In situ fluorescent staining of yeast biofilms
Bibliographic record
Abstract
Studies of microbial biofilms have focused mostly on bacterial attachment, resulting in an underdeveloped knowledge base for other microorganisms, including yeasts. Comprehension of yeast biofilms may be critical for therapeutic, control and exploitation strategies in clinical, industrial and natural environments. Visualization techniques form a generic part of biofilm studies, and recent development of fluorescent probes have enhanced localization, quantification and metabolic aspects of microorganisms. Limited application of these probes in yeast biofilms, however, has been reported. We evaluated a series of cell wall stains, live/dead probes, and yeast mitochondrial probes using both planktonic and biofilm cells of species of Cryptococcus , Saccharomyces and Schizosaccharomyces , including comparison with a filamentous fungus, Penicillium glabrum . Our results confirm the difference in metabolic functioning of planktonic versus attached matrix‐enclosed cells, with different concentrations of fluorescent probes, as well as staining periods, applicable to the two microbial lifestyles. Shortcomings in fluorophore properties, with fast photobleaching, and background fluorescence, are also indicated. Exopolymeric interference in visualization, and recalcitrance of biofilms to penetration of stains, are highlighted. In conclusion, the spectrum of parameter variation, and deviation from supplier information, exemplify the need for empirical evaluation before significant results can be obtained when staining biofilms.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".