Leguminous ferritin, a natural protein for iron supplementation, <scp>Pickering</scp> emulsion formation and encapsulation of bioactive molecules
Bibliographic record
Abstract
Abstract Ferritin is a naturally occurring iron storage protein. Leguminous ferritins exhibit unique structural features, including diverse subunit composition and an extension peptide, which contribute to superior thermal stability compared to animal ferritins. The high iron content, remarkable effectiveness, low risk of oxidative damage and thermal stability make the leguminous ferritin an attractive candidate for iron supplementation. Moreover, apoferritin is an excellent nanosized carrier for encapsulating bioactive compounds due to its inherent inner cavity, water solubility, biocompatibility, and reversible self‐assembly behavior. However, the harsh condition during encapsulation by unmodified ferritins may cause damage to sensitive bioactive compounds. Thus, different processing methods are employed to alter the leguminous ferritin structures, including chemical, enzymatic, mild heat treatments, and nonthermal processing to achieve gentler encapsulation conditions for a wide range of bioactive compounds. Another challenge is to improve the stability of leguminous ferritin to withstand gastric digestion. The degradation of ferritin by proteases may lead to premature release of bioactive compounds. Recent works demonstrated that certain phenolic compounds such as proanthocyanidin‐induced protein association, thereby enhancing digestive stability of ferritins, leading to a sustained release and a potentially greater bioavailability of bioactive compounds. Leguminous ferritin also has the potential to serve as a stabilizer for the Pickering emulsion, where the hydrophilic and hydrophobic compounds can be encapsulated in the ferritin nanocages and oil phase, respectively. The release and absorption of bioactive compounds in encapsulates and emulsions will need to be further demonstrated through in vivo studies.
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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) | 0.001 | 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".