Pathological Changes in Microvascular Morphology, Density, Size and Responses Following Comorbid Cerebral Injury
Bibliographic record
Abstract
Aberrations in brain microcirculation and the associated increase in blood-brain-barrier (BBB) permeability in addition to neuroinflammation and Aβ deposition observed in Alzheimer's disease (AD) and ischemia have gained considerable attention recently. However, the role of microvascular homeostasis as a pathogenic substrate to disturbed microperfusion as well as an overlapping etiologic mechanism between AD and ischemia has not been thoroughly explored. In this study, we employ temporal histopathology of cerebral vasculature in a rat model of β-amyloid (Aβ) toxicity and endothelin-1 induced-ischemia (ET1) to investigate the panorama of cerebral pathology and the protein expression on d1, d7, and d28 post-injury. The combination of Aβ and ET1 pathological states leads to an alteration in microvascular anatomy, texture, diameter, density, and protein expression, in addition to disturbed vessel-matrix-connections, inter-compartmental water exchange and basement membrane profile within the lesion epicenter localized in the striatum of Aβ+ET1 brains compared to Aβ and ET1 rats. We conclude that the neural microvascular network, in addition to the neural tissue, is not only sensitive to structural deterioration but also serves as an underlying vascular etiology between ischemia and AD pathologies. Such investigation can provide prospects to appreciate the interrelationships between structure and responses of cerebral microvasculature and to provide a venue for vascular remodeling as a new treatment strategy.
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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 | 0.000 |
| 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".