Toll‐like receptor (TLR)‐2 and TLR‐4 regulate inflammation, gliosis, and myelin sparing after spinal cord injury
Bibliographic record
Abstract
Activation of macrophages via toll-like receptors (TLRs) is important for inflammation and host defense against pathogens. Recent data suggest that non-pathogenic molecules released by trauma also can trigger inflammation via TLR2 and TLR4. Here, we tested whether TLRs are regulated after sterile spinal cord injury (SCI) and examined their effects on functional and anatomical recovery. We show that mRNA for TLR1, 2, 4, 5, and 7 are increased after SCI as are molecules associated with TLR signaling (e.g. MyD88, NFkappaB). The significance of in vivo TLR2 and TLR4 signaling was evident in SCI TLR4 mutant (C3H/HeJ) and TLR2 knockout (TLR2-/-) mice. In C3H/HeJ mice, sustained locomotor deficits were observed relative to SCI wild-type control mice and were associated with increased demyelination, astrogliosis, and macrophage activation. These changes were preceded by reduced intraspinal expression of interleukin-1beta mRNA. In TLR2-/- mice, locomotor recovery also was impaired relative to SCI wild-type controls and novel patterns of myelin pathology existed within ventromedial white matter--an area important for overground locomotion. Together, these data suggest that in the absence of pathogens, TLR2 and TLR4 are important for coordinating post-injury sequelae and perhaps in regulating inflammation and gliosis after SCI.
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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) | 0.001 | 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".