Intracerebral Hemorrhage Models in Rat: Comparing Collagenase to Blood Infusion
Bibliographic record
Abstract
Many therapies have shown promise in preclinical stroke studies, but few benefit patients. A greater understanding of stroke pathophysiology is needed to successfully develop therapies, and this depends on appropriate animal models. The collagenase and blood infusion models of intracerebral hemorrhage (ICH) are widely used; yet, investigators often prefer using one model for a variety of reasons. Thus, we directly compared these to highlight advantages and limitations of each as well as the assessment approach. An ICH was created by infusing blood or bacterial collagenase into the rats' striatum. We matched initial hematoma volume in each model (Experiment 1) and assessed the time course of bleeding (Experiment 2). Functional deficits and the progression of injury were tracked over 6 weeks using behavior, magnetic resonance imaging, and histology (Experiment 3). Despite similar initial hematoma volumes, collagenase-induced ICH resulted in a greater blood-brain barrier breakdown and more damage to the striatum, substantia nigra, white matter, and cortex. Magnetic resonance imaging revealed faster hematoma resolution in the blood model, and little increase in the volume of tissue lost from 1 to 6 weeks. In contrast, tissue loss continued over 4 weeks in the collagenase model. Finally, functional deficits recovered more quickly and completely in the blood model. This study highlights key differences between these models and that neither closely replicates the human condition. Thus, both should be used whenever possible taking into account the significant differences between these models and their limitations. Furthermore, this work illustrates significant weaknesses with several outcome measures.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".