Nano-encapsulation of isolated lactoferrin from camel milk in starch nanoparticles: characterisation and retention of antidiabetic activity during <i>in vitro</i> digestion
Bibliographic record
Abstract
Abstract Starch nanoparticles have been widely used as a carrier material for the encapsulation of lactoferrin (Lf). In this study, Lf was extracted from camel milk through the alkali precipitation method and loaded onto starch nanoparticles and its impact on the retention of antidiabetic activity was evaluated. Starch nanoparticles were prepared from underutilised sources such as arrowhead (Ar), bracken (Br), and Kudzu (Kd) using ball-milling process. Particle size, surface charge, structural characteristics, surface morphology, and colour parameters were studied. All the starch nanoparticles encapsulated Lf showed significantly reduced particle size (ranging from 116.8 ± 3.15 to 105.2 ± 2.92 nm) with higher encapsulation efficiency (75.9%–83.87%). Antidiabetic activity through inhibition of α-amylase, α-glucosidase, and dipeptidyl peptidase-IV was retained at a higher level in encapsulated Lf compared to the free-Lf upon simulated in vitro gastrointestinal digestion. The results demonstrated that encapsulated Lf remained intact under gastric conditions and reflects its bioactivity under intestinal conditions. These findings suggested that Lf-encapsulated in starch nanoparticles could be advanced as a favourable delivery system for its potential application as a bioactive and functional protein in the food industry.
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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".