Biofilm Models: Different Ways of Biofilm Characterization and Drug Discovery
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".