Microglial Kv1.3 Channels and P2Y12 Receptors Differentially Regulate Cytokine and Chemokine Release from Brain Slices of Young Adult and Aged Mice
Bibliographic record
Abstract
Brain tissue damage following stroke or traumatic brain injury is accompanied by neuroinflammatory processes, while microglia play a central role in causing and regulating neuroinflammation via production of proinflammatory substances, including cytokines and chemokines. Here, we used brain slices, an established in situ brain injury model, from young adult and aged mice to investigate cytokine and chemokine production with particular focus on the role of microglia. Twenty four hours after slice preparation, higher concentrations of proinflammatory cytokines, i.e. TNF-α and IL-6, and chemokines, i.e. CCL2 and CXCL1, were released from brain slices of aged mice than from slices of young adult mice. However, maximal microglial stimulation with LPS for 24 h did not reveal age-dependent differences in the amounts of released cytokines and chemokines. Mechanisms underlying microglial cytokine and chemokine production appear to be similar in young adult and aged mice. Inhibition of microglial Kv1.3 channels with margatoxin reduced release of IL-6, but not release of CCL2 and CXCL1. In contrast, blockade of microglial P2Y12 receptors with PSB0739 inhibited release of CCL2 and CXCL1, whereas release of IL-6 remained unaffected. Cytokine and chemokine production was not reduced by inhibitors of Kir2.1 K+ channels or adenosine receptors. In summary, our data suggest that brain tissue damage-induced production of cytokines and chemokines is age-dependent, and differentially regulated by microglial Kv1.3 channels and P2Y12 receptors.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".