Recent progress in biomedical applications of RGD‐based ligand: From precise cancer theranostics to biomaterial engineering: A systematic review
Bibliographic record
Abstract
Arginine-glycine-aspartic acid (RGD) peptide family is known as the most prominent ligand for extracellular domain of integrin receptors. Specific expression of these receptors in various tissue of human body and tight association of their expression profile with various pathophysiological conditions made these receptors a suitable targeting candidate for several disease diagnosis and treatment as well as regeneration of various organs. For these reasons, various forms of RGD-based integrins ligands have been greatly used in biomedical studies. Here, we summarized the last decade application progress of RGD for cancer theranostics, control of inflammation, thrombosis inhibition and critically discussed the effect of RGD peptides structure and sequence on the efficacy of gene/drug delivery systems in preclinical studies. Furthermore, we will show recent advances in application of RGD functionalized biomaterials for various tissue regenerations including cornea repair, artificial neovascularization and bone tissue regeneration. Finally, we analyzed clinically translatability of RGD peptides, considering examples of integrin ligands in clinical trials. In conclusion, prospects on using RGD peptide for precise drug delivery and biomaterial engineering are well discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".