Effects of a β-Glucan-Rich Blend of Medicinal Mushrooms and Botanicals on Innate Immune Cell Activation and Function Are Enhanced by a Very Low Dose of Bovine Colostrum Peptides
Bibliographic record
Abstract
Nutraceutical immune support offers potential for designing blends with complementary mechanisms of action for robust support of innate immune alertness. We documented enhanced immune activation when bovine colostrum peptides (BC-Pep) were added to an immune blend (IB) containing β-glucans from yeast, shiitake, maitake, and botanical non-β-glucan polysaccharides. Human peripheral blood mononuclear cells (PBMCs) were cultured with IB, BC-Pep, and IB + BC-Pep for 20 h, whereafter expression of the activation marker CD69 was evaluated on NK cells, NKT cells, and T cells. Cytokine levels were tested in culture supernatants. PBMCs were co-cultured with K562 target cells to evaluate T cell-mediated cytotoxicity. IB + BC-Pep triggered highly significant increases in IL-1β, IL-6, and TNF-α, above that of cultures treated with matching doses of either IB or BC-Pep. NK cell and T cell activation was increased by IB + BC-Pep, reaching levels of CD69 expression several fold higher than either BC-Pep or IB alone. IB + BC-Pep significantly increased T cell-mediated cytotoxic killing of K562 target cells. This synergistic effect suggests unique amplification of signal transduction of NK cells and T cells due to modulation of IB-induced signaling pathways by BC-Pep and is of interest for further pre-clinical and clinical testing of immune defense activity against virally infected and transformed cells.
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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".