Polysaccharide isolated from<i> Grifola frondosa</i> eliminates myeloid-derived suppressor cells and inhibits tumor growth by enhancing T cells responses
Bibliographic record
Abstract
Myeloid derived suppressor cells (MDSCs) are known to accumulate in cancer patients and tumor-bearing mice, playing a significant role in promoting tumor growth.Depleting MDSCs has emerged as a potential therapeutic strategy for cancer.Here, we demonstrated that a fungal polysaccharide, extracted from Grifola frondosa, can effectively suppress breast tumorigenesis in mice by reducing the accumulation of MDSCs.Treatment with Grifola frondosa polysaccharide (GFI) leads to a substantial decrease in MDSCs in the blood and tumor tissue, and a potent inhibition of tumor growth.GFI treatment significantly reduces the number and proportion of MDSCs in the spleen, although this effect is not observed in the bone marrow.Further analysis reveals that GFI treatment primarily targets PMN-MDSCs, sparing M-MDSCs.Our research also highlights that GFI treatment has the dual effect of restoring and activating CD8 + T cells, achieved through the downregulation of TIGIT expression and the upregulation of Granzyme B. Taken together, our findings suggest that GFI treatment effectively eliminates PMN-MDSCs in the spleen, leading to a reduction in MDSC numbers in circulation and tumor tissues, ultimately enhancing the antitumor immune response of CD8 + T cells and inhibiting tumor growth.This study introduces a promising therapeutic agent for breast cancer.
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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".