<i>Tlr4</i>Deletion Modulates Cytokine and Extracellular Matrix Expression in Chronic Spinal Cord Injury, Leading to Improved Secondary Damage and Functional Recovery
Bibliographic record
Abstract
Toll-like receptors (TLRs) play an important role in the innate immune response after CNS injury. Although TLR4 is one of the best characterized, its role in chronic stages after spinal cord injury (SCI) is not well understood. We examined the role of TLR4 signaling in injury-induced responses at 1 d, 7 d, and 8 weeks after spinal cord contusion injury in adult female TLR4 null and wild-type mice. Analyses include secondary damage, a range of transcriptome and protein analyses of inflammatory, cell death, and extracellular matrix (ECM) molecules, as well as immune cell infiltration and changes in axonal sprouting and locomotor recovery. Lack of TLR4 signaling results in reduced neuronal and myelin loss, reduced activation of NFκB, and decreased expression of inflammatory cytokines and necroptotic cell death pathway at a late time point (8 weeks) after injury. TLR4 null mice also showed reduction of scar-related ECM molecules at 8 weeks after SCI, accompanied by increase in ECM molecules associated with perineuronal nets, increased sprouting of serotonergic fibers, and improved locomotor recovery. These findings reveal novel effects of TLR4 signaling in chronic SCI. We show that TLR4 influences inflammation, cell death, and ECM deposition at late-stage post-injury when secondary injury processes are normally considered to be over. This highlights the potential for late-stage targeting of TLR4 as a potential therapy for chronic 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".