Optimalizace metodiky vedoucí k hodnocení citlivosti biofilm formujících mikrobiálních agens vůči látkám s antimikrobním potenciálem
Bibliographic record
Abstract
Charles University Faculty of Pharmacy in Hradec Králové Study program: Pharmacy Candidate: Jana Roubalová Consultant: RNDr. Klára Konečná, Ph.D. Title of thesis: Optimization of methods for sensitivity evaluation of biofilm-forming microbial agents towards substances with antimicrobial potential Background: The aim of this diploma thesis was to optimize the methodical approach to the production of yeast biofilms in vitro on the so-called pegs and the walls of the wells of the 96-well panel. This biofilm formation system should be an analogous approach to the commercially available Calgary Biofilm Device. 10 strains of Candida yeast and 4 different culture media (nutritionally poor / rich) were used to optimize the methodology. Both the reference strains and the clinical isolates were among the yeast strains tested. Methods: The ability to form yeast biofilms by different strains in different culture media was evaluated by the approach of fixation, staining of the formed biofilms by crystal violet and extraction and spectrophotometric measurement of the intensity of the extracted color. Results: Individual strains, after cultivation in different media, were categorized according to their ability to form biofilm. Larger yeast adherence occurs in the wells than on pegs where the yeast adhered very...
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".