The Potency of Immunomodulatory Herbs May Be Primarily Dependent upon Macrophage Activation
Bibliographic record
Abstract
Standardized extracts of Echinacea, cat's claw, and saw palmetto were each evaluated for ability to activate macrophage and natural killer cells, in vitro, using two independent measures of activation for each immune cell population. A standard series of exposure concentrations were tested for each herbal extract in a panel of four assays that evaluated macrophage phagocytosis, macrophage synthesis of interleukin-12, natural killer (NK) cell cytocidal activity (synthesis of granzyme B), and NK cell synthesis of interferon-gamma. Macrophage phagocytosis was stimulated by all three herbs tested: saw palmetto (up to 2.3-fold, P < .05), Echinacea (up to 3.6-fold, P < .01), and cat's claw (up to 4.7-fold, P < .01). Additionally, NK cell synthesis of interferon-gamma was stimulated by saw palmetto (up to 6.3-fold, P < .01) and Echinacea (up to 8.1 fold, P < .01) but not by exposure to cat's claw. None of the three herbs stimulated macrophage synthesis of interleukin-12 or NK cell synthesis of granzyme B. Comparison of the in vitro data with our earlier observations that cat's claw and Echinacea (but not saw palmetto) were each effective in reducing B16/F10 lung tumor colony formation in C57BL/6J mice suggests macrophage activation is the primary means by which these herbs modulate the immune system. Thus, macrophage activation (phagocytosis) may provide a potentially higher throughput method to identify herbal extracts with in vivo stimulatory effects.
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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".