Nanophotonics Enable Targeted Photothermal Silencing of Nociceptor Neurons
Bibliographic record
Abstract
Abstract The sensory nervous and immune systems work in concert to preserve homeostasis. While this endogenous interplay protects from danger, it may drive chronic pathologies. Currently, genetic engineering of neurons remains the primary approach to interfere selectively with this potentially deleterious interplay. However, such manipulations are not feasible in a clinical setting. Here, this work reports a nanotechnology‐enabled concept to silence subsets of unmodified nociceptor neurons that exploits their ability to respond to heat via the transient receptor potential vanilloid type 1 (TRPV1) channel. This strategy uses laser stimulation of antibody‐coated gold nanoparticles to heat‐activate TRPV1, turning this channel into a cell‐specific drug‐entry port. This delivery method allows transport of a charged cationic derivative of an N‐type calcium channel blocker (CNCB‐2) into targeted sensory fibers. CNCB‐2 delivery blocks neuronal calcium currents and neuropeptides release, resulting in targeted silencing of nociceptors. Finally, this work demonstrates the ability of the approach to probe neuro‐immune crosstalk by targeting cytokine‐responsive nociceptors and by successfully preventing nociceptor‐induced CD8+ T‐cells polarization. Overall, this work constitutes the first demonstration of targeted silencing of nociceptor neuron subsets without requiring genetic modification, establishing a strategy for interfering with deleterious neuro‐immune interplays.
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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".