Mechanisms by Which Probiotic Bacteria Attenuate the Risk of Hepatocellular Carcinoma
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) is the most common primary liver cancer and the second leading cause of cancer-related deaths worldwide. Chronic infections with hepatitis B virus (HBV) and hepatitis C virus (HCV), alcoholic liver disease (ALD), and non-alcoholic fatty liver disease (NAFLD)/non-alcoholic steatohepatitis (NASH) are the major extrinsic risk factors of HCC development. Genetic background is pivotal in HCC pathogenesis, and both germline mutations and single nucleotide polymorphism (SNP) are intrinsic risk factors of HCC. These HCC risk factors predispose to hepatic injury and subsequent activation of fibrogenesis that progresses into cirrhosis and HCC. Probiotic bacteria can mitigate HCC risk by modulating host gut microbiota (GM) to promote growth of beneficial microbes and inhibit HCC-associated dysbiosis, thus preventing pathogen-associated molecular patterns (PAMPs)-mediated hepatic inflammation. Probiotics have antiviral activities against HBV and HCV infections, ameliorate obesity and risk of NAFLD/NASH, and their antioxidant, anti-proliferative, anti-angiogenic, and anti-metastatic effects can prevent the HCC pathogenesis. Probiotics also upregulate the expression of tumor suppressor genes and downregulate oncogene expression. Moreover, metabolites generated by probiotics through degradation of dietary phytochemicals may mitigate the risk of HCC development. These multiple anticancer mechanisms illustrate the potential of probiotics as an adjuvant strategy for HCC risk management and treatment.
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) | 0.001 | 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 | 0.001 | 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".