Cytokine-specific Neurograms in the Sensory Vagus Nerve
Bibliographic record
Abstract
The axons of the sensory, or afferent, vagus nerve transmit action potentials to the central nervous system in response to changes in the body's metabolic and physiological status. Recent advances in identifying neural circuits that regulate immune responses to infection, inflammation and injury have revealed that vagus nerve signals regulate the release of cytokines and other factors produced by macrophages. Here we record compound action potentials in the cervical vagus nerve of adult mice and reveal the specific activity that occurs following administration of the proinflammatory cytokines tumor necrosis factor (TNF) and interleukin 1β (IL-1β). Importantly, the afferent vagus neurograms generated by TNF exposure are abolished in double knockout mice lacking TNF receptors 1 and 2 (TNF-R1/2KO), whereas IL-1β-specific neurograms are eliminated in knockout mice lacking IL-1β receptor (IL-1RKO). Conversely, TNF neurograms are preserved in IL-1RKO mice, and IL-1β neurograms are unchanged in TNF-R1/2KO mice. Analysis of the temporal dynamics and power spectral characteristics of afferent vagus neurograms for TNF and IL-1β reveals cytokine-selective signals. The nodose ganglion contains the cell bodies of the sensory neurons whose axons run through the vagus nerve. The nodose neurons express receptors for TNF and IL-1β, and we show that exposing them to TNF and IL-1β significantly stimulates their calcium uptake. Together these results indicate that afferent vagus signals in response to cytokines provide a basic model of nervous system sensing of immune responses.
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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".