HDL‐Mimicking Peptide–Lipid Nanoparticles with Improved Tumor Targeting
Bibliographic record
Abstract
Targeted delivery of intracellularly active diagnostics and therapeutics in vivo is a major challenge in cancer nanomedicine. A nanocarrier should possess long circulation time yet be small and stable enough to freely navigate through interstitial space to deliver its cargo to targeted cells. Herein, it is shown that by adding targeting ligands to nanoparticles that mimic high-density lipoprotein (HDL), tumor-targeted sub-30-nm peptide-lipid nanocarriers are created with controllable size, cargo loading, and shielding properties. The size of the nanocarrier is tunable between 10 and 30 nm, which correlates with a payload of 15-100 molecules of fluorescent dye. Ligand-directed nanocarriers targeting epidermal growth factor receptor (EGFR) are confirmed both in vitro and in vivo. The nanocarriers show favorable circulation time, tumor accumulation, and biodistribution with or without the targeting ligand. The EGFR targeting ligand is proved to be essential for the EGFR-mediated tumor cell uptake of the nanocarriers, a prerequisite of intracellular delivery. The results demonstrate that targeted HDL-mimetic nanocarriers are useful delivery vehicles that could open new avenues for the development of clinically viable targeted nanomedicine.
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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".