Cu immobilized on chitosan-modified iron oxide magnetic nanoparticles: Preparation, characterization and investigation of its anti-lung cancer effects
Bibliographic record
Abstract
Chitosan is a linear polysaccharide and non-toxic bioactive polymer with a wide variety of applications due to its functional properties such as ease of modification, and biodegradability. In this study, a green protocol for supporting of Cu(II) on chitosan-encapsulated magnetic Fe3O4 nanoparticles is described. The morphological and physicochemical features of the material were determined using several advanced techniques like fourier transformed infrared spectroscopy (FT-IR), field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), energy dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), inductively coupled plasma (ICP), vibrating sample magnetometer (VSM) and X-ray photoelectron spectroscopy (XPS). The average diameter of the NPs was approximately 15–25 nm. In addition, the Fe3O/CS/Cu(II) nanocomposite was engaged in biological assays like study of anti-oxidant properties by DPPH mediated free radical scavenging test using BHT as a reference molecule. Thereafter, on having a significant IC50 value in radical scavenging assay, we extended the bio-application of the desired nanocomposite in anticancer study of lung well-differentiated bronchogenic adenocarcinoma, lung moderately differentiated adenocarcinoma, and lung poorly differentiated adenocarcinoma of human lung in-vitro conditions. In the cytotoxicity and anti-human lung studies, the nanocomposite was treated to lung cancer lung well-differentiated bronchogenic adenocarcinoma (HLC-1), lung moderately differentiated adenocarcinoma (LC-2/ad), and lung poorly differentiated adenocarcinoma (PC-14) cell line following MTT assay. The cell viability of malignant lung cell line reduced dose-dependently in the presence of Fe3O/CS/Cu(II) nanocomposite. The recent results suggest that Fe3O/CS/Cu(II) nanocomposite have a suitable anticancer activity against lung cell lines.
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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".