Impacts of designed vanillic acid-polymer-magnetic iron oxide nanocomposite on breast cancer cells
Bibliographic record
Abstract
The engineered nano-vehicle was constructed using magnetic iron oxide nanoparticles (MIONs) and chitosan (CTS) to stabilize anticancer agent vanillic acid (VNA) which was loaded on CTS-coated MIONs nanocarrier, and more importantly, to achieve sustained VNA release and subsequent proper anticancer activity. The new thermally stable VNA-CTS- MIONs nanocomposite was spherical with a middle diameter of 6 nm and had a high drug loading of about 11.8 %. The MIONs and resulting nanocomposite were composed of pure magnetite and therefore, were superparamagnetic with saturation magnetizations of 53.3 and 45.7 emu.g −1 , respectively. The release profiles of VNA from VNA-CTS-MIONs nanocomposite in different pH values were sustained and showed controlled pH-responsive delivery of the loaded VNA with 89 % and 74 % percentage release within 2354 and 4046 min at pH 5 and 7.4, respectively, as well as were in accordance with the pseudo-second-order model. The VNA-CTS-MIONs nanocomposite treatment at diverse concentrations remarkably decreased the viability and promoted ROS accumulation and apoptosis in the MDA-MB-231 breast cancer cells. Hence, it can be a propitious candidate for the management of breast cancer in the future.
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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) | 0.000 | 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".