Inflammatory Cytokine Profile and Plasticity of Brain and Spinal Microglia in Response to ATP and Glutamate
Bibliographic record
Abstract
Microglia are the primary cells in the central nervous system that identify and respond to injury or damage. Such a perturbation in the nervous system induces the release of molecules including ATP and glutamate that act as damage-associated molecular patterns (DAMPs). DAMPs are detected by microglia, which then regulate the inflammatory response in a manner sensitive to their surrounding environment. The available data indicates that ATP and glutamate can induce the release of pro inflammatory factors TNF (tumor necrosis factor), IL-1β (interleukin 1 beta), and NO (nitric oxide) from microglia. However, non-physiological concentrations of ATP and glutamate were often used to derive these insights. Here, we have compared the response of spinal cord microglia (SM) relative to brain microglia (BM) using physiologically relevant concentrations of glutamate and ATP that mimic injured conditions in the central nervous system. The data show that ATP and glutamate are not significant modulators of the release of cytokines from either BM or SM. Consistent with previous studies, spinal microglia exhibited a general trend toward reduced release of inflammatory cytokines relative to brain-derived microglia. Moreover, we demonstrate that the responses of microglia to these DAMPs can be altered by modifying the biochemical milieu in their surrounding environment. Preconditioning brain derived microglia with media from spinal cord derived mixed glial cultures shifted their release of IL-1ß and IL-6 to a less inflammatory phenotype consistent with spinal microglia.
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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".