Flavonoids-Rich Plant Extracts Against Helicobacter pylori Infection as Prevention to Gastric Cancer
Bibliographic record
Abstract
Gastric cancer is the fifth most common and fourth type to cause the highest mortality rates worldwide. The leading cause is related to Helicobacter pylori ( H. pylori ) infection. Unfortunately, current treatments have low success rates, highlighting the need for alternative treatments against carcinogenic agents, specifically H. pylori . Noteworthy, natural origin products contain pharmacologically active metabolites such as flavonoids, with potential antimicrobial applications. Objective: This article overviews flavonoid-rich extracts’ biological and pharmacological activities. It focuses on using these substances against Helicobacter pylori infection to prevent gastric cancer. For this, PubMed and Science Direct databases were searched for studies that reported the activity of flavonoids against H. pylori , published within a 10-year time frame (2010 to August 2020). It resulted in 1,773 publications, of which 44 were selected according to the search criteria. The plant family primarily found in publications was Fabaceae (9.61%). Among the flavonoids identified after extraction, the most prevalent were quercetin (19.61%), catechin (13.72), epicatechin (11.76), and rutin (11.76). The potential mechanisms associated with anti- H. pylori activity to the extracts were: inhibition of urease, damage to genetic material, inhibition of protein synthesis, and adhesion of the microorganism to host cells. Conclusion: Plant extracts rich in flavonoids with anti- H. pylori potential proved to be a promising alternative therapy source, reinforcing the relevance of studies with natural products.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".