Abstract 92: Understanding The Impact Of Anesthetics On Ischemic Cortical Damage By Inducing Stroke In Awake, Freely Moving Mice
Bibliographic record
Abstract
Anesthetics such as isoflurane are commonly used to sedate experimental animals during the induction of stroke. However, there is an abundance of data showing that anesthetics modulate brain excitability, blood flow and vascular tone. Given these diverse effects, it begs the question of to what extent do anesthetics modulate ischemic damage and functional outcome? To better understand the effect of isoflurane on stroke outcome, we developed a protocol for inducing photothrombotic cerebral ischemia in an awake, unanesthetized mouse. This protocol involves temporary attachment of a 1mm diameter plastic fiber optic cable to a previously implanted Luer lock headmount for delivery of photoactivating green laser light (532nm, 1100 mW/cm 2 ) for 15 minutes to the forelimb somatosensory cortex. Evidence for compromised forelimb use is observed within 2 minutes of illumination onset. Histological assessment of ischemic damage and behavioural tests of functional outcome are being examined at different times following stroke induction in awake versus 1.5% isoflurane anesthetized mice. Our preliminary results suggest that infarct volume at 1 and 7 days post-stroke is significantly increased in awake relative to anesthetized mice ( 1 day = 3.4mm 3 ± 0.6 vs. 2.03mm 3 ±0.39; 7 days = 2.97mm 3 +/-0.36 vs. 1.97mm 3 +/-0.19, respectively). Consistent with larger infarct volume, mice that were awake during stroke induction show greater and more persistent deficits in sensory function of the affected forepaw. These experiments will enhance our understanding of whether anesthetics modulate ischemic damage. Further, our simple method for inducing stroke in awake, freely moving animals might be useful for future neuroprotective studies that wish to avoid the potentially confounding effects of anesthetics or examine state-dependent modulation of ischemic damage (e.g. sleep, exercise).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".