Bibliographic record
Abstract
Relaxin, a two-chain peptide from the insulin family that regulates the turnover of connective tissue, is being developed by Connetics and Celltech for the potential treatment of infertility and labor complications. It was also under development by the companies for scleroderma, but development has been discontinued for this indication due to disappointing results in its phase III trial [385058,385485]. In addition, Connetics is conducting preclinical studies to evaluate the potential use of relaxin for the treatment of organ fibrosis and infertility [311269]. Relaxin may also have potential for development in the treatment of other connective tissue diseases. In October 2000, preclinical results assessing the ability of relaxin to selectively induce angiogenesis were published [387968]. The results showed relaxin induced significantly more blood vessel growth at ischemic sites compared to animals treated with vehicle alone and, when administered systemically, it did not cause an increase in VEGF in non-wound cells. The data suggest that relaxin may be useful in the treatment of ischemic conditions [387968]. Connetics presented data at the Wound Healing Society 2000 meeting in Toronto, which suggested that relaxin could be an effective treatment for non-healing ulcers as it improves blood flow to oxygen-deprived tissue. The presented research demonstrated that relaxin stimulated wound healing when administered to animals with impaired blood flow, or a genetic defect rendering them diabetic [370335].
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.012 |
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".