acute viral myocarditis.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".