Theranostic pH-sensitive nanoparticles for highly efficient targeted delivery of doxorubicin for breast tumor treatment
Bibliographic record
Abstract
Abstract: A multifunctional theranostic nanoplatform integrated with environmental responses has been developed rapidly over the past few years as a novel treatment strategy for several solid tumors. We synthesized pH-sensitive poly(β-thiopropionate) nanoparticles with a supermagnetic core and folic acid (FA) conjugation (FA-doxorubicin-iron oxide nanoparticles [ [email protected] ]) to deliver an antineoplastic drug, DOX, for the treatment of folate receptor (FR)-overexpressed breast cancer. In addition to an imaging function, the nanoparticles can release their payloads in response to an environment of pH 5, such as the acidic environment found in tumors. After chemical ( 1 H nuclear magnetic resonance) and physical (morphology and supermagnetic) characterization, [email protected] were shown to demonstrate pH-dependent drug release profiles. Western blotting analysis revealed the expression of FRs in three breast cancer cell lines, MCF-7, BT549, and MD-MBA-231. The cell counting kit-8 assay and transmission electron microscopy showed that [email protected] had the strongest cytotoxicity against breast cancer cells, compared with free DOX and non-FR targeted nanoparticles ( [email protected] ), and caused cellular apoptosis. The [email protected] cellular uptake and intracellular internalization were clarified by fluorescence microscopy. [email protected] plus magnetic field treatment suppressed in vivo tumor growth in mice to a greater extent than either treatment alone; furthermore, the nanoparticles exerted no toxicity against healthy organs. Magnetic resonance imaging was successfully applied to monitor the nanoparticle accumulation. Our results suggest that theranostic pH-sensitive nanoparticles with dual targeting could enhance the available therapies for cancer. Keywords: theranostics, magnetic nanoparticle, pH sensitivity, folate receptor targeting, breast cancer
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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".