Rapid Immune Modulation after Consuming Euglena gracilis Whole Algae Involving Altered Responses to Ex Vivo Immune Challenges: A Placebo-Controlled Cross-Over Trial
Bibliographic record
Abstract
Euglena gracilis (EG) microalgae has immune-modulating properties, partly due to its unique intracellular β-glucan-granules (paramylon). We evaluated the effects of EG consumption on immune status in vivo, ex vivo, and in vitro. A placebo-controlled cross-over study evaluated acute immune surveillance, followed by a 1-week open-label phase. Immune training was documented using ex vivo immune challenges and cytokine profiles. In vitro testing of monocytes compared the effects of EG to pure β-glucan. Compared to placebo, EG consumption triggered increased T cell numbers in the blood circulation (1 h: p < 0.01) and decreased monocyte numbers (2 h: p < 0.05). Natural killer cells showed increased CD25 expression (1 and 2 h: p < 0.01) and reduced CD69 expression (2 h: p < 0.01). T cells showed reduced CD25 and CD69 expression (p < 0.01). There were no significant changes to serum cytokines. After EG consumption, ex vivo cultures of peripheral blood mononuclear cells showed significant changes to spontaneous and inflammation-induced cytokine levels after 2 h (increased G-CSF: p < 0.01, reduced IL-1β and TNF-α (p < 0.05)) and one week (reduced TNF-α (p < 0.01) and increased IL-10 (p < 0.05)). In vitro, EG-trained monocytes responded differently to a second stimulus than β-glucan-trained monocytes (increased IL-1b: p < 0.1, TNF-α: p < 0.01). EG-mediated training of innate immunity, combined with long-term modulation of inflammation, suggests a nutraceutical strategy for preventive immune support.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".