Differential Vulnerability and Response to Injury among Brain Cell Types Comprising the Neurovascular Unit
Bibliographic record
Abstract
The neurovascular unit (NVU) includes multiple different cell types, including neurons, astrocytes, endothelial cells, and pericytes, which respond to insults on very different time or dose scales. We defined differential vulnerability among these cell types, using response to two different insults: oxygen-glucose deprivation (OGD) and thrombin-mediated cytotoxicity. We found that neurons are most vulnerable, followed by endothelial cells and astrocytes. After temporary focal cerebral ischemia in male rats, we found significantly more injured neurons, compared with astrocytes in the ischemic area, consistent with differential vulnerability in vivo. We sought to illustrate different and shared mechanisms across all cell types during response to insult. We found that gene expression profiles in response to OGD differed among the cell types, with a paucity of gene responses shared by all types. All cell types activated genes relating to autophagy, apoptosis, and necroptosis, but the specific genes differed. Astrocytes and endothelial cells also activated pathways connected to DNA repair and antiapoptosis. Taken together, the data support the concept of differential vulnerability in the NVU and suggest that different elements of the unit will evolve from salvageable to irretrievable on different time scales while residing in the same brain region and receiving the same (ischemic) blood flow. Future work will focus on the mechanisms of these differences. These data suggest future stroke therapy development should target different elements of the NVU differently.
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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 | 0.000 |
| 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".