Optimalizace metody vedoucí k hodnocení citlivosti biofilm formujících stafylokoků vůči kandidátním antimikrobním látkám
Bibliographic record
Abstract
Charles University Faculty of Pharmacy in Hradec Králové Study program: Healthcare Bioanalytics Author: Bc. Adéla Diepoltová Supervisor: RNDr. Klára Konečná, Ph.D. Title of thesis: Optimization of the method for sensitivity evaluation of biofilm- forming staphylococci against candidate antimicrobial compounds Background: The aim of this thesis was to optimize approach for in vitro formation of staphylococcal biofilms on the pegs and on the wells of the 96-well panel as an analogous approach to commercially available Calgary Biofilm Device system. The aim of the Experiment 1 was to evaluate incubation conditions (such as impact of a growth medium, incubation mode, optical density of the starting bacterial inoculum and type of surface) leading to maximal biofilm formation of two biofilm producer strains with unknown biofilm phenotype and one staphylococcal strain known as strong biofilm producer. The most advisable conditions were used in incubation process of Experiment 2. This work should propose the approach leading to in vitro formation of the most voluminous staphylococcal biofilms exploitable for candidate drug antimicrobial activity testing. Methods: Spectrophotometric measurement of the crystal violet colour extracted from wells with fixed and stained Staphylococci to evaluate the ability to...
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".