The anti-inflammatory cytokine, interleukin (IL)-10, blocks the inhibatory effect of IL-1 beta on long term potentiation - a role for JNK
Bibliographic record
Abstract
Several effects of the proinflammatory cytokine, interleukin-1? (IL-1?), have been described in the central nervous system, and one area of the brain where marked changes have been reported is the hippocampus. Among these changes are an IL-1?-induced inhibition of long term potentiation (LTP) in perforant path-granule cell synapses and an attenuation of glutamate release in synaptosomes prepared from the hippocampus. Evidence suggests that, at least in circulating cells, the anti-inflammatory cytokine, IL-10, antagonizes certain effects of IL-1. We investigated the effect of IL-10 on IL-1?-induced inhibition of LTP and glutamate release. The evidence presented indicates that IL-1? stimulates the stress-activated protein kinase, c-Jun-activated protein kinase (JNK), and IL-1 receptor-associated kinase, which may explain its inhibitory effect on release and LTP, and that IL-10 reversed the IL-1?-induced stimulation of JNK activity and inhibition of release and LTP. We observed that IL-10 abrogated the stimulatory effect of IL-1? on superoxide dismutase activity and reactive oxygen species production, whereas the H2O2-induced inhibition of LTP was also blocked by IL-10. We present evidence that suggests that the action of IL-10 may be mediated by its ability to induce shedding of the IL-1 type I receptor.
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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) | 0.006 | 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".