Biofilm Models: Different Ways of Biofilm Characterization and Drug Discovery
Why this work is in the frame
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Bibliographic record
Abstract
The ability of bacteria to develop biofilms and its added effect on antimicrobial resistance have been a concern for both animal and human medicine. The need to understand biofilm biology has been addressed with the help of three biofilm models, i.e., in vitro, ex vivo, and in vivo. Due to the implications of animal welfare involved in in vivo models, this article is mainly focused on in vitro and ex vivo study models to analyze biofilm biology. In in vitro biofilm models, the microtiter plate and Calgary biofilm device are the most commonly used techniques for biofilm analysis. Quantification of the biofilm biomass generated by these two techniques can be assessed with the help of a crystal violet assay. Although in vitro biofilm models help advance understanding of the biology of biofilm and are easy to perform, they fail to address certain important questions, such as the importance of the substrate on which biofilm grows and the interaction between the organisms and the substrate. To address this concern, an ex vivo model can be utilized to characterize the behavior and characteristics of biofilms on different substrates. Ex vivo biofilm models are considered a bridge between the in vitro and in vivo biofilm models. Although neither of the currently available biofilm assessment models is considered the gold standard, they have significantly increased understanding of biofilm behavior. Further studies are warranted to develop more refined biofilm models. © 2023 Wiley Periodicals LLC. Basic Protocol 1: In vitro biofilm models for microtiter plate/crystal violet assay for biofilm growth assessment Basic Protocol 2: Crystal violet assay/tissue culture plate method for testing of antibiofilm agents Alternate Protocol: Calgary biofilm device to determine biofilm susceptibility to antimicrobial agents Basic Protocol 3: Ex vivo biofilm skin models: canine/porcine skin explants.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it