Quercetin Administration After Spinal Cord Trauma Changes S-100β Levels
Bibliographic record
Abstract
BACKGROUND: It has been shown previously that S-100beta levels in serum correspond with the severity of central nervous system (CNS) trauma. It also has been suggested that S-100beta in CNS tissue is involved in neuroprotection and neuroregeneration. We have previously shown that administration of quercetin results in improved motor function in an animal model of spinal cord trauma. METHODS: Mid-thoracic spinal cord compression injury was produced in adult male Wistar rats. Serum and tissue samples were acquired from quercetin-treated animals (25 micromol/kg) and saline controls at 6, 12 and 24 hours after the trauma. S-100beta levels were measured using a luminometric assay in the damaged tissue and in the serum of the animals. RESULTS: The increase in serum S-100beta levels seen in saline controls after spinal cord trauma was ameliorated in the quercetin-treated animals at all time points, although the difference to saline controls became statistically significant only at 24 hrs after the trauma. Compared to tissue S-100beta levels in healthy animals, values were significantly decreased in saline controls at all three time points, while they were decreased at 6 hrs and increased at both 12 and 24 hrs in quercetin-treated animals. At all three time points tissue S-100beta levels were significantly higher in quercetin-treated animals than in saline controls. CONCLUSIONS: Administration of quercetin results in modification of S-100beta levels in the setting of experimental spinal cord trauma. The kinetic patterns of the S-100beta fluctuations in serum and tissue suggest that post-traumatic administration of quercetin decreases the extent of CNS injury.
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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".