Local Serpin Treatment via Chitosan-Collagen Hydrogel after Spinal Cord Injury Reduces Tissue Damage and Improves Neurologic Function
Bibliographic record
Abstract
Spinal cord injury (SCI) results in massive secondary damage characterized by a prolonged inflammation with phagocytic macrophage invasion and tissue destruction. In prior work, sustained subdural infusion of anti-inflammatory compounds reduced neurological deficits and reduced pro-inflammatory cell invasion at the site of injury leading to improved outcomes. We hypothesized that implantation of a hydrogel loaded with an immune modulating biologic drug, Serp-1, for sustained delivery after crush-induced SCI would have an effective anti-inflammatory and neuroprotective effect. Rats with dorsal column SCI crush injury, implanted with physical chitosan-collagen hydrogels (CCH) had severe granulomatous infiltration at the site of the dorsal column injury, which accumulated excess edema at 28 days post-surgery. More pronounced neuroprotective changes were observed with high dose (100 µg/50 µL) Serp-1 CCH implanted rats, but not with low dose (10 µg/50 µL) Serp-1 CCH. Rats treated with Serp-1 CCH implants also had improved motor function up to 20 days with recovery of neurological deficits attributed to inhibition of inflammation-associated tissue damage. In contrast, prolonged low dose Serp-1 infusion with chitosan did not improve recovery. Intralesional implantation of hydrogel for sustained delivery of the Serp-1 immune modulating biologic offers a neuroprotective treatment of acute 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) | 0.000 | 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".