Activation of nicotinic acetylcholine receptor prevents the production of reactive oxygen species in fibrillar β amyloid peptide (1-42)-stimulated microglia
Bibliographic record
Abstract
Recent studies have reported that the cholinergic anti-inflammatory pathway regulates peripheral inflammatory responses via alpha7 nicotinic acetylcholine receptors (alpha7 nAChRs) and that acetylcholine and nicotine regulate the expression of proinflammatory mediators such as TNF-alpha and prostaglandin E2 in microglial cultures. In a previous study we showed that ATP released by beta-amyloid-stimulated microglia induced reactive oxygen species (ROS) production, in a process involving the P2X(7) receptor (P2X(7)R), in an autocrine fashion. These observations led us to investigate whether stimulation by nicotine could regulate fibrillar beta amyloid peptide (1-42) (fAbeta1-42)-induced ROS production by modulating ATP efflux-mediated Ca(2+) influx through P2X(7)R. Nicotine inhibited ROS generation in fAbeta(1-42)-stimulated microglial cells, and this inhibition was blocked by mecamylamine, a non-selective nAChR antagonist, and a-bungarotoxin, a selective alpha7 nAChR antagonist. Nicotine inhibited NADPH oxidase activation and completely blocked Ca(2+) influx in fAbeta(1-42)-stimulated microglia. Moreover, ATP release from fAbeta(1-42)-stimulated microglia was significantly suppressed by nicotine treatment. In contrast, nicotine did not inhibit 2',3'-O-(4-benzoyl)-benzoyl ATP (BzATP)-induced Ca(2+) influx, but inhibited ROS generation in BzATP-stimulated microglia, indicating an inhibitory effect of nicotine on a signaling process downstream of P2X(7)R. Taken together, these results suggest that the inhibitory effect of nicotine on ROS production in fAbeta1-42-stimulated microglia is mediated by indirect blockage of ATP release and by directly altering the signaling process downstream from P2X(7)R.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".