Effects of self-assembled cell-penetrating peptides and their nano-complexes on ABCB1 expression and activity.
Bibliographic record
Abstract
OBJECTIVES: Doxorubicin (Dox) is one of the most well-known chemotherapeutics that are commonly applied for a wide range of cancer treatments. However, in most cases, efflux pumps like P-glycoprotein (P-gp), expel the taken drugs out of the cell and decrease the Dox bioavailability. Expression of P-gp is associated with elevated mRNA expression of the ATP-binding cassette B1 (ABCB1) gene. MATERIALS AND METHODS: In the current study, different sequences of cell-penetrating peptides (CPPs) containing tryptophan, lysine, and arginine and their nano-complexes were synthesized and their impact on the expression and activity of the ABCB1 gene was evaluated in the A549 lung carcinoma cell line. Furthermore, the cellular uptake of designed CPPs in the A549 cell line was assessed. RESULTS: The designed peptides, including [W4K4], [WR]3-QGR, R10, and K10 increased Dox cytotoxicity after 48 hr. Furthermore, arginine-rich peptides showed higher cellular uptake. Rhodamin123 accumulation studies illustrated that all the obtained peptides could successfully inhibit the P-gp pump. The designed peptides inhibited the ABCB1 gene expression, of which, [W4K4] resulted in the lowest expression ratio. CONCLUSION: [W4K4], [WR]3-QGR, R10, and K10 could successfully increase the Dox cytotoxicity by decreasing the efflux pump gene expression.
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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".