Optogenetic Silencing of Na<sub>v</sub>1.8-Positive Afferents Alleviates Inflammatory and Neuropathic Pain
Why this work is in the frame
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Bibliographic record
Abstract
We report a novel transgenic mouse model in which the terminals of peripheral nociceptors can be silenced optogenetically with high spatiotemporal precision, leading to the alleviation of inflammatory and neuropathic pain. Inhibitory archaerhodopsin-3 (Arch) proton pumps were delivered to Nav1.8(+) primary afferents using the Nav1.8-Cre driver line. Arch expression covered both peptidergic and nonpeptidergic nociceptors and yellow light stimulation reliably blocked electrically induced action potentials in DRG neurons. Acute transdermal illumination of the hindpaws of Nav1.8-Arch(+) mice significantly reduced mechanical allodynia under inflammatory conditions, while basal mechanical sensitivity was not affected by the optical stimulation. Arch-driven hyperpolarization of nociceptive terminals was sufficient to prevent channelrhodopsin-2 (ChR2)-mediated mechanical and thermal hypersensitivity in double-transgenic Nav1.8-ChR2(+)-Arch(+) mice. Furthermore, prolonged optical silencing of peripheral afferents in anesthetized Nav1.8-Arch(+) mice led to poststimulation analgesia with a significant decrease in mechanical and thermal hypersensitivity under inflammatory and neuropathic conditions. These findings highlight the role of peripheral neuronal inputs in the onset and maintenance of pain hypersensitivity, demonstrate the plasticity of pain pathways even after sensitization has occurred, and support the involvement of Nav1.8(+) afferents in both inflammatory and neuropathic pain. Together, we present a selective analgesic approach in which genetically identified subsets of peripheral sensory fibers can be remotely and optically inhibited with high temporal resolution, overcoming the compensatory limitations of genetic ablations.
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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 | 0.001 | 0.003 |
| 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 it