Influence of high-intensity ultrasound on the IgE binding capacity of Act d 2 allergen, secondary structure, and In-vitro digestibility of kiwifruit proteins
Bibliographic record
Abstract
Kiwifruit can trigger allergic reactions that can lead to death, causing public health concerns worldwide. In the present study, we treated kiwifruit samples with high-intensity ultrasound (20 kHz, 400 W, 50% duty cycle) for 0 to 16 min to evaluate its effect on the IgE binding capacity of kiwifruit allergen Act d 2, secondary structure and in-vitro digestibility of kiwifruit proteins. The changes in the protein solubility and microstructures of kiwifruit were also analyzed. The results showed that treatment with powerful ultrasound caused a significant disruption in the microstructure of kiwifruit tissues, leading to the changes in the secondary structures of proteins, including a loss of alpha-helixes and an increase in beta-sheet structures. These structural changes were due to the ultrasound treatment, especially 16 min of treatment, resulted in a 50% reduction in Act d 2 allergen content and significantly improved in-vitro digestibility up to 62% from the initial level of 35%. Furthermore, the solubility of the total proteins present in kiwifruit samples was significantly decreased by 20% after 16-min ultrasound processing. The results of this work showed that high-intensity ultrasound treatment has a potential application in reducing the allergenicity of kiwifruit or related products.
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".