Hesperidin Loaded on Gold Nanoparticles as a Drug Delivery System for a Successful Biocompatible, Anti-Cancer, Anti-Inflammatory and Phagocytosis Inducer Model
Bibliographic record
Abstract
Hesperidin is a flavonoid glycoside with proven therapeutic activities for various diseases, including cancer. However, its poor solubility and bioavailability render it only slightly absorbed, requiring a delivery system to reach its therapeutic target. Hesperidin loaded on gold nanoparticles (Hsp-AuNPs) was prepared by a chemical synthesis method. Various characterization techniques such as UV-VIS spectroscopy, FTIR, XRD, FESEM, TEM and EDX, Zeta potential analysis, particle size analysis, were used to confirm the synthesis of Hsp-AuNPs. The cytotoxic effect of Hsp-AuNPs on human breast cancer cell line (MDA-MB-231) was assessed using MTT and crystal violet assays. The results revealed significant decrease in proliferation and inhibition of growth of the treated cells when compared with human normal breast epithelial cell line (HBL-100). Determination of apoptosis by fluorescence microscope was also performed using acridine orange-propidium iodide dual staining assay. The in vivo study was designed to evaluate the toxicity of Hsp-AuNPs in mice. The levels of hepatic and kidney functionality markers were assessed. No significant statistical differences were found for the tested indicators. Histological images of liver, spleen, lung and kidney showed no apparent damages and histopathological abnormalities after treatment with Hsp-AuNPs. Hsp-AuNPs ameliorated the functional activity of macrophages against Ehrlich ascites tumor cells-bearing mice. The production of the pro-inflammatory cytokines was also assessed in bone marrow-derived macrophage cells treated with Hsp-AuNPs. The results obviously demonstrated that Hsp-AuNPs treatment significantly inhibited the secretion of IL-1β, IL-6 and TNF.
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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".