MyD88 contributes to neuroinflammatory responses induced by cerebral ischemia/reperfusion in mice
Bibliographic record
Abstract
Myeloid differentiation primary-response protein-88 (MyD88) is one of adaptor proteins mediating Toll-like receptors (TLRs) signaling. Activation of MyD88 results in the activation of nuclear factor kappa B (NFκB) and the increase of inflammatory responses. Evidences have demonstrated that TLRs signaling contributes to cerebral ischemia/reperfusion (I/R) injury. However, the role of MyD88 in this mechanism of action is disputed and needs to be clarified. In the present study, in a mouse model of cerebral I/R, we examined the activities of NFκB and interferon factor-3 (IRF3), and the inflammatory responses in ischemic brain tissue using ELISA, Western blots, and real-time PCR. Neurological function and cerebral infarct size were also evaluated 24 h after cerebral I/R. Our results showed that NFκB activity increased in ischemic brains, but IRF3 was not activated after cerebral I/R, in wild-type (WT) mice. MyD88 deficit inhibited the activation of NFκB, and the expression of interleukin-1β (IL-1β), IL-6, Beclin-1 (BECN1), pellino-1, and cyclooxygenase-2 (COX-2) increased by cerebral I/R compared with WT mice. Interestingly, the expression of interferon Beta 1 (INFB1) and vascular endothelial growth factor (VEGF) increased in MyD88 KO mice. Unexpectedly, although the neurological function improved in the MyD88 knockout (KO) mice, the deficit of MyD88 failed to reduce cerebral infarct size compared to WT mice. We concluded that MyD88-dependent signaling contributes to the inflammatory responses induced by cerebral I/R. MyD88 deficit may inhibit the increased inflammatory response and increase neuroprotective signaling.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".