Inhibition of TLR4 signaling protects mice from sensory and motor dysfunction in an animal model of autoimmune peripheral neuropathy
Bibliographic record
Abstract
Abstract Background While the etiology remains elusive, macrophages and T cells in peripheral nerves are considered as effector cells mediating autoimmune peripheral neuropathy (APN), such as Guillain-Barre syndrome. By recognizing both pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs) signals, TLRs play a central role in the initiation of both innate and adaptive immune responses. In this study, we aimed to understand the involvement of TLR4 in the pathogenesis of APN and explore the potential of TLR4 as a drug target for therapeutic use. Methods APN was induced by a partial ligation on one of the sciatic nerves in B7.2 (L31) transgenic mice which possess a predisposed inflammatory background. APN pathology and neurological function were evaluated on the other non-injured sciatic nerve. Results TLR4 and its endogenous ligand HMGB1 were highly expressed in L31 mice, in circulating immune cells and in peripheral nerves. Enhanced TLR4 signaling was blocked with TAK 242, a selective TLR4 inhibitor, before and after disease onset. Intraperitoneal administration of TAK 242 not only inhibited monocyte, macrophage and CD8 + T cell activation, but also reduced the release of pro-inflammatory cytokines. TAK 242 protected mice from severe myelin and axonal loss, resulting in a remarkable improvement in mouse motor and sensory functions. TAK 242 was effective in alleviating the disease in both preventive and reversal paradigms. Conclusion The study identified the critical contribution of TLR4-mediated macrophage activation in disease course and provided strong evidence to support TLR4 as a useful drug target for treating inflammatory autoimmune neuropathy.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".