Robust effects of genetic background on responses to subarachnoid hemorrhage in mice
Bibliographic record
Abstract
Outcome varies among patients with subarachnoid hemorrhage but known prognostic factors explain only a small portion of the variation in outcome. We hypothesized that individual genetic variations influence brain and vascular responses to subarachnoid hemorrhage and investigated this using inbred strains of mice.Subarachnoid hemorrhage was induced in seven inbred and a chromosome 7 substitution strain of mouse. Cerebral blood flow, vasospasm of the middle cerebral artery, and brain injury were assessed. After 48 h of subarachnoid hemorrhage, mice showed significant middle cerebral artery vasospasm that correlated positively with reduction in cerebral blood flow at 45 min. Mice also had increased neuronal injury compared to sham controls; A/J and C57BL/6 J strains represented the most and least severe, respectively. However, brain injury did not correlate with cerebral blood flow reduction at 45 min or with vasospasm at 48 h. Chromosome 7 substitution did not influence the degree of vasospasm or brain injury.Our data suggested that mouse genetic background influences outcome of subarachnoid hemorrhage. Investigations into the genetic factors causing these inter-strain differences may provide insight into the etiology of the brain damage following subarachnoid hemorrhage. These findings also have implications for animal modeling of disease and suggest that genetic differences may also modulate outcome in other cardiovascular diseases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 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".