Method to grow Actinobacillus pleuropneumoniaebiofilm on a biotic surface
Bibliographic record
Abstract
BACKGROUND: Actinobacillus pleuropneumoniae is a Gram-negative bacterium and a member of the Pasteurellaceae family. This bacterium is the causative agent of porcine pleuropneumonia, which is a highly contagious respiratory disease causing important economical losses to the worldwide pig industry. It has been shown that A. pleuropneumoniae can form biofilms on abiotic surfaces (plastic and glass). Although in vitro models are extremely useful to gain information on biofilm formation, these models may not be representative of the conditions found at the mucosal surface of the host, which is the natural niche of A. pleuropneumoniae. RESULTS: In this paper, we describe a method to grow A. pleuropneumoniae biofilms on the SJPL cell line, which represents a biotic surface. A non-hemolytic, non-cytotoxic mutant of A. pleuropneumoniae was used in our assays and this allowed the SJPL cell monolayers to be exposed to A. pleuropneumoniae for longer periods. This resulted in the formation of biofilms on the cell monolayer after incubations of 24 and 48 h. The biofilms can be stained with fluorescent probes, such as a lectin against the polymer of N-acetyl-D-glucosamine present in the biofilm matrix, and easily observed by confocal laser scanning microscopy. CONCLUSIONS: This is the first protocol that describes the formation of an A. pleuropneumoniae biofilm on a biotic surface. The advantage of this protocol is that it can be used to study biofilm formation in a context of host-pathogen interactions. The protocol could also be adapted to evaluate biofilm inhibitors or the efficacy of antibiotics in the presence of biofilms.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".