Prefrontal Ischemia in the Rat Leads to Secondary Damage and Inflammation in Remote Gray and White Matter Regions
Bibliographic record
Abstract
Secondary damage processes, such as inflammation and oxidative stress, can exacerbate an ischemic lesion and spread to adjacent brain regions. Yet, few studies investigate how regions remote from the infarct could also suffer from degeneration and inflammation in the aftermath of a stroke. To find out to what extent far-remote brain regions are affected after stroke, we used a bilateral endothelin-1-induced prefrontal infarct rat model. Brain regions posterior to the prefrontal cortical infarct were analyzed for ongoing neurodegeneration using FluoroJadeB (FJB) and for neuroinflammation using Iba1 and OX-6 immunohistochemistry 28 days post-stroke. The FJB-positive dorsomedial nucleus of the thalamus (DMN) and retrosplenial area (RSA) of the cortex displayed substantial neuroinflammation. Significant neuronal loss was only observed within the cortex. Significant microglia recruitment and activation in the FJB-positive internal capsule indicates remote white matter pathology. These findings demonstrate that even regions far remote from an infarct are affected predictably based on anatomical connectivity, and that white matter inflammation is an integral part of remote pathology. The delayed nature of this pathology makes it a valid target for preventative treatment, potentially with an extended time window of opportunity for therapeutic intervention using anti-inflammatory agents.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".