Infiltration of CCR2+Ly6Chigh Proinflammatory Monocytes and Neutrophils into the Central Nervous System Is Modulated by Nicotinic Acetylcholine Receptors in a Model of Multiple Sclerosis
Bibliographic record
Abstract
Myeloid cells, including proinflammatory monocytes and neutrophils, have important roles in the pathology of multiple sclerosis and its animal model, experimental autoimmune encephalomyelitis (EAE). These cells infiltrate the CNS in the early stages of disease development and contribute to the inflammatory response that is associated with symptom severity. It is thus crucial to identify and understand new mechanisms that can regulate the CNS infiltration of proinflammatory myeloid cells. Nicotinic acetylcholine receptors (nAChRs) have been increasingly studied for their immune-regulatory properties. In this study, we assessed the ability of nicotine, an nAChR ligand, to modulate proinflammatory myeloid cell numbers within the bone marrow, spleen, blood, and CNS of EAE mice. We found that nicotine significantly inhibits the infiltration of proinflammatory monocytes and neutrophils into the CNS at time points where these cells are known to play critical roles in disease pathology. In contrast, nicotine does not affect the expansion of other monocytes. We also show that nicotine exerts these effects by acting on α7 and α9 nAChR subtypes. Finally, mRNA transcript levels for CCL2 and CXCL2, chemokines involved in the chemotaxis of proinflammatory monocytes and neutrophils, respectively, are reduced in the brain of nicotine-treated EAE mice before the massive infiltration of these cells. Taken together, our data provide evidence that nAChRs can regulate proinflammatory cell infiltration into the CNS, which could be of significant value for the treatment of neuroinflammatory disorders.
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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 | 0.000 |
| 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) | 0.001 | 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".