Bibliographic record
Abstract
Cancer remains a leading cause of mortality worldwide, with millions of deaths each year. One emerging area of interest in cancer prevention and treatment is the effect of gut microbiota and how dietary interventions can modulate its composition to exert anti-tumor effects. The gastrointestinal tract hosts a diverse community of microorganisms, collectively known as the gut microbiota, which is vital for human health maintenance. When dysbiosis occurs, it can contribute to diseases such as colorectal cancer (CRC). Recent studies have identified mushroom polysaccharides as promising agents with potent anti-tumor properties. Mushroom polysaccharides act as indigestible polysaccharides that can serve as prebiotics to maintain gut health and regulate the immune system. In this article, we review and record studies about the effects of mushroom polysaccharides in anti-tumor. They stimulate and activate immune responses and induce tumor cell death through apoptosis and necrosis. They also affect angiogenesis, thereby inhibiting the nutrient absorption of cancer cells. This paper describes the structure of polysaccharides that can influence bioactivity and anti-tumor effects and summarizes the functions of mushroom polysaccharides in clinical settings.
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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".