Diamine Oxidase from White Pea (Lathyrus sativus) Combined with Catalase Protects the Human Intestinal Caco-2 Cell Line from Histamine Damage
Bibliographic record
Abstract
Diamine oxidase (DAO) administration has been proposed to treat certain gastrointestinal dysfunctions induced by histamine, an immunomodulator, signaling, and pro-inflammatory factor. However, H 2 O 2 resulting from the oxidative deamination of histamine by DAO may be toxic. The purpose of this study was to investigate to which extent DAO from white pea ( Lathyrus sativus ), alone or in combination with catalase, may modulate histamine toxicity in the human intestinal Caco-2 cell line. The results show that histamine at concentrations higher than 1 mM is toxic to the Caco-2 cells, independently of the cell differentiation status, with a LC 50 of ≅ 10 mM following a 24-h exposure. Depending on its concentration, DAO increased histamine toxicity to a greater extent in differentiated cells compared to undifferentiated cultures. In the presence of catalase, the DAO-induced increase in histamine toxicity was completely abolished in the undifferentiated cells and only partially decreased in differentiated cells, showing differences in the sensitivity of Caco-2 cells to the products resulting from histamine degradation by DAO (H 2 O 2 , NH 3 , or imidazole aldehyde). It appears that treatment of food histaminosis using a combination of vegetal DAO and catalase would protect against histamine toxicity and prevent H 2 O 2 -induced damage that may occur during histamine oxidative deamination.
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".