Impact of pH-Shifting and Autoclaving on the Allergenic Potential of Red Kidney Bean (<i>Phaseolus vulgaris</i> L.) Lectins
Bibliographic record
Abstract
The ingestion of red kidney bean products is hindered by the persistent allergenicity of lectins, even after autoclaving. This study examined the modification of lectin allergenicity in red kidney beans by pH-shifting and autoclaving treatments, utilizing BALB/c mouse sensitization, in situ recirculating perfusion, and a bone marrow-derived dendritic cell (BMDC) model for allergenicity evaluation. Compared to autoclaving alone, combined pH-shifting and autoclaving reduced allergic symptoms in BALB/c mice, as evidenced by lower serum IgE, mMCPT-1, GM-CSF, HIS, IL-2, IL-4, IL-9, IL-13, and IL-17 levels and higher IgG1, IgG2a, IL-10, IFN-γ, and IFN-α cytokine release. Moreover, lectin continued to affect intestinal permeability and damaged the barrier despite undergoing pH-shifting and autoclaving treatments. Additionally, the uptake of lectin by BMDCs through mannose receptor-mediated endocytosis was diminished, with an increased susceptibility to endolysosomal degradation. The T-cell polarization was consistent with the mouse experiments, where the balance of Th1 and Th2 cells remained in lectin with pH-shifting and autoclaving treatments though the decreased abundance ratios of peptide YKYDSNAHT and increased abundance ratios of peptide ITKGNVETN in endolysosomal degradation. Therefore, the immunogenicity of lectins could be decreased by pH-shifting and autoclaving treatments, offering insights into the development of hypoallergenic legume products.
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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".