A strategy for photothermal conversion of polymeric nanoparticles by polyaniline for smart control of targeted drug delivery
Bibliographic record
Abstract
The near-infrared (NIR)-mediated novel strategy to control the drug release from nanocarriers has developed rapidly in recent decades. Polyaniline as a non-cytotoxic and electroactive material for studying cellular proliferation has attracted great attention in recent years. In the present work, polyaniline-mediated polymeric nanoparticles were developed to target the delivery of cisplatin and release it in a controllable way. The prepared polyaniline nanoparticles displayed a size of 90 ± 1.0 nm, a favorable morphology in water, and could be targeted to tumors through the high affinity between trastuzumab and the overexpressed Her2 in tumor cells. In addition, the developed nanoparticles demonstrated exciting photothermal conversion efficiency induced by NIR light and achieved significant cell inhibition efficiency (93.97%) in vitro when exposed to an 808 nm NIR laser with the power of 1.54 W for 5 min. Therefore, the developed external control release delivery system with excellent specificity and high cytotoxicity exhibited great potential in cell research and our research demonstrated that the polyaniline also has potential in the application of photothermal conversion in biomedicine.
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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 | 0.001 |
| 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".