Live Imaging of Neuroinflammation Reveals Sex and Estrogen Effects on Astrocyte Response to Ischemic Injury
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: We sought to develop a model system for live analysis of brain inflammatory response in ischemic injury. METHODS: Using a reporter mouse-expressing luciferase gene under transcriptional control of the murine glial fibrillary acidic protein (GFAP) promoter (GFAP-luc mice) and biophotonic/bioluminescent imaging as tools, we developed a model system for in vivo analysis of astrocyte activation/response in cerebral ischemia. RESULTS: Analysis of photon emissions from the brains of living animals revealed marked sex differences in astrocyte response to ischemic injury. The increase in GFAP signals was significantly higher in female mice in the metestrus/diestrus period compared with male transgenic mice (1.71 x 10(7)+/-0.19 x 10(7) vs 0.92 x 10(7)+/-0.15 x 10(7), P<0.001). Similar results were obtained by quantitative immunohistochemistry (males vs females: 13.4+/-0.5 vs 16.96+/-0.64, P<0.0001). However, astrocyte activation/GFAP signals showed cyclic, estrus-dependent variations in response to ischemic injury. Physiologically higher levels of estrogen and application of pharmacologic doses of estrogen during replacement therapy attenuated GFAP upregulation after stroke. Interestingly, contrary to a positive correlation between the intensities of GFAP signals and infarct size in male mice, no such correlation was observed in any of the experimental groups of female GFAP-luc mice. CONCLUSIONS: Our results suggest that GFAP upregulation in ischemic injury may have different functional significance in female and male experimental animals and may not directly reflect the extent of ischemia-induced neuronal damage in female GFAP-luc mice. Using a novel live imaging approach, we demonstrated that the early-phase brain inflammatory response to ischemia may be associated with sex-specific biomarkers of brain damage.
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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".