Rapid Isolation and Purification of Secreted Bacteriocins from Streptococcus mutans and Other Lactic Acid Bacteria
Bibliographic record
Abstract
Bacteriocins are small ribosomally synthesized antimicrobial peptides produced by some microorganisms including lactic acid bacteria (LAB), a group of Gram-positive bacteria (cocci, rods) expressing high tolerance for low pH.Bacteriocins kill bacteria rapidly and are biologically active at very low concentrations.Bacteriocins produced by LAB are primarily active against closely related bacterial species.Many bacteriocins have been investigated with respect to their potential use in promoting human, plant, and animal health, and as food biopreservatives.Bacteriocins produced by LAB are particularly interesting since several LAB have been granted GRAS (Generally Recognized as Safe) status.Because it is not always possible to extract active bacteriocins secreted from cells grown in liquid medium, we developed a simple and inexpensive peptide extraction procedure using a semi-solid nutrient-rich agar medium.We hereby present a detailed procedure that leads to the rapid extraction of secreted bioactive bacteriocin peptides from the oral species Streptococcus mutans, a prolific bacteriocin-producing species, and its potential application for bacteriocin extraction from other LAB (e.g., Streptococcus, Lactococcus, Enterococcus).We also present a simple method for the detection of bacteriocin activity from the purified extracellular peptide extract.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".