Selective Ablation of Proliferating Microglial Cells Exacerbates Ischemic Injury in the Brain
Bibliographic record
Abstract
Here we report in vivo evidence of a neuroprotective role of proliferating microglial cells in cerebral ischemia. Using transgenic mice expressing a mutant thymidine kinase form of herpes simplex virus driven by myeloid-specific CD11b promoter and ganciclovir treatment as a tool, we selectively ablated proliferating (Mac-2 positive) microglia after transient middle cerebral artery occlusion. The series of experiments using green fluorescent protein-chimeric mice demonstrated that within the first 72 h after ischemic injury, the Mac-2 marker [unlike Iba1 (ionized calcium-binding adapter molecule 1)] was preferentially expressed by the resident microglia. Selective ablation of proliferating resident microglia was associated with a marked alteration in the temporal dynamics of proinflammatory cytokine expression, a significant increase in the size of infarction associated with a 2.7-fold increase in the number of apoptotic cells, predominantly neurons, and a 1.8-fold decrease in the levels of IGF-1. A double-immunofluorescence analysis revealed a approximately 100% colocalization between IGF-1 positive cells and Mac-2, a marker of activated/proliferating resident microglia. Conversely, stimulation of microglial proliferation after cerebral ischemia by M-CSF (macrophage colony stimulating factor) resulted in a 1.9-fold increase in IGF-1 levels and a significant increase of Mac2+ cells. Our findings suggest that a postischemic proliferation of the resident microglial cells may serve as an important modulator of a brain inflammatory response. More importantly, our results revealed a marked neuroprotective potential of proliferating microglia serving as an endogenous pool of neurotrophic molecules such as IGF-1, which may open new therapeutic avenues in the treatment of stroke and other neurological disorders.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".