Augmentation of salivary IgA production in healthy subjects by fucoidan-agaricus mix ingestion
Bibliographic record
Abstract
Abstract Fucoidan is a series of sulfated polysaccharides derived from brown algae, and have reported to have various biological activities. Previously, we demonstrated that fucoidan derived from Cladosiphon okamuranus and Undaria pinnatifida effectively augmented anti-tumor immunity in combination with Agaricus blazei mycelia extract. In this study, we evaluated the capacity of the fucoidan-agaricus mix (FAM) to enhance oral mucosal immune function. Fifteen healthy volunteers (mean age, 41.2 years old; range, 22–56 years old; 8 males, 7 females) ingested 4 capsules each containing 250 mg FAM powder every day for 12 weeks. As a result, the mean of secretory rates of salivary secretory immunoglobulin A (sIgA) tended to be increased by the FAM administration for 4 and 12 weeks as compared with the initial value. The enhanced salivary sIgA secretion was more distinctly observed in subject group whose initial values of salivary sIgA secretory rate were lower than the total average. Furthermore, the intake of FAM led to significant augmentation in the salivary sIgA secretion (by 1.3-fold) in the group of subjects under 40 years of age. On the other hand, concanavalin A-induced blastogenic response of peripheral blood mononuclear cells and serum IgA concentration was not elevated during this trial. Therefore, it was suggested that FAM stimulated functional maturation rather than expansion of B lymphocyte. The intake of FAM did not significantly affect NK cell activities. In addition, the safety of FAM consumption was confirmed because no abnormal findings were observed in general clinical tests. From the results, it was suggested that intake of FAM was useful in augmentation of oral mucosal immune defense via enhancing salivary sIgA production.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".