Oral bacteria in biofilms exhibit slow reactivation from nutrient deprivation
Bibliographic record
Abstract
The ability of oral bacteria to enter a non-growing state is believed to be an important mechanism for survival in the starved micro-environments of the oral cavity. In this study, we examined the reactivation of nutrient-deprived cells of two oral bacteria in biofilms, Streptococcus anginosus and Lactobacillus salivarius. Non-growing cells were generated by incubation in 10 mM potassium phosphate buffer for 24 h and the results were compared to those of planktonic cultures. When both types of cells were shifted from a rich, peptone-yeast extract-glucose (PYG) medium to buffer for 24 h, dehydrogenase and esterase activity measured by the fluorescent dyes 5-cyano-2,3-ditolyl-tetrazolium chloride (CTC) and fluorescein diacetate (FDA), respectively, was absent in both species. However, the membranes of the vast majority of nutrient-deprived cells remained intact as assessed by LIVE/DEAD staining. Metabolic reactivation of the nutrient-deprived biofilm cells was not observed for at least 48 h following addition of fresh PYG medium, whereas the non-growing planktonic cultures of the same two strains were in rapid growth in less than 2 h. At 72 h, the S. anginosus biofilm cells had recovered 78 % of the dehydrogenase activity and 61 % of the esterase activity and the biomass mm(-2) had increased by 30-35 %. With L. salivarius at 72 h, the biofilms had recovered 56 % and 75 % of dehydrogenase and esterase activity, respectively. Reactivation of both species in biofilms was enhanced by removal of glucose from PYG, and S. anginosus cells were particularly responsive to yeast extract (YE) medium. The data suggest that the low reactivity of non-growing biofilm cells to the introduction of fresh nutrients may be a survival strategy employed by micro-organisms in the oral cavity.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".