Endothelin: 20 years from discovery to therapyThis article is one of a selection of papers published in the special issue (part 2 of 2) on Forefronts in Endothelin.
Bibliographic record
Abstract
Since its identification as an endothelial cell-derived vasoconstrictor peptide in 1988, endothelin-1, the predominant member of the endothelin peptide family, has received considerable interest in basic medical science and in clinical medicine, which is reflected by more than 20 000 scientific publications on endothelin research in the past 20 years. The story of endothelin is unique as the gene sequences of endothelin receptors and the first receptor antagonists became available within only 4 years of the identification of the peptide sequence. The first clinical study in patients with congestive heart failure was published only 3 years thereafter. Yet, despite convincing experimental evidence of a pathogenetic role for endothelin in development, cell function, and disease, many initial clinical studies on endothelin antagonism were negative. In many of these studies, study designs or patient selection were inadequate. Today, for diseases such as pulmonary hypertension, endothelin antagonist treatment has become reality in clinical medicine, and ongoing clinical studies are evaluating additional indications, such as renal disease and cancer. Twenty years after the discovery of endothelin, its inhibitors have finally arrived in the clinical arena and are now providing us with new options to treat disease and prolong the lives of patients. Possible future indications include resistant arterial hypertension, proteinuric renal disease, cancer, and connective tissue diseases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".