Evolution of Inflammation and White Matter Injury in a Model of Transient Focal Ischemia
Bibliographic record
Abstract
After an ischemic stroke, there is a prolonged inflammatory response and secondary phase of injury that is more amenable to treatment than acute neurotoxicity. Surprisingly, little is known about temporal and spatial relationships between inflammation and white matter injury. Here, we quantified development of white matter damage, inflammation, and a glial limitans at 1, 3, and 7 days after transient ischemia in the rat striatum using immunohistochemistry. Quantitative analysis showed that decreased staining for myelin basic protein and increased staining for damaged myelin basic protein began in the core, coincided with neutrophil infiltration, and progressed outward over time. Axon damage (i.e. accumulation of amyloid precursor protein) began at the edge of the lesion, coinciding with substantial microglia/macrophage activation, and progressed into the core. During the 7 days, activated microglia/macrophages dramatically increased only in the core and edge of the lesion. Detailed spatial analyses revealed that activated microglia/macrophages that surrounded undamaged axon bundles did not express ED1, a marker of phagocytic cells, whereas those inside damaged bundles expressed ED1. These results imply different contributions of neutrophils and microglia/macrophages to white matter injury after ischemic stroke. The distinct localizations of activated microglia/macrophages imply complex signals that regulate their migration toward and infiltration of damaged white matter.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".