Data set describing the in vitro biological activity of JMV2009, a novel silylated neurotensin(8–13) analog
Bibliographic record
Abstract
Neurotensin (NT) is a tridecapeptide displaying interesting antinociceptive properties through its action on its receptors, NTS1 and NTS2. Neurotensin-like compounds have been shown to exert better antinociceptive properties than morphine at equimolar doses. In this article, we characterized the molecular effects of a novel neurotensin (8–13) (NT(8–13)) analog containing an unnatural amino acid. This compound, named JMV2009, displays a Silaproline in position 10 in replacement of a proline in the native NT(8–13). We first examined the binding affinities of this novel NT(8–13) derivative at both NTS1 and NTS2 receptor sites by performing competitive displacement of iodinated NT on purified cell membranes. Then, we evaluated the ability of JMV2009 to activate NTS1-related G proteins as well as to promote the recruitment of β-arrestins 1 and 2 by using BRET-based cellular assays in live cells. We next assessed its ability to induce p42/p44 MAPK phosphorylation and NT receptors internalization using western blot and cell-surface ELISA, respectively. Finally, we determined the in vitro plasma stability of this NT derivative. This article is associated with the original article “Pain relief devoid of opioid side effects following central action of a silylated neurotensin analog” published in European Journal of Pharmacology [1]. The reader is directed to the associated article for results interpretation, comments, and discussion.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 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) | 0.029 | 0.016 |
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".