A self-assembling peptide reduces glial scarring, attenuates post-traumatic inflammation and promotes neurological recovery following spinal cord injury
Bibliographic record
Abstract
The pathophysiology of spinal cord injury (SCI) involves post-traumatic inflammation and glial scarring which interfere with repair and recovery. Self-assembling peptides (SAPs) are molecules designed for tissue engineering. Here, we tested the performance of K2(QL)6K2 (QL6), a SAP that attenuates inflammation and glial scarring, and facilitates functional recovery. We injected QL6 into the spinal cord tissue of rats 24 h after clip compression SCI. QL6 led to a significant reduction in post-traumatic apoptosis, inflammation and astrogliosis. It also resulted in significant tissue preservation as determined by quantitative histomorphometry. Furthermore, QL6 promoted axonal preservation/regeneration, demonstrated by BDA anterograde and Fluorogold retrograde tracing. In vitro experiments found that a QL6 scaffold enhanced neuronal differentiation and suppressed astrocytic development. The electrophysiology confirmed that QL6 led to significant functional improvement of axons, including increased conduction velocity, reduced refractoriness and enhanced high-frequency conduction. These neuroanatomical and electrophysiological improvements were associated with significant neurobehavioral recovery as assessed by the Basso-Beattie-Bresnahan technique. As the first detailed examination of the pathophysiological properties of QL6 in SCI, this work reveals the therapeutic potential of SAPs, and may suggest an approach for the reconstruction of the injured spinal cord.
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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) | 0.000 | 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 | 0.000 |
| 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".