Secreted Metabolites from Pseudomonas, Staphylococcus, and Borrelia Biofilm: Modulation of Immunogenicity by a Nutraceutical Enzyme and Botanical Blend
Bibliographic record
Abstract
Bacterial biofilms are hardy, adaptable colonies, evading immune recognition while triggering and sustaining inflammation. The goals for this study were to present a method for testing the immunogenicity of secreted metabolites from pathogenic biofilm and to document whether biofilm treated with a nutraceutical enzyme and botanical blend (NEBB) showed evidence of reprogrammed bacterial metabolism, potentially becoming more recognizable to the immune system. We screened immune-modulating properties of metabolites from established biofilm from Pseudomonas aeruginosa (Pa), Stapholycoccus simulans (Ss), and Borrelia burgdorferi (Bb). Secreted metabolites significantly increased the cytokine production by human peripheral blood mononuclear cells, including Interleukin-1-beta (IL-1β), Interleukin-6 (IL-6), macrophage inflammatory protein-1-alpha (MIP-1α), tumor necrosis factor-alpha (TNF-α), interleukin-1 receptor antagonist (IL-1ra), and interleukin-10 (IL-10). Pa metabolites triggered the most robust increase in IL-1β, whereas Bb metabolites triggered the most robust increase in IL-10. NEBB-disrupted biofilm produced metabolites triggering altered immune modulation compared to metabolites from untreated biofilm. Metabolites from NEBB-disrupted biofilm triggered increased MIP-1α levels and reduced IL-10 levels, suggesting a reduced ability to suppress the recruitment of phagocytes compared to untreated biofilm. The results suggest that nutraceutical biofilm disruption offers strategies for inflammation management in chronic infectious illnesses. Further clinical studies are warranted to evaluate clinical correlations in infected human hosts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".