Molecular Magnetic Resonance Imaging of Acute Vascular Cell Adhesion Molecule-1 Expression in a Mouse Model of Cerebral Ischemia
Bibliographic record
Abstract
The pathogenesis of stroke is multifactorial, and inflammation is thought to have a critical function in lesion progression at early time points. Detection of inflammatory processes associated with cerebral ischemia would be greatly beneficial in both designing individual therapeutic strategies and monitoring outcome. We have recently developed a new approach to imaging components of the inflammatory response, namely endovascular adhesion molecule expression on the brain endothelium. In this study, we show specific imaging of vascular cell adhesion molecule (VCAM)-1 expression in a mouse model of middle cerebral artery occlusion (MCAO), and a reduction in this inflammatory response, associated with improved behavioral outcome, as a result of preconditioning. The spatial extent of VCAM-1 expression is considerably greater than the detectable lesion using diffusion-weighted imaging (25% versus 3% total brain volume), which is generally taken to reflect the core of the lesion at early time points. Thus, VCAM-1 imaging seems to reveal both core and penumbral regions, and our data implicate VCAM-1 upregulation and associated inflammatory processes in the progression of penumbral tissue to infarction. Our findings indicate that such molecular magnetic resonance imaging (MRI) approaches could be important clinical tools for patient evaluation, acute monitoring of therapy, and design of specific treatment strategies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".