Chronic exposure of sensory neurones to increased levels of nerve growth factor modulates CB<sub>1</sub>/TRPV1 receptor crosstalk
Bibliographic record
Abstract
BACKGROUND: Anandamide (AEA) activates both cannabinoid CB(1) and TRPV1 receptors, which are expressed on cultured dorsal root ganglion neurones. Increased levels of nerve growth factor (NGF) are associated with chronic pain states. EXPERIMENTAL APPROACH: The aim of this study was to compare of the effects of AEA on CB(1) receptor signalling and TRPV1-CB(1) crosstalk in low and high concentrations of NGF, using voltage-clamp electrophysiology and Fura-2 calcium imaging. KEY RESULTS: Chronic exposure to high NGF (200 ng ml(-1)) as compared to low NGF (20 ng ml(-1)) increases the proportion of neurones that exhibit an inward current in response to AEA (1 microM), from 7 to 29%. In contrast, inhibition of voltage-gated calcium currents by AEA is not significantly different in low NGF (33+/-9%, compared to high NGF 28+/-6%). Crosstalk between CB and TRPV1 receptors is modulated by exposure to high NGF. In low NGF, exposure to the CB(1) receptor antagonist, SR141716A, (100 nM) increases the percentage of neurones in which AEA elicits an increase in [Ca(2+)](i), from 10 to 23%. In high NGF, the antagonist does not alter the percentage of responders (33 to 30%). In low NGF, exposure to the CB receptor agonist, WIN55 (1 microM) reduces capsaicin-mediated increases in [Ca(2+)](i) to 28+/-8% of control as compared to an enhancement to 172+/-26% of control observed in high NGF. CONCLUSIONS AND IMPLICATIONS: We conclude that cannabinoid-mediated modulation of TRPV1 receptor activation is altered after exposure to high NGF.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".