Preparation, characterization, and antioxidant activities of cellulose nanocrystals/genistein nanocomposites
Bibliographic record
Abstract
Genistein (GEN), a typical isoflavone compound, exhibits desirable pharmacological activities, such as antioxidation, anti-inflammatory, , and anti-cancer properties. However, the pharmaceutical application of GEN is limited because of its poor water solubility in aqueous systems. In this study, cellulose nanocrystals (CNCs) and cetyltrimethylammonium bromide (CTAB)-coated CNCs were used as carriers for GEN to improve its dissolution rate and antioxidant activity in aqueous systems. The CNC/GEN and CNC/CTAB/GEN nanocomposites were successfully prepared and characterized by Fourier transform infrared spectroscopy, transmission electron microscopy, and X-ray diffraction analysis. The results showed that the nanonized GEN performed better and its crystalline structure decreased because of the formation of the CNC/GEN and CNC/CTAB/GEN nanocomposites. The GEN dissolution rates in the CNC/GEN and CNC/CTAB/GEN nanocomposites increased to 72.1% and 92.5% at 120 min, respectively, compared with that of the original GEN (0.85%). Furthermore, the in vitro antioxidant activity of the GEN, which was evaluated by the hydroxyl radical scavenging efficiency, was remarkably enhanced. Based on the above results, CNCs as nanocarriers are a useful method for improving the dissolution and antioxidant activities of GEN in aqueous system.
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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".