Alcalase immobilized on novel porous hydrogel beads via CO2 bubble strategy to reduce cow's milk allergenicity
Bibliographic record
Abstract
• CO 2 bubbles can improve the activity and reusability of alcalase immobilized on hydrogel beads. • Immobilized alcalase shows superior pH, temperature, and storage stabilities to free alcalase. • Immobilized alcalase enables the controlled hydrolysis of cow's milk proteins to reduce their allergenicity. • Gas strategy is an efficient method for preparing safety and recyclable hydrogel-based biocatalyst. Cow's milk allergy poses major global health risks that limit the consumption of milk-based products. Meanwhile, recycling enzymes in the enzymatic methods for decreasing cow's milk antigenicity has been challenging. Here, we developed a green and simple approach using carbon dioxide (CO 2 ), generated by the reaction between sodium bicarbonate and acetic acid, as the gas porogen to fabricate novel porous carboxymethyl chitosan/alginate hydrogel beads to immobilize alcalase, aiming at reducing cow's milk antigenicity. This CO 2 bubble strategy notably enhanced the porosity of the hydrogel beads. The specific enzyme activity (SEA) of alcalase immobilized on the porous hydrogel beads prepared with 0.075 mM NaHCO 3 (Alc/GHB-G7.5) increased by 42.2% compared with those on supports without gas treatment. Additionally, after four cycles of operation at 40°C and pH 7.0, Alc/GHB-G7.5 maintained 84.0% of its initial SEA. Compared with free alcalase, the immobilized form exhibited superior pH and temperature tolerance, along with improved storage stability. The allergenicity of cow's milk decreased by 65.6% after one hour of enzymatic hydrolysis using Alc/GHB-G7.5. Importantly, all materials involved in the study were food-grade, fully ensuring food safety. Therefore, the proposed biocatalyst has great potential for producing hypoallergenic cow's milk-related foods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".