Hyaluronic acid-modified selenium nanoparticles for enhancing the therapeutic efficacy of paclitaxel in lung cancer therapy
Bibliographic record
Abstract
Targeted delivery of chemotherapeutics by functionalized nanoparticles exhibits a wonderful prospect for cancer treatment. In this paper, selenium nanoparticles (SeNPs) was linked with hyaluronic acid (HA) to prepare tumor-targeted delivery vehicle HA-SeNPs, and HA-SeNPs was loaded with paclitaxel (PTX) to fabricate functionalized selenium nanoparticles HA-Se@PTX. HA-Se@PTX showed greater uptake in A549 cells in comparison with that in HUVEC, verifying HA-mediated specific uptake of HA-Se@PTX. HA-Se@PTX was capable of entering A549 cells via clathrin-associated endocytosis and showed faster drug release in cancer cell microenvironment in comparison with normal physiological environment. HA-Se@PTX could obviously inhibit the proliferation, migration and invasion of A549 cells and trigger A549 cells apoptosis. Moreover, active targeting functionalized selenium nanoparticles HA-Se@PTX showed greater in vivo antitumor activity compared with free PTX or passive targeting delivery system Se@PTX. In addition, HA-Se@PTX exhibited negligible toxicity on the major organs of mice. In a word, HA-Se@PTX may develop into a valuable nanoscale antitumor drug agent for lung cancer treatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.015 | 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".