Relaxin‐like peptides in cancer
Bibliographic record
Abstract
The members of the relaxin-like hormone family, relaxin and INSL3, also known as relaxin-like factor (RLF) or Leydig cell-derived insulin-like factor (LEY-I-L), are implicated in various mechanisms associated with tumor cell growth, differentiation, invasion and neovascularization. The recent discovery of the relaxin receptor LGR7 and the INSL3/relaxin receptor LGR8 has provided evidence of an auto/paracrine relaxin-like action in tumor tissues and enables the elucidation of the cellular pathways involved in the proposed functions of relaxin in tumor biology. Our review summarizes our current knowledge of the expression of relaxin and INSL3 in human neoplastic tissues and discusses the etiological roles of these heterodimeric peptide hormones in cancer. Discussion of possible cellular cascades involved in actions linking relaxin-like peptides and neoplasia include the role of relaxin-like peptides in tumor cell growth and differentiation; the effect of relaxin in stimulating the synthesis of the vasodilatory and tumor cell cytostatic and antiapoptotic molecule, nitric oxide; the potential ability of relaxin to upregulate vascular endothelial growth factor to promote angiogenesis and neovascularization and the concerted fine-tuned action of relaxin on the matrix metalloproteinases on the extracellular matrix to facilitate tumor cell attachment, migration and invasion.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".