Potentiating effects of high-molecular weight fucoidan-agaricus mix (CUA) feeding on tumor vaccination
Bibliographic record
Abstract
Abstract Fucoidan is a series of sulfated polysaccharides derived from brown algae that mainly consist of L-fucose. Various biological activities of fucoidan such as anti-cancer and immune modulatory effects were reported, and our investigation with animal experiments and human trials demonstrated fucoidan from Cladosiphon okamuranus and Undaria pinnatifida effectively augmented anti-tumor immunity in combination with Agaricus blazei mycelium extract. In this study, we evaluated dietary effects of the two types of high-molecular weight fucoidans and the agaricus extract mix (CUA) on achievement of effective tumor vaccination with a tumor antigen gp70 expressed on colon-26 tumor cell line. Balb/c mice were immunized with 0.05 mg gp70 peptide emulsified in complete freund’s adjuvant and intake 1% CUA containing AIN93G diet for 4 weeks. This procedure totally enhanced systemic immune function because splenocytes from the vaccinated mice extensively proliferate in response to concanavalin A-stimulation. The NK cell activity and gp70 peptide-stimulated IFN-gamma production in splenocytes from the vaccinated mice were tended to augment by the CUA feeding. On the other hands, the CUA feeding potentiated the killing activity to colon-26 carcinoma of draining lymph node (LN) cells from the vaccinated mice in association with increase of gp70-specific CD8-positive T cell population. Furthermore, the expressions of MHC class II molecule (I-A/I-E) on CD11c-positive and F4/80-positive populations of LN cells from the vaccinated mice were elevated by the intake of CUA. These results suggested that the CUA feeding potentially support effective induction of anti-tumor immune function by vaccination with tumor antigen peptides.
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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".