Alkali production associated with malolactic fermentation by oral streptococci and protection against acid, oxidative, or starvation damage
Bibliographic record
Abstract
Alkali production by oral streptococci is considered important for dental plaque ecology and caries moderation. Recently, malolactic fermentation (MLF) was identified as a major system for alkali production by oral streptococci, including Streptococcus mutans. Our major objectives in the work described in this paper were to further define the physiology and genetics of MLF of oral streptococci and its roles in protection against metabolic stress damage. L-Malic acid was rapidly fermented to L-lactic acid and CO(2) by induced cells of wild-type S. mutans, but not by deletion mutants for mleS (malolactic enzyme) or mleP (malate permease). Mutants for mleR (the contiguous regulator gene) had intermediate capacities for MLF. Loss of capacity to catalyze MLF resulted in loss of capacity for protection against lethal acidification. MLF was also found to be protective against oxidative and starvation damage. The capacity of S. mutans to produce alkali from malate was greater than its capacity to produce acid from glycolysis at low pH values of 4 or 5. MLF acted additively with the arginine deiminase system for alkali production by Streptococcus sanguinis, but not with urease of Streptococcus salivarius. Malolactic fermentation is clearly a major process for alkali generation by oral streptococci and for protection against environmental stresses.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".