Antibacterial Activity of Bioactive Compounds of Green Coffee Beans on Periodontogenic and Nosocomial Bacteria
Bibliographic record
Abstract
The emergence of antimicrobial resistance and the side effects of synthetic drugs have raised an interest in searching for new antimicrobial compounds. The present study aims to evaluate in vitro antibacterial activity of green coffee and its active compounds (chlorogenic acid extract and caffeine extract) against some periodontogenic and nosocomial bacteria. The bioactive compounds, viz. chlorogenic acid and caffeine, were extracted through soxhlet extraction using methanol and water, respectively, and HPLC UV quantified these compounds. The study reported 3 CQA, 4 CQA, and 5 CQA as the significant chlorogenic acids in green coffee beans. Aqueous extract of green coffee beans (AGCB), which is dominant in caffeine, has been found to be the least effective against both periodontal and nosocomial bacteria. The result of our study revealed that the methanol extract of green coffee bean (MGCB), rich in chlorogenic acid, exhibits the highest inhibitory activity against periodontogenic bacteria, followed by the ethanol extract of green coffee bean (EGCB) and AGCB extract. EGCB extract was significantly effective against Staphylococcus epidermidis among selected nosocomial pathogens. AGCB extract was least effective against all bacteria. The results highlight that green coffee polyphenols, especially chlorogenic acid, could be used as antimicrobial agents in different biotechnological applications. The antibacterial property of green coffee highlights its potential as a naturally active antibacterial compound.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".