Fabrication and Characterization of Curcumin-Complexed Nanoparticles Using Coconut Protein Nanoparticles
Bibliographic record
Abstract
Background/Objectives: Curcumin (Cur) has various biological properties, including anti-microbial, antioxidant, anticancer, anti-diabetic, anticarcinogenic, antitumor, and anti-inflammatory activities. However, using Cur in functional food products is challenging because of its low solubility in an aqueous environment, rapid degradation, and low bioavailability. Nanostructure delivery systems provide a high surface area to volume ratio and sustainable release properties. Methods: Coconut protein nanoparticles (CPNPs) have been fabricated through heat treatment at 85 °C and pH 2 for 5 h. The formation of CPNP-Cur was used to improve Cur solubility, followed by antioxidant activity at neutral pH in an aqueous solution. Results: The maximum efficiency and loading capacity of Cur in CPNP were 96.6% and 19.32 µg/mg protein, respectively. Scanning electron microscopy indicated the spherical and organized shape of CPNP with a small size of 80 nm. The fluorescence quenching of CPNP-Cur confirmed the potential of Cur to bind to the tryptophane and tyrosine residues in CPNP. The structural properties of CPNP and CPNP-Cur were investigated using FTIR and X-ray diffraction. The antioxidant activity of samples, measured with the ABTS radical scavenging method, demonstrated that the antioxidant capacity of the aqueous solution of Cur was significantly enhanced through the encapsulation into CPNP. The steady release of Cur was observed in the simulated gastrointestinal tract, and the percentage of the cumulative release increased up to 29.2% after 4 h. Conclusions: Our findings suggest that CPNP was a suitable nanocarrier for Cur due to improved antioxidant activity and controlled release behavior. These results are valuable for the development of coconut protein nanoparticles to use as a novel nano-delivery system of bioactive components.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".