Importance of probiotics in the prevention and treatment of colorectal cancer
Bibliographic record
Abstract
Colorectal cancer (CRC) remains one of the most common and deadly cancers. Intestinal gut microflora is important to maintain and contributes to several intestinal functions, including the development of the mucosal immune system, absorption of complex macromolecules, synthesis of amino acids/vitamins and the protection against pathogenic microorganisms. It is well known that the gut microbiota changes or dysbiosis may have an essential impact in the initiation and promotion of chronic inflammatory pathways and also have a profound different genetic and epigenetic alterations leading to dysplasia, clonal expansion, and malignant transformation. Probiotic bacteria has antitumor activity with various mechanisms such as nonspecific physiological and immunological mechanisms. This review evaluates the effects of microbiota and probiotics in clinical trials, in vitro and animal model studies that have explored how probiotic against cancer development and also discusses the possible immunomodulatory mechanisms. Several mechanisms alteration of the intestinal microflora; inactivation of cancerogenic compounds; competition with putrefactive and pathogenic microbiota; improvement of the host's immune response; antiproliferative effects via regulation of apoptosis and cell differentiation; fermentation of undigested food; inhibition of tyrosine kinase; reduces the enteropathogenic complications before and after colon cancer surgery and improve diarrhea and it's have been able to create the integrity of gut mucosal and have stimulatory effects on the systemic immune system and prevent the CRC metastasis. Research in clinical trials encouraging findings that support a role of probiotics in CRC prevention and improve the safety and effectiveness of cancer therapy even though additional clinical research is still necessary.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".