Effect of Processing on Physicochemical Characteristics and Bioefficacy of β-Lactoglobulin–Epigallocatechin-3-gallate Complexes
Bibliographic record
Abstract
Varying amounts of epigallocatechin-3-gallate (EGCG) were encapsulated in β-lactoglobulin (β-Lg) nanoparticles, either native or processed, denoted as heated or desolvated protein. The stability, physical properties, and bioactivity of the β-Lg-EGCG complexes were tested. Native β-Lg-EGCG complexes showed comparable stability and binding efficacy (EGCG/β-Lg molar ratio of 1:1) to heated β-Lg nanoparticles (1% and 5% protein w/w). The sizes of heated and desolvated β-Lg nanoparticles were comparable, but the latter showed the highest binding affinity for EGCG. The presence of EGCG complexed with β-Lg did not affect the interfacial tension of the protein when tested at the soy oil-water interface but caused a decrease in dilational elasticity. All β-Lg complexes (native, heated, or desolvated) showed a decrease in cellular proliferation similar to that of free ECGC. In summary, protein-EGCG complexes did not alter the bioefficacy of EGCG and contributed to increased stability with storage, demonstrating the potential benefits of nanoencapsulation.
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 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".