Cell‐penetrating peptides: Nanocarrier for macromolecule delivery in living cells
Bibliographic record
Abstract
Novel classes and applications of cell-penetrating peptides (CPPs) are being constantly discovered since they were first identified 2 decades ago. These short cationic peptides (nanomolecules) either by covalent binding or by noncovalent binding can traverse cell membranes and deliver a variety of molecules that are unable to overcome the permeability barrier in their own capacity. The ability of the CPPs to deliver variety of macromolecules, such as oligonucleotides, therapeutic drugs, proteins, and medical imaging agents, by forming nanoparticulate carriers in a range of cells has led them to emerge as a potential tool for both macromolecule delivery application and to gain insight into the fundamentals of mechanism of cellular uptake across the plasma membrane. This review explores the recent advances, challenges, and future prospects in the field of CPP-mediated cargo delivery in mammalian and plant cells. Studies have been conducted into the peptide chemistry and stability of CPP-macromolecular complexes. Most of the CPPs have been shown to be nontoxic and do not interfere with the functionality of the macromolecules delivered across the cell membrane. The mechanism of uptake of CPP-cargo complexes and the uptake of CPPs alone across the plasma membrane remains unresolved. As the world of CPPs is rapidly advancing in both mammalian and plant system, there is a promising future for the various applications of transduction and transfection into intact cells.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".