Poly-arginine-18 peptides do not exacerbate bleeding, or improve functional outcomes following collagenase-induced intracerebral hemorrhage in the rat
Bibliographic record
Abstract
BACKGROUND: Cationic arginine-rich peptides (CARPs) have demonstrated neuroprotective and/or behavioural efficacy in ischemic and hemorrhagic stroke and traumatic brain injury models. Therefore, in this study we investigated the safety and neuroprotective efficacy of the CARPs poly-arginine-18 (R18; 18-mer of arginine) and its D-enantiomer R18D given in the acute bleeding phase in an intracerebral hemorrhage (ICH) model. METHODS: One hundred and fifty-eight male Sprague-Dawley rats received collagenase-induced ICH. Study 1 examined various doses of R18D (30, 100, 300, or 1000 nmol/kg) or R18 (100, 300, 1000 nmol/kg) administered intravenously 30 minutes post-collagenase injection on hemorrhage volume 24 hours after ICH. Study 2 examined R18D (single intravenous dose) or R18 (single intravenous dose, plus 6 daily intraperitoneal doses) at 300 or 1000 nmol/kg commencing 30 minutes post-collagenase injection on behavioural outcomes (Montoya staircase test, and horizontal ladder test) in the chronic post-ICH period. A histological assessment of tissue loss was assessed using a Nissl stain at 28 days after ICH. RESULTS: When administered during ongoing bleeding, neither R18 or R18D exacerbated hematoma volume or worsened functional deficits. Lesion volume assessment at 28 days post-ICH was not reduced by the peptides; however, animals treated with the lower R18D 300 nmol/kg dose, but not with the higher 1000 nmol/kg dose, demonstrated a statistically increased lesion size compared to saline treated animals. CONCLUSION: Overall, both R18 and R18D appeared to be safe when administered during a period of ongoing bleeding following ICH. Neither peptide appears to have any statistically significant effect in reducing lesion volume or improving functional recovery after ICH. Additional studies are required to further assess dose efficacy and safety in pre-clinical ICH studies.
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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 | 0.000 |
| 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".