The collagenase model of intracerebral hemorrhage in awake, freely moving animals: The effects of isoflurane
Bibliographic record
Abstract
Intracerebral hemorrhage (ICH) is a devastating stroke often modelled in rats. Isoflurane anesthetic, commonly used in preclinical research, affects general physiology (e.g., blood pressure) and electrophysiology (e.g., burst suppression) in many ways. These physiological changes may detract from the clinical relevance of the model. Here, we revised the standard collagenase model to produce an ICH in rats without anesthetic. Guide cannulas were implanted stereotaxically under anesthetic. After 3 days of recovery, collagenase was infused through an internal cannula into the striatum of animals randomly assigned to the non-anesthetized or isoflurane group. We assessed whether isoflurane affected hematoma volume, core temperature, movement activity, pain, blood pressure, and seizure activity. With a small ICH, there was a hematoma volume increased from 8.6 (±3.3, 95% confidence interval) µL in anesthetized rats to 13.2 (±3.1) µL in non-anesthetized rats (P = 0.008), but with a larger ICH, hematoma volumes were similar. Isoflurane decreased temperature by 1.3 °C (±0.16 °C, P < 0.001) for 2 h and caused a 35.1 (±1.7) mmHg group difference in blood pressure (P < 0.007) for 12 m. Blood glucose increased twofold after isoflurane procedures (P < 0.001). Pain, as assessed with the rat grimace scale, did not differ between groups. Seizure incidence rate (62.5%) in non-anesthetized ICH rats was similar to historic amounts (61.3%). In conclusion, isoflurane appears to have some significant and injury size-dependent effects on the collagenase model. Thus, when anesthetic effects are a known concern, the use of the standardized cannula infusion approach is scientifically and ethically acceptable.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".