Suppression of Tumorigenesis: Modulation of Inflammatory Cytokines by Oral Administration of Microencapsulated Probiotic Yogurt Formulation
Bibliographic record
Abstract
The objective of this study was to examine the ability of a novel microencapsulated probiotic yogurt formulation to suppress the intestinal inflammation. We assessed its anticancer activity by screening interleukin-1, 6, and 12 (IL-1, 6, 12), secretory levels of tumor necrosis factor-alpha (TNF-α), interferon-gamma (IFN-γ), prostaglandin E(2) (PGE(2)), and thromboxane B2 in the digesta obtained from the duodenum, jejunum, proximal, and distal segments of the ileum of C57BL/6J-Apc(Min)/J mice. Formulation-receiving animals showed consistently lower proinflammatory cytokines' levels when compared to control group animals receiving empty alginate-poly-L-lysine-alginate (APA) microcapsules suspended in saline. The concentrations of IL-12 found in serum in control and treatment group animals were significant: 46.58 ± 16.96 pg/mL and 158.58 ± 28.56 pg/mL for control and treatment animals, respectively. We determined a significant change in plasma C-reactive protein: 81.04 ± 23.73 ng/mL in control group and 64.21 ± 16.64 ng/mL in treatment group. Western blots showed a 71% downregulation of cyclooxygenase-2 (COX-2) protein in treatment group animals compared to control. These results point to the possibility of using this yogurt formulation in anticancer therapies, in addition to chronic gut diseases such as Crohn's disease, irritable bowel syndrome (IBS), and inflammatory bowel disease (IBD) thanks to its inflammation lowering properties.
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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) | 0.000 | 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 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".