Quantifying the Effects of Antimicrobials on <em>In vitro</em> Biofilm Architecture using COMSTAT Software
Bibliographic record
Abstract
Biofilms are aggregates of microorganisms that rely on a self-produced matrix of extracellular polymeric substance for protection and structural integrity. The nosocomial pathogen, Pseudomonas aeruginosa, is known to adopt a biofilm mode of growth, causing chronic pulmonary infection in patients with cystic fibrosis (CF). The computer program, COMSTAT, is a useful tool for quantifying antimicrobial-induced changes in P. aeruginosa biofilm architecture by extracting data from three-dimensional confocal images. However, standardized operation of the software is less commonly addressed, which is important for optimal reporting of biofilm behavior and cross-center comparison. Thus, the aim of this protocol is to provide a simple and reproducible framework for quantifying in vitro biofilm structures under varying antimicrobial conditions via COMSTAT. The technique is modeled using a CF P. aeruginosa isolate, grown in the form of biofilm replicates, and exposed to tobramycin and the anti-Psl monoclonal antibody, Psl0096. The step-by-step approach aims to reduce user ambiguity and minimize the chance of overlooking crucial image-processing steps. Specifically, the protocol emphasizes the elimination of subjective variations associated with the manual operation of COMSTAT, including image segmentation and the selection of appropriate quantitative analysis functions. Although this method requires users to spend additional time processing confocal images prior to running COMSTAT, it helps minimize misrepresented biofilm heterogenicity in automated outputs.
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 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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".