Microglia and macrophages differentially modulate cell death after brain injury caused by oxygen‐glucose deprivation in organotypic brain slices
Why this work is in the frame
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Bibliographic record
Abstract
Macrophage can adopt several phenotypes, process call polarization, which is crucial for shaping inflammatory responses to injury. It is not known if microglia, a resident brain macrophage population, polarizes in a similar way, and whether specific microglial phenotypes modulate cell death in response to brain injury. In this study, we show that both BV2-microglia and mouse bone marrow derived macrophages (BMDMs) were able to adopt different phenotypes after LPS (M1) or IL-4 (M2) treatment in vitro, but regulated cell death differently when added to mouse organotypic hippocampal brain slices. BMDMs induced cell death when added to control slices and exacerbated damage when combined with oxygen-glucose deprivation (OGD), independently of their phenotype. In contrast, vehicle- and M2-BV2-microglia were protective against OGD-induced death. Direct treatment of brain slices with IL-4 (without cell addition) was protective against OGD and induced an M2 phenotype in the slice. In vivo, intracerebral injection of LPS or IL-4 in mice induced microglial phenotypes similar to the phenotypes observed in brain slices and in cultured cells. After injury induced by middle cerebral artery occlusion, microglial cells did not adopt classical M1/M2 phenotypes, suggesting that another subtype of regulatory phenotype was induced. This study highlights functional differences between macrophages and microglia, in response to brain injury with fundamentally different outcomes, even if both populations were able to adopt M1 or M2 phenotypes. These data suggest that macrophages infiltrating the brain from the periphery after an injury may be cytotoxic, independently of their phenotype, while microglia may be protective.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it