Jet-O-mized Eggshell Membrane Powder: A Novel Hydrolysis Strategy to Unlock Gut-Promoting Bioactivities
Bibliographic record
Abstract
In developed countries like Canada, significant amounts of eggshell (ES) and eggshell membrane (ESM) waste are generated by egg-breaking plants serving the food and biopharmaceutical industries. ESM is composed of 90% proteins with diverse bioactivities, including antibacterial, immunomodulatory, anti-inflammatory, and antioxidant properties. As a supplement, ESM shows promise in promoting gut health by modulating the microbiome through its bioactive effects. However, advanced solubilization strategies for creating ESM-based functional platforms remain underexplored. This study aimed to develop novel ESM formulations for gut health. Size-reduced ESM particles (JEM) were prepared via jet-O-mizing and hydrolyzed under optimized alkaline conditions using dilute potassium hydroxide, yielding a 50% solubilization of bioactive proteins and peptides. The hydrolyzed JEM suspension (WJ) was centrifuged to obtain the soluble fraction (SJ). Both WJ and SJ were further processed through simulated gastrointestinal digestion (SGID) to produce hydrolyzed metabolites (SJ-G, WJ-G, SJ-GI, and WJ-GI). JEM formulations demonstrated a 15-fold increase in Trolox-equivalent antioxidant capacity (TEAC) compared to non-hydrolyzed JEM (NJEM), with 10 mg/mL SJ showing 667.3 ± 25 µM Trolox versus 43.7 ± 7.1 for NJEM. SJ also exhibited bacteriostatic effects, inhibiting E. coli growth by 50 % over 24 hours compared to untreated culture. Additionally, SJ and WJ-G reduced Lipopolysaccharideinduced nitric oxide production by up to 80% in RAW 264.7 macrophages. The impact on intestinal barrier function was evaluated using Caco-2 cells, showing no significant changes in cell permeability. These findings highlight the development of bioactive ESM formulations with antibacterial, antioxidant, and anti-inflammatory properties, positioning them as promising dietary supplements for gut health.
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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".