Granulocyte macrophage colony‐stimulating factor treatment results in recovery of motor function after white matter damage in mice
Bibliographic record
Abstract
Clinical stroke usually results from a cerebral ischaemic event, and is frequently a debilitating condition with limited treatment options. A significant proportion of clinical strokes result from specific damage to the subcortical white matter (SWM), but currently there are few animal models available to investigate the pathogenesis and potential therapeutic strategies to promote recovery. Granulocyte macrophage colony-stimulating factor (GM-CSF) is a cytokine that has been previously shown to promote neuroprotective effects after brain damage; however, the mechanisms mediating this effect are not known. Here, it is reported that GM-CSF treatment results in dramatic functional improvement in a white matter model of stroke in mice. SWM stroke was induced in mice by unilateral injections of the vasoconstrictor, endothelin-1 (ET-1). The results reveal that ET-1-induced stroke impairs skilled motor function on the single pellet-reaching task and results in forelimb asymmetry, in adult mice. Treatment with GM-CSF, after stroke, restores motor function and abolishes forelimb asymmetry. The results also indicate that GM-CSF promotes its effects by activating mammalian target of rapamycin signalling mechanisms in the brain following stroke injury. Additionally, a significant increase in GM-CSF receptor expression was found in the ipsilateral hemisphere of the ET-1-injected brain. Taken together, the present study highlights the use of an under-utilized mouse model of stroke (using ET-1) and suggests that GM-CSF treatment can attenuate ET-1-induced functional deficits.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".