<scp>Poly‐L</scp>‐lysine‐coated α‐lactalbumin nanoparticles: preparation, effect of <scp>pH,</scp> and stability under <i>in vitro</i> simulated gastrointestinal conditions
Bibliographic record
Abstract
Abstract BACKGROUND The development of bioactive compound delivery systems using protein‐based nanoparticles as carriers is a rapidly growing field of study. α‐Lactalbumin (α‐La) nanoparticles are adequate for this task because they are nonantigenic, biodegradable, and easily modifiable via the surface physical adsorption of other molecules. Here, the effect of poly‐L‐lysine (PLL) coating of α‐La nanoparticles on the stability against enzymatic degradation in the gastrointestinal tract was investigated. RESULTS α‐La nanoparticles were prepared by desolvation with acetone. It was observed that the pH of the initial protein suspension did not have a significant effect on the size, polydispersity index (PDI), or ζ‐potential of the nanoparticles, so the subsequent experiments were performed at pH 9, where 151.2 nm nanoparticles with a PDI of 0.072 and a ζ‐potential of −29.7 mV were obtained. The nanoparticles coating was performed with two MW‐range PLL at three concentrations. Coating efficiency increased as the polymer concentration increased and this was independent of the PLL MW. The best coating efficiency (90–95%) was obtained in the case of the 1 mg mL−1 PLL coating. Most PLL‐coated nanoparticles showed a good stability to the conditions of in vitro simulated gastrointestinal digestion compared with the uncoated nanoparticles. CONCLUSION It can be concluded that the acetone desolvation method at pH 9 is adequate to obtain α‐La nanoparticles with good characteristics. The process of PLL coating of nanoparticles showed an increase in efficiency with the increase in concentration of the polymer. The resulting coated nanoparticles had enhanced resistance to simulated gastrointestinal conditions. © 2021 Society of Chemical Industry (SCI).
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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".