Preventing Colorectal Cancer through Prebiotics
Bibliographic record
Abstract
Colorectal cancer (CRC), the third most common cancer in the world, has been recently rising in emerging countries due to environmental and lifestyle factors. Many of these factors are brought up by industrialization, which includes lack of physical activity, poor diet, circadian rhythm disruption, and increase in alcohol consumption. They can increase the risk of CRC by changing the colonic environment and by altering gut microbiota composition, a state referred to as gut dysbiosis. Prebiotics, which are nutrients that can help maintain intestinal microbial homeostasis and mitigate dysbiosis, could be beneficial in preventing inflammation and CRC. These nutrients can hinder the effects of dysbiosis by encouraging the growth of beneficial bacteria involved in short-chain fatty acids (SCFA) production, anti-inflammatory immunity, maintenance of the intestinal epithelial barrier, pro-apoptotic mechanisms, and other cellular mechanisms. This review aims to summarize recent reports about the implication of prebiotics, and probable mechanisms, in the prevention and treatment of CRC. Various experimental studies, specifically in gut microbiome, have effectively demonstrated the protective effect of prebiotics in the progress of CRC. Hence, comprehensive knowledge is urgent to understand the clinical applications of prebiotics in the prevention or treatment of CRC.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".