Natural Killer Cell Activation and Modulation of Chemokine Receptor Profile <i>In Vitro</i> by an Extract from the Cyanophyta <i>Aphanizomenon flos-aquae</i>
Bibliographic record
Abstract
The present research was designed to study the effects of an extract from the edible cyanophyta Aphanizomenon flos-aquae on human natural killer (NK) cells. We have previously shown, using a double-blind randomized placebo-controlled crossover design, that ingestion of 1.5 g of dried whole A. flos-aquae resulted in a transient reduction in peripheral blood NK cells in 21 healthy human volunteers, suggesting increased NK cell homing into tissue. We have now identified an extract from A. flos-aquae (AFAe) that directly activates NK cells in vitro and modulates the chemokine receptor profile. NK cell activation was evaluated by expression of CD25 and CD69 on CD3-CD56+ cells after 18 hours. Changes in CXCR3 and CXCR4 chemokine receptor expression after 5-60 minutes were evaluated by immunostaining and flow cytometry. AFAe induced the expression of CD69 on CD3-CD56+ NK cells, induced CD25 expression on 25% of these cells, and acted in synergy with interleukin 2. NK cells enriched by RosetteSep (StemCell Technologies Inc., Vancouver, BC, Canada) were not activated by AFAe, indicating that the NK activation was dependent on other cells such as monocytes. The low-molecular-weight fraction <5,000 of AFAe was responsible for the most robust NK cell activation, suggesting novel compounds different from previously reported macrophage-activating large polysaccharides.
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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) | 0.001 | 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".