The combination of deferoxamine and minocycline strengthens neuroprotective effect on acute intracerebral hemorrhage in rats
Bibliographic record
Abstract
Objectives: Intracerebral hemorrhage (ICH) is a devastating type of strokes that carries high mortality rates, but effective therapeutic options are still lacking. Here, the adult rat model of ICH was used to investigate the efficacy of a combinational therapy of deferoxamine (DFX) and minocycline.Methods: The ICH was induced by stereotaxic infusion of collagenase into striatum of adult rats. After the induction of ICH, rats were treated with intraperitoneal injection of deferoxamine (50 mg/kg), minocycline (45 mg/kg), or both agents, at 2 hours after ICH and then every 12 hours for up to 3 days. The vehicle group were treated with phosphate-buffered saline (PBS) only. Rats were killed at 1, 2, and 3 day(s) for examination of iron deposition, neuronal death, neurological deficits, the area of brain damage, activation of microglia/macrophages.Results: Our data revealed that the systemic administration of DFX and/or minocycline decreased iron accumulation. And immunofluorescence staining results indicated that drug-treated group significantly decreased the neuronal degeneration, the number of activated microglia/macrophages and the amount of cell death after ICH. In addition, neurological deficits caused by ICH were improved in the presence of DFX and/or minocycline compare with vehicle group. Furthermore, the combination treatment showed better effects in neuroprotection and anti-inflammation when compared to the monotherapy groups.Conclusions: The combination therapy significantly reduces the number of neuronal deaths, suppresses of the activation of microglia/macrophages, decreases iron accumulation in the area around the hematoma, lessening the brain damage area, and improving neurological deficits in ICH.
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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".