Neuroprotective Effect of Subdural Infusion of Serp-1 in Spinal Cord Trauma
Bibliographic record
Abstract
Spinal cord injury (SCI) initiates a severe, destructive inflammation with pro-inflammatory, CD68+/CD163-, phagocytic macrophages infiltrating the area of necrosis and hemorrhage by day 3 and persisting for the next 16 weeks. Inhibition of macrophage infiltration of the site of necrosis that is converted into a cavity of injury (COI) during the first week post-SCI, should limit inflammatory damage, shorten its duration and result in neuroprotection. By sustained subdural infusion we administered Serp-1, a Myxoma virus-derived immunomodulatory protein previously shown to improve neurologic deficits and inhibit macrophage infiltration in the COI in rats with the balloon crush SCI. Firstly, in a 7 day long study, we determined that the optimal dose for macrophage inhibition was 0.2 mg/week. Then, we demonstrated that a continuous subdural infusion of Serp-1 for 8 weeks resulted in consistently accelerated lowering of pro-inflammatory macrophages in the COI and in their almost complete elimination similar to that previously observed at 16 weeks in untreated SCI rats. The macrophage count in the COI is a quantitative test directly related to the severity of destructive inflammation initiated by the SCI. This test has consistently demonstrated anti-inflammatory effect of Serp-1 interpreted as neuroprotection, the first and necessary step in a therapeutic strategy in neurotrauma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".