Abstract 254: The potential of <i>Lactobacillus</i> probiotic treatments in colorectal cancer (CRC)
Bibliographic record
Abstract
Abstract Colorectal cancer (CRC) is the third leading cause of death worldwide. It is known to be a type of cancer that is preventable by changes in diet and lifestyle. Mounting evidence are supporting the role of gut microbiome in the etiology of CRC and emphasize on the potential of probiotics as biotherapeutics in the prevention and management of CRC. Lactobacillus probiotic bacteria were suggested to balance pathogenic and oncogenic microbial community in the colon and produce anti-tumorigenic and anti-inflammatory effects in healthy individuals at risk and CRC patients. There is a need, however, for studies that focus on identifying potent probiotic strains with activity against CRC and inhibit cancer growth. This report discusses and present findings about the formulation of novel probiotic Lactobacillus biotherapeutic for CRC based on anti-CRC proliferative effect, immune modulation and metabolic activity, in vitro and in vivo, using a genetically-induced animal CRC model. Results and metabolomic analysis demonstrated the potential acrion of probiotic treatment to change host and gut microbiome co-metabolic profiles, produce local and systemic anti-inflammatory effects, inhibit cancer-causing events, and improve overall gut health. Note: This abstract was not presented at the meeting. Citation Format: Imen Kahouli, Meenakshi Malhotra, Susan Westfall, Moulay Alaoui-Jamali, Satya Prakash. The potential of Lactobacillus probiotic treatments in colorectal cancer (CRC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 254. doi:10.1158/1538-7445.AM2017-254
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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 | 0.001 | 0.000 |
| 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) | 0.012 | 0.003 |
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".