Relaxin Peptide Hormones Are Protective During the Early Stages of Ischemic Stroke in Male Rats
Bibliographic record
Abstract
The pregnancy hormone relaxin protects tissue from ischemic damage. The ability of relaxin-3, a relaxin paralog, to do so has not been explored. The cerebral expression levels of these peptides and their receptors make them logical targets for study in the ischemic brain. We assessed relaxin peptide-mediated protection, relative relaxin family peptide receptor (RXFP) involvement, and protective mechanisms. Sprague-Dawley rats receiving permanent (pMCAO) or transient middle cerebral artery occlusions (tMCAO) were treated with relaxin peptides, and brains were collected for infarct analysis. Activation of the endothelial nitric oxide synthase pathway was evaluated as a potential protective mechanism. Primary cortical rat astrocytes were exposed to oxygen glucose deprivation and treated with relaxin peptides, and viability was examined. Receptor involvement was explored using RXFP3 antagonist or agonist treatment and real-time PCR. Relaxin and relaxin-3 reduced infarct size after pMCAO. Both peptides activated endothelial nitric oxide synthase. Because relaxin-3 has not previously been associated with this pathway and displays promiscuous RXFP binding, we explored the receptor contribution. Expression of rxfp1 was greater than that of rxfp3 in rat brain, although peptide binding at either receptor resulted in similar overall protection after pMCAO. Only RXFP3 activation reduced infarct size after tMCAO. In astrocytes, rxfp3 gene expression was greater than that of rxfp1. Selective activation of RXFP3 maintained astrocyte viability after oxygen glucose deprivation. Relaxin peptides are protective during the early stages of ischemic stroke. Differential responses among treatments and models suggest that RXFP1 and RXFP3 initiate different protective mechanisms. This preliminary work is a pivotal first step in identifying the clinical implications of relaxin peptides in ischemic stroke.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".