Cortical <scp>G</scp>luK1 kainate receptors modulate scratching in adult mice
Bibliographic record
Abstract
Abstract Recent investigations into the mechanisms mediating itch transmission have focused on spinal mechanisms, whereas few studies have investigated the role of the cerebral cortex in itch‐related behaviors. Human imaging studies show that several cortical regions are active in correspondence with itch, including the anterior cingulate cortex (ACC). We present here evidence of cortical modulation of pruritogen‐induced scratching behavior. We combine pharmacological, genetic, and electrophysiological approaches to show that cortical GluK1‐containing kainate (KA) receptors are involved in scratching induced by histamine and non‐histamine‐dependent itching stimuli. We further show that scratching corresponds with enhanced excitatory transmission in the ACC through KA receptor modulation of inhibitory circuitry. In addition, we found that inhibiting GluK1‐containing KA receptors in the ACC also reduced behavioral nociceptive responses induced by formalin. Our results reveal a new role of the cortex in pruritogen‐induced scratching. image Cortical contribution to pruritogen‐induced scratching is shown in an animal model. Through a combination of pharmacological, genetic, and electrophysiological data, we show that cortical GluK1‐containing kainate receptors (KAR) are involved in pruritogen‐induced scratching. Peripheral application of itching stimuli activates excitatory afferents projecting to ACC layer II/III pyramidal neurons, initiating presynaptic glutamate (Glu) release which activates postsynaptic AMPA receptors. Simultaneously, presynaptic Glu activates presynaptic KAR located on inhibitory neurons, thereby modulating evoked GABA release and affecting the attenuation of the postsynaptic response.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".