Adaptation of plateau frog peptide: From antimicrobial to angiogenic and proliferative functions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it