Characterization of TGFβ signaling during tail regeneration in the leopard Gecko (<i>Eublepharis macularius</i>)
Bibliographic record
Abstract
INTRODUCTION: The transforming growth factor beta (TGFβ)/activin signaling pathway has a number of documented roles during wound healing and is increasingly appreciated as an essential component of multi-tissue regeneration that occurs in amphibians and fish. Among amniotes (reptiles and mammals), less is known due in part to the lack of an appropriate model organism capable of multi-tissue regeneration. The leopard gecko Eublepharis macularius is able to spontaneously, and repeatedly, regenerate its tail following tail loss. We examined the expression and localization of several key components of the TGFβ/activin signaling pathway during tail regeneration of the leopard gecko. RESULTS: We observed a marked increase in phosphorylated Smad2 expression within the regenerate blastema indicating active TGFβ/activin signaling. Interestingly, during early regeneration, TGFβ1 expression is limited whereas activin-βA is strongly upregulated. We also observe the expression of EMT transcription factors Snail1 and Snail2 in the blastema. CONCLUSIONS: Combined, these observations provide strong support for the importance of different TGFβ ligands during multi-tissue regeneration and the potential role of TGFβ/activin-induced EMT programs during this process.
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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) | 0.001 | 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".