Efferent signaling along nociceptive peripheral terminals <i>in vivo</i> is enhanced during inflammation
Bibliographic record
Abstract
Abstract Primary nociceptors are essentially characterized as afferent neurons carrying noxious sensory information from the periphery to the CNS. However, the information flow on primary nociceptors is bidirectional. Nociceptor peripheral terminals release a variety of mediators to the target organ in the vicinity of the injured area. These mediators promote sensitization of adjacent sensory neurons, vasodilation, and edema and affect innate and adaptive immunity, leading to hyperalgesia and inflammation that often expands beyond the injured areas. Many theories associate these phenomena with the antidromic action potential propagation along nociceptor terminals; however, the antidromic efferent signaling at the single nociceptor terminals has never been demonstrated. Here, using in vivo calcium imaging from the individual nociceptive terminals innervating the mouse cornea together with a computational approach, we demonstrated that short-lasting activation of a single terminal in vivo was sufficient to activate the remote, non-activated terminal, which branches from the same nociceptor fiber. This increase was dependent on the activation of voltage-gated sodium and calcium channels. Moreover, we showed that the efferent signaling along nociceptive terminals increases under inflammatory conditions, culminating in enhanced calcium signaling in the remote non-activated terminals. This inflammation-induced increase in intra-terminal calcium could trigger the enhanced release of inflammatory mediators, spilling over wider areas and affecting terminals from adjacent unstimulated receptive fields, leading to the expansion of hyperalgesia and inflammation.
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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) | 0.001 | 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".