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.
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 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 | 0.000 | 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 | 0.001 |
| 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 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".