Nonpeptidic endothelin-converting enzyme inhibitors and their potential therapeutic applications
Bibliographic record
Abstract
Endothelins (ETs) are potent vasoconstrictors, promitogens, and inflammatory mediators. They have been implicated in the pathogenesis of various cardiovascular, renal, pulmonary, and central nervous system diseases. Since the final step of the biosynthesis of ETs is catalyzed by a family of endothelin-converting enzymes (ECEs), inhibitors of these enzymes may represent novel therapeutic agents. Currently, seven isoforms of these metalloproteases have been identified; they all share a significant amino acid sequence identity with neutral endopeptidase 24.11 (NEP), another metalloprotease. Therefore, it is not surprising that the majority of ECE inhibitors also possess potent NEP inhibitory activity. To date, three classes of ECE inhibitors have been synthesized: dual ECE/NEP inhibitors, triple ECE/NEP/ACE inhibitors, and selective ECE inhibitors. Potential clinical applications of these compounds in hypertension, chronic heart failure, restenosis, renal failure, and cerebral vasospasm deduced from studies with relevant animal models are reviewed.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".