Inhibitory Spectra and Modes of Antimicrobial Action of Gallotannins from Mango Kernels (<i>Mangifera indica</i>L.)
Bibliographic record
Abstract
This study investigated the antimicrobial activities and modes of action of penta-, hexa-, hepta-, octa-, nona-, and deca-O-galloylglucose (gallotannins) isolated from mango kernels. The MICs and minimum bactericidal concentrations (MBCs) against food-borne bacteria and fungi were determined using a critical dilution assay. Gram-positive bacteria were generally more susceptible to gallotannins than were Gram-negative bacteria. The MICs of gallotannins against Bacillus subtilis, Bacillus cereus, Clostridium botulinum, Campylobacter jejuni, Listeria monocytogenes, and Staphylococcus aureus were 0.2 g liter(-1) or less; enterotoxigenic Escherichia coli and Salmonella enterica were inhibited by 0.5 to 1 g liter(-1), and lactic acid bacteria were resistant. The use of lipopolysaccharide mutants of S. enterica indicated that the outer membrane confers resistance toward gallotannins. Supplementation of LB medium with iron eliminated the inhibitory activity of gallotannins against Staphylococcus aureus, and siderophore-deficient mutants of S. enterica were less resistant toward gallotannins than was the wild-type strain. Hepta-O-galloylglucose sensitized Lactobacillus plantarum TMW1.460 to hop extract, indicating inactivation of hop resistance mechanisms, e.g., the multidrug resistance (MDR) transporter HorA. Carbohydrate metabolism of Lactococcus lactis MG1363, a conditionally respiring organism, was influenced by hepta-O-galloylglucose when grown under aerobic conditions and in the presence of heme but not under anaerobic conditions, indicating that gallotannins influence the respiratory chain. In conclusion, the inhibitory activities of gallotannins are attributable to their strong affinity for iron and likely additionally relate to the inactivation of membrane-bound proteins.
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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 | 0.000 | 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) | 0.000 | 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".