Tetrameric far‐red fluorescent protein as a scaffold to assemble an octavalent peptide nanoprobe for enhanced tumor targeting and intracellular uptake <i>in vivo</i>
Bibliographic record
Abstract
Relatively weak tumor affinities and short retention time in vivo hinder the application of targeting peptides in tumor molecular imaging. Multi‐valent strategies based on various scaffolds have been utilized to improve the ability of peptide‐receptor binding or extend the clearance time of peptide‐based probes. Here, we use a tetrameric far‐red fluorescent protein (tfRFP) as a scaffold to create a self‐assembled octavalent peptide fluorescent nanoprobe (Octa‐FNP) using a genetic engineering approach. The multiligand connecting, fluorophore labeling and nanostructure formation of Octa‐FNP were performed in one step. In vitro studies showed Octa‐FNP is a 10‐nm fluorescent probe with excellent serum stability. Cellular uptake of Octa‐FNP by human nasopharyngeal cancer 5‐8F cells is 15‐fold of tetravalent probe, ~80‐fold of monovalent probe and ~ 600‐fold of nulvalent tfRFP. In vivo enhanced tumor targeting and intracellular uptake of Octa‐FNP were confirmed using optical imaging and Western blot analysis. It achieved extremely high contrast of Octa‐FNP signal between tumor tissue and normal organs, especially seldom Octa‐FNP detected in liver and spleen. Owing to easy preparation, precise structural and functional control, and multivalent effect, Octa‐FNP provides a powerful tool for tumor optical molecular imaging and evaluating the targeting ability of numerous peptides in vivo. —Luo, H., Yang, J., Jin, H., Huang, C, Fu, J., Yang, F., Gong, H., Zeng, S., Luo, Q., Zhang, Z. Tetrameric far‐red fluorescent protein as a scaffold to assemble an octavalent peptide nanoprobe for enhanced tumor targeting and intracellular uptake in vivo. FASEB J. 25, 1865‐1873 (2011). www.fasebj.org
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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) | 0.001 | 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".