Regeneration neurohormones and growth factors in echinoderms
Bibliographic record
Abstract
There has been much recent interest in the presence and biological functions of growth regulators in invertebrates. In spite of the different distribution patterns of these molecules in different phyla (from molluscs, insects, and annelids to echinoderms and tunicates), they seem always to be extensively involved in developmental processes, both embryonic and regenerative. Echinoderms are well known for their striking regenerative potential and many can completely regenerate arms that, for example, are lost following self-induced or traumatic amputation. Thus, they provide a valuable experimental model for the study of regenerative processes from the macroscopic to the molecular level. In crinoids as well as probably all ophiuroids, regeneration is rapid and occurs by means of a mechanism that involves blastema formation, known as epimorphosis, where the new tissues arise from undifferentiated cells. In asteroids, morphallaxis is the mechanism employed, replacement cells being derived from existing tissues following differentiation and (or) transdifferentiation. This paper focuses on the possible contribution of neurohormones and growth factors during both repair and regenerative processes. Three different classes of regulatory molecules are proposed as plausible candidates for growth-promoting factors in regeneration: neurotransmitters (monoamines), neuropeptides (substance P, SALMFamides 1 and 2), and growth-factor-like molecules (TGF-β (transforming growth factor β), NGF (nerve growth factor), RGF-2 (basic fibroblast growth factor)).
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".