Evaluation of Cannabinoid Receptor 2 and Metabotropic Glutamate Receptor 1 Functional Responses Using a Cell Impedance–Based Technology
Bibliographic record
Abstract
Recently, new technologies based on biosensors and called label free have been developed. These technologies eliminate the need for using markers and dyes. The authors applied one of these technologies, based on measurement of cell impedance variation, to study the pharmacological profiles of ligands for the cannabinoid receptor 2 (CB2), a Gi-coupled receptor, and for the metabopotropic glutamate receptor 1 (mGluR1), a Gq-coupled receptor. Reference agonists and antagonists/inverse agonists for the 2 receptors were applied to recombinant cell lines and impedance monitored over time. Agonists (JWH133 and CP55940 for CB2; quisqualate, glutamate, 1S-3R-ACPD, and S-3,5-DHPG for mGluR1) triggered a variation of impedance consistent in both potency and efficacy with data obtained using classical assays measuring cAMP or Ca(2+) levels. This effect was not present in the parental nontransfected cell line, confirming specific receptor-mediated response. Application of antagonists (AM630 for CB2; YM298198, SCH1014222, J&J16259685, and CPCCOEt for mGluR1) reduced agonist-induced impedance changes. The only exception was the mGluR1 antagonist BAY367620 that, while active in the Ca(2+) assay, was inactive in the impedance assay. Overall, these results confirm the possibility of using cell impedance-based technology to study the pharmacological profile of ligands acting at G-protein-coupled receptors coupled to different downstream signaling pathways.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".