SYBR green as a fluorescent probe to evaluate the biofilm physiological state of <i>Staphylococcus epidermidis</i>, using flow cytometry
Bibliographic record
Abstract
Staphylococcus epidermidis biofilms with different proportions of viable but nonculturable bacteria were used to show that SYBR green (SYBR) may be used as a probe to evaluate the bacterial physiological state using flow cytometry. Biofilms grown in excess glucose presented significantly higher proportions of dormant bacteria than biofilms grown in excess glucose with buffered pH conditions or with exponential-phase planktonic cultures. Bacteria obtained from biofilms with high or low proportions of viable but nonculturable cells were further cultured in broth medium and stained with SYBR at different time points. An association between bacterial growth and SYBR staining intensity was observed. In addition, bacteria presenting higher SYBR fluorescence intensity also stained more intensely with cyanoditolyl tetrazolium chloride, used as a probe to evaluate cellular metabolism. Accordingly, planktonic bacteria treated with rifampicin, an inhibitor of bacterial RNA transcription, presented lower SYBR and cyanoditolyl tetrazolium chloride staining intensity than nontreated bacteria. Overall, our results indicate that SYBR, in addition to being used as a component of LIVE/DEAD stain, may also be used as a probe to evaluate the physiological state of S. epidermidis cells.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".