Mild cerebral hypoxia–ischemia produces a sub‐acute transient inflammatory response that is less selective and prolonged after a substantial insult
Bibliographic record
Abstract
Cerebral ischemia initiates various injurious processes including neuroinflammatory responses such as activation of microglia and increases in cytokine and nitric oxide release. Evidence primarily from in vitro studies, indicates that neuroinflammatory effects can be either beneficial or harmful, possibly related to stimulus strength. We investigated using in vivo models, the effect of a mild or substantial cerebral hypoxia-ischemia on: cerebral microglial/macrophage activation (ED1), pro-inflammatory cytokines (tumor necrosis factor-alpha), nitrosative stress (nitrotyrosine) and permanent brain damage. A mild insult produced a transient (1-2 days post) increase in activated microglia/macrophages within subcortical white and not gray matter but transiently increased cytokine or nitrotyrosine expression in cortex and not white matter. There was also prolonged scattered cell death in cortex and white matter over weeks along with loss of myelin/axons and cortical atrophy at 4 weeks post-insult. In contrast, a substantial insult produced white and gray matter necrosis, cyst formation and atrophy, along with increases in tumor necrosis factor and nitrotyrosine staining within both white and gray matter starting at 1-2 days post-insult. Microglial/macrophage staining was increased starting at 1-week post a substantial insult and remained elevated for weeks thereafter. Thus, a transient neuroinflammatory response occurs following a mild insult whereas prolonged scattered cell death occurs for weeks, particularly in white matter. Insult severity also affects the progression of the neuroinflammatory response, which is prolonged after a substantial insult. Effective therapy will need to be customized for insult severity and timing; and, monitoring the injury processes with imaging or biomarkers may help guide treatment.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".