Augmenting Collateral Blood Flow during Ischemic Stroke via Transient Aortic Occlusion
Bibliographic record
Abstract
Collateral circulation provides an alternative route for blood flow to reach ischemic tissue during a stroke. Blood flow through the cerebral collaterals is a critical predictor of clinical prognosis after stroke and response to recanalization, but data on collateral dynamics and collateral therapeutics are lacking. Here, we investigate the efficacy of a novel approach to collateral blood flow augmentation to increase collateral circulation by optically recording blood flow in leptomeningeal collaterals in a clinically relevant model of ischemic stroke. Using high-resolution laser speckle contrast imaging (LSCI) during thromboembolic middle cerebral artery occlusion (MCAo), we demonstrate that transiently diverting blood flow from peripheral circulation towards the brain via intra-aortic catheter and balloon induces persistent increases in blood flow through anastomoses between the anterior and middle cerebral arteries. Increased collateral flow restores blood flow in the distal middle cerebral artery segments to baseline levels during aortic occlusion and persists for over 1 hour after removal of the aortic balloon. Given the importance of collateral circulation in predicting stroke outcome and response to treatment, and the potential of collateral flow augmentation as an adjuvant or stand-alone therapy for acute ischemic stroke, this data provide support for further development and translation of collateral therapeutics including transient aortic occlusion.
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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 | 0.000 | 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 | 0.001 |
| 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".