Role of perivascular and meningeal macrophages in outcome following experimental subarachnoid hemorrhage
Bibliographic record
Abstract
The distribution and clearance of erythrocytes after subarachnoid hemorrhage (SAH) is poorly understood. We aimed to characterize the distribution of erythrocytes after SAH and the cells involved in their clearance. To visualize erythrocyte distribution, we injected fluorescently-labelled erythrocytes into the prechiasmatic cistern of mice. 10 minutes after injection, we found labelled erythrocytes in the subarachnoid space and ventricular system, and also in the perivascular spaces surrounding large penetrating arterioles. 2 and 5 days after SAH, fluorescence was confined within leptomeningeal and perivascular cells. We identified the perivascular cells as perivascular macrophages based on their morphology, location, Iba-1 immunoreactivity and preferential uptake of FITC-dextran. We subsequently depleted meningeal and perivascular macrophages 2 days before or 3 hours after SAH with clodronate liposomes. At day 5 after SAH, we found increased blood deposition in mice treated prior to SAH, but not those treated after. Treatment post-SAH improved neurological scoring, reduced neuronal cell death and perivascular inflammation, whereas pre-treatment only reduced perivascular inflammation. Our data indicate that after SAH, erythrocytes are distributed throughout the subarachnoid space extending into the perivascular spaces of parenchymal arterioles. Furthermore, meningeal and perivascular macrophages are involved in erythrocyte uptake and play an important role in outcome after SAH.
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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) | 0.001 | 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".