Phenotypic and spatial heterogeneity of brain myeloid cells after stroke is associated with cell ontogeny, tissue damage, and brain connectivity
Bibliographic record
Abstract
Summary Acute stroke causes substantial mortality and morbidity and provokes extensive changes to myeloid immune cell populations in the brain that may be targets for limiting brain damage and enhancing brain repair. The most effective immunomodulatory approaches will require precise manipulation of discrete myeloid cell phenotypes in time and space to avoid harmful effects of indiscriminate neuroimmune perturbation. We sought to define how stroke alters the composition and phenotypes of mononuclear myeloid cells with particular attention to how cell ontogeny and spatial organisation combine to expand myeloid cell diversity across the brain after stroke. Multiple reactive microglial states and dual monocyte-derived populations contributed to an extensive repertoire of myeloid cells in post-stroke brain. We identified important overlap and distinctions among different cell types and states that involved ontogeny- and spatial-related properties. Notably, brain connectivity with infarcted tissue underpinned the pattern of local and remote altered cell accumulation and reactivity. Our discoveries suggest a global but anatomically-governed brain myeloid cell response to stroke that comprises diverse phenotypes arising through intrinsic cell ontogeny factors interacting with exposure to spatially-organised brain damage and neuroaxonal cues.
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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".