Teasing Out the Interplay Between Natural Killer Cells and Nociceptor Neurons
Bibliographic record
Abstract
Somatosensory neurons have evolved to detect noxious stimuli and activate defensive reflexes. By sharing means of communication, nociceptor neurons also tune host defenses by controlling the activity of the immune system. The communication between these systems is mostly adaptive, helping to protect homeostasis, it can also lead to, or promote, the onset of chronic diseases. Both systems co-evolved to allow for such local interaction, as found in primary and secondary lymphoid tissues and mucosa. Recent studies have demonstrated that nociceptors directly detect and respond to foreign antigens, immune cell-derived cytokines, and microbes. Nociceptor activation not only results in pain hypersensitivity and itching, but lowers the nociceptor firing threshold, leading to the local release of neuropeptides. The peptides that are produced by, and released from, the peripheral terminals of nociceptors can block the chemotaxis and polarization of lymphocytes, controlling the localization, duration, and type of inflammation. Recent evidence shows that sensory neurons interact with innate immune cells via cell-cell contact, for example, engaging group 2D (NKG2D) receptors on natural killer (NK) cells. Given that NK cells express the cognate receptors for various nociceptor-produced mediators, it is conceivable that nociceptors use neuropeptides to control the activity of NK cells. Here, we devise a co-culture method to study nociceptor neuron-NK cell interactions in a dish. Using this approach, we found that lumbar nociceptor neurons decrease NK cell cytokine expression. Overall, such a reductionist method could be useful to study how tumor-innervating neurons control the anticancer function of NK cells and how NK cells control the elimination of injured neurons.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".