Profiling of how nociceptor neurons detect danger – new and old foes
Bibliographic record
Abstract
Abstract The host evolves redundant mechanisms to preserve physiological processing and homeostasis. These functions range from sensing internal and external threats, creating a memory of the insult and generating reflexes, which aim to resolve inflammation. Impairment in such functioning leads to chronic inflammatory diseases. By interacting through a common language of ligands and receptors, the immune and sensory nervous systems work in concert to accomplish such protective functions. Whilst this bidirectional communication helps to protect from danger, it can contribute to disease pathophysiology. Thus, the somatosensory nervous system is anatomically positioned within primary and secondary lymphoid tissues and mucosa to modulate immunity directly. Upstream of this interplay, neurons detect danger, which prompts the release of neuropeptides initiating (i) defensive reflexes (ranging from withdrawal response to coughing) and (ii) chemotaxis, adhesion and local infiltration of immune cells. The resulting outcome of such neuro‐immune interplay is still ill‐defined, but consensual findings start to emerge and support neuropeptides not only as blockers of TH1‐mediated immunity but also as drivers of TH2 immune responses. However, the modalities detected by nociceptors revealed broader than mechanical pressure and temperature sensing and include signals as various as cytokines and pathogens to immunoglobulins and even microRNAs. Along these lines, we aggregated various dorsal root ganglion sensory neuron expression profiling datasets supporting such wide‐ranging sensing capabilities to help identifying new danger detection modalities of these cells. Thus, revealing unexpected aspects of nociceptor neuron biology might prompt the identification of novel drivers of immunity, means to resolve inflammation and strategies to safeguard homeostasis.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".