Catheter based selective hypothermia reduces stroke volume during focal cerebral ischemia in swine
Bibliographic record
Abstract
BACKGROUND: Total body hypothermia is an established neuroprotectant in global cerebral ischemia. The role of hypothermia in acute ischemic stroke remains uncertain. Selective application of hypothermia to a region of focal ischemia may provide similar protection with more rapid cooling and elimination of systemic side effects. We studied the effect of selective endovascular cooling in a focal stroke model in adult domestic swine. METHODS: After craniotomy under general anesthesia, a proximal middle cerebral artery branch was occluded for 3 h, followed by 3 h of reperfusion. In half of the animals, selective hypothermia was induced during reperfusion using a dual lumen balloon occlusion catheter placed in the ipsilateral common carotid artery. Following reperfusion, the animals were sacrificed. Brain MRI and histology were evaluated by experts who were blinded to the intervention. RESULTS: 25 animals were available for analysis. Using selective hypothermia, hemicranial temperature was successfully cooled to a mean of 26.5 °C. Average time from start of perfusion to attainment of moderate hypothermia (<30 °C) was 25 min. Mean MRI stroke volumes were significantly reduced by selective cooling (0.050±0.059 control, 0.005±0.011 hypothermia (ratio stroke:hemisphere volume) (p=0.046). Stroke pathology volumes were reduced by 42% compared with controls (p=0.256). CONCLUSIONS: Selective moderate hypothermia was rapidly induced using endovascular techniques in a clinically realistic swine stroke model. A significant reduction in stroke volume on MRI was observed. Endovascular selective hypothermia can provide neuroprotection within time frames relevant to acute ischemic stroke treatment.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".