Effects of relaxin peptides on the release of pro-inflammatory cytokines in oxygen and glucose deprived brain tissue
Bibliographic record
Abstract
Stroke is the third leading cause of death in Canada. At least two relaxin peptides are now known to protect heart, kidney, liver, pancreas, and brain tissues during ischemia through anti-inflammatory, vasodilatory, angiogenic, and anti-fibrotic actions. This makes relaxin a promising candidate as a stroke therapeutic. Previous work in our lab showed that in cultured brain slices exposed to oxygen and glucose-deprivation (OGD, mimicking conditions of ischemic stress), two relaxin peptides reduce cell death. Evidence also suggests that this protection may be mediated through a reduction in inflammation. Pro-inflammatory cytokines are signalling molecules that initiate the inflammatory response after ischemia. Therefore, it was my hypothesis that a relaxin peptide treatment would decrease cell death in cultured brain slices through a reduction of pro-inflammatory cytokines. To test this hypothesis, neonatal rat brains were sectioned, plated, and cultured for two weeks. Subsequently, slices were treated with one of three media conditions: normoxic glucose control, oxygen and glucose-deprived control, or oxygen glucose deprivation with one of three relaxin treatments (100 pM): human relaxin-2, human relaxin-3, or relaxin-3/insulin-5 (a relaxin-3receptor agonist). After treatment for 1 hour, slices were laced back into culture for 1 or 6 hours. Subsequently, slices were flash frozen and stored at -80° C. Protein was isolated and assayed for interleukin-1 beta and tumour necrosis factor alpha. Although relaxin peptides did not significantly reduce cytokine levels in OGD slices, the data suggest that relaxin-3 and relaxin-3/insulin-5 treatments may have reduced levels of the tested cytokines at a higher dosage. Further research is required to determine if relaxin peptides affect the release of pro-inflammatory cytokines in cultured brain tissue.
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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".