Apelins, ELABELA, and their derivatives: Peptidic regulators of the cardiovascular system and beyond
Bibliographic record
Abstract
Abstract The apelinergic system emerges as an important regulator of cardiovascular functions via its actions on the heart, vasculature, and kidney. It also possesses additional beneficial properties, via its actions on the pancreas and skeletal muscle, on type 2 diabetes. The apelinergic system distinguishes itself by the presence of two structurally distinct sets of endogenous ligands, the Apelins (–13, −17, and −36) and Elabela, which both activate the apelin (APJ) receptor. In the past decade, numerous peptidic ligands have been used to better understand the structure–activity relationship of apelin (and more recently Elabela), providing important tools to rationalize how ligand modifications impact receptor structure and dynamics as well as its downstream signaling. The recently disclosed structure of the apelin receptor in complex with an analogue of apelin‐17 provides an important tool in this quest. In this review, we first summarize the physiopharmacology of the apelinergic system, then, review existing knowledge on the various ligands of the apelin receptor with an emphasis on peptidic ligands, although small molecules are covered as well. Throughout this work, we tried to integrate existing knowledge of ligands’ pharmacological profiles with structure and signaling profile.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".