Toll-Like Receptor 2 Signaling in Response to Brain Injury: An Innate Bridge to Neuroinflammation
Bibliographic record
Abstract
Reactive gliosis is a prominent feature of neurodegenerative and neuroinflammatory disease in the CNS, yet the stimuli that drive this response are not known. There is growing appreciation that signaling through Toll-like receptors (TLRs), which is key to generating innate responses to infection, may have pathogen-independent roles. We show that TLR2 was selectively upregulated by microglia in the denervated zones of the hippocampus in response to stereotactic transection of axons in the entorhinal cortex. In mice lacking TLR2, there were transient, selective reductions in lesion-induced expression of cytokines and chemokines. Recruitment of T cells, but not macrophages, was delayed in TLR2-deficient mice, as well as in mice lacking TNFR1 (tumor necrosis factor receptor 1). TLR2 deficiency also affected microglial proliferative expansion, whereas all of these events were unaffected in TLR4-mutant mice. Consistent with the fact that responses in knock-out mice had all returned to wild-type levels by 8 d, there was no evidence for effects on neuronal plasticity at 20 d. These results identify a role for TLR2 signaling in the early glial response to brain injury, acting as an innate bridge to neuroinflammation.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".