Adaptation of plateau frog peptide: From antimicrobial to angiogenic and proliferative functions
Bibliographic record
Abstract
INTRODUCTION: Amphibian skin peptides, particularly defensins, play important roles in environmental adaptation, but often exhibit functional redundancy. SC17-2, a novel peptide from the high altitude frog Nanorana parkeri, exhibits unique angiogenesis and cell migration promoting activities, allowing adaptation to the extreme environment of the Tibetan Plateau with high UV radiation and low microbial diversity. OBJECTIVES: This study aimed to investigate the adaptive role of SC17-2 in high-UV environments, its functional differences from typical defensins, and its potential biomedical applications in wound healing and angiogenesis. METHODS: Bioinformatics analyses, including sequence alignment and ancestral reconstruction, identified positively selected amino acid sites in SC17-2. Molecular docking examined its interaction with the epidermal growth factor receptor (EGFR). In vitro and in vivo experiments, using mouse and zebrafish models, assessed its wound healing and angiogenic properties. RESULTS: SC17-2 exhibited no antimicrobial activity, but it demonstrated antioxidant activity and potent wound healing and angiogenic properties. Molecular docking indicated that SC17-2 interacts with EGFR, potentially activating downstream signalling pathways. In vivo experiments showed that SC17-2 significantly accelerated wound healing by promoting collagen regeneration and angiogenesis, in some aspects outperforming VEGF. CONCLUSION: SC17-2 represents a unique functional divergence in amphibian peptides, driven by ecological adaptation rather than microbial pressure. Its ability to promote angiogenesis and cell migration highlights its potential as a novel therapeutic agent for regenerative medicine, shaped by the extreme conditions of the Tibetan Plateau.
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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 | 0.000 | 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) | 0.002 | 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".