Porcine Model of Cerebral Ischemic Stroke Utilizing Intracortical Recordings for the Continuous Monitoring of the Ischemic Area
Bibliographic record
Abstract
PURPOSE: Our aim was to use intracortical recording to enable the tracking of ischemic infarct development over the first few critical hours of ischemia with a high time resolution in pigs. We employed electrophysiological measurements to obtain quick feedback on neural function, which might be useful for screening, e.g., for the optimal dosage and timing of agents prior to further pre-clinical evaluation. METHODS: Micro-electrode arrays containing 16 (animal 1) or 32 electrodes (animal 2-7) were implanted in the primary somatosensory cortex of seven female pigs, and continuous electrical stimulation was applied at 0.2 Hz to a cuff electrode implanted on the ulnar nerve. Ischemic stroke was induced after 30 min of baseline recording by injection of endothelin-1 onto the cortex adjacent to the micro-electrode array. Evoked responses were extracted over a moving window of 180 s and averaged across channels as a measure of cortical excitability. RESULTS: , mean blood pressure, heart rate, blood oxygenation, or core temperature, which all remained stable throughout the experiment. CONCLUSIONS: The animal model may assist in maturing neuroprotective approaches by testing them in an accessible model of resemblance to human neural and cardiovascular physiology and body size. This would constitute an intermediate step for translating positive results from rodent studies into human application, by more efficiently enabling effective optimization prior to chronic pre-clinical studies in large animals.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".