Longitudinal Assessment of Imatinib’s Effect on the Blood–Brain Barrier After Ischemia/Reperfusion Injury with Permeability MRI
Bibliographic record
Abstract
Acute ischemic stroke (AIS) often results in degeneration of the blood-brain barrier (BBB), which can lead to vasogenic edema and an increased risk of intracerebral hemorrhage. Imatinib is an agent that may be able to protect the BBB and reduce the risk of the harmful consequences of BBB degeneration. We sought to measure the effect of Imatinib on the BBB after experimental stroke longitudinally in vivo with permeability dynamic contrast-enhanced MRI. Ischemia/reperfusion injury was induced with a transient middle cerebral artery occlusion surgery. Rats were given Imatinib at 2 and 20 h after stroke onset. Post-assessment included neurologic functioning, MR imaging, Evans Blue extravasation, Western blot, and immunohistology assay. Imatinib protected the BBB by 24 h but failed to decrease BBB permeability at an earlier time-point. Imatinib also reduced infarct volume, edema, and improved neurologic functioning by 24 h. Rats treated with Imatinib also had a higher expression of the BBB structural protein Zona ocludens-1 and a reduction in nuclear factor-kappa beta (NF-κβ) activation. Imatinib is a promising agent to protect the BBB after AIS, but its effect on the BBB may not become prominent until 24 h after the onset of ischemia. This finding may help elucidate Imatinib's role in the clinical management of AIS and influence future study designs.
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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 | 0.001 |
| 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".