Bone marrow stem cells have the ability to populate the entire central nervous system into fully differentiated parenchymal microglia
Bibliographic record
Abstract
Pluripotent stem cells can differentiate into a variety of cell types during tissue development and regeneration. However, it is still unclear whether bone marrow-derived stem cells can migrate across the blood-brain barrier in many regions of the central nervous system (CNS) and if these cells can readily differentiate into functional parenchymal microglia. We thus studied the differentiation fate of bone marrow stem cells upon immigration into the CNS. To this end, we systemically transplanted stem cells that express green fluorescent protein (GFP) into lethally irradiated mice and found that these cells immigrated into the brain parenchyma of many regions of the CNS. Nearly all of the infiltrating cells had a highly ramified morphology and colocalized with the microglial marker iba1. Moreover, these cells expressed high levels of the protein CD11c, indicating that microglia of bone marrow origin may be potent antigen presenting cells. These data suggest that microglia of blood origin could activate cells of the adaptive immune system and cause harm to the CNS. Therefore, these results may have great clinical relevance for both immune-derived neuronal disorders and cancer patients undergoing allogeneic hematopoietic stem-cell transplantation.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".