Neurovascular Reactivity in Tissue Scarring Following Cerebral Ischemia
Bibliographic record
Abstract
Tissue scarring upon cerebral ischemia entails a cascade of multifaceted cellular and molecular mechanisms that govern the remodeling of the neurovascular unit, which integrates neuronal, glial, and vascular functions. The process encompasses inflammation, glial reactivity, vascular reactivity, and neuronal remodeling. In this chapter we cover three major aspects involved in tissue scarring after cerebral ischemia. First, we outline the primary cellular mechanisms underlying glial scar formation, emphasizing on the interactions between astrocytes, microglia, and mural cells, including pericytes and fibroblasts at the injury core and perilesional areas. Next, we address the key routes of extracellular matrix deposition by reactive and fibrogenic cells, including proteoglycans, tenascins, fibronectin, and collagen. Finally, we discuss the promises and challenges of manipulating tissue scarring as a strategy to promote brain structural remodeling and neurological recovery.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".