<i>Actinobacillus pleuropneumoniae</i> grows as aggregates in the lung of pigs: is it time to refine our <i>in vitro</i> biofilm assays?
Bibliographic record
Abstract
Actinobacillus pleuropneumoniae causes porcine pleuropneumonia and forms biofilms in vitro on abiotic surfaces; however, presence of biofilms during infections has not been documented. The aim of this study was to use a species-specific fluorescent oligonucleotide probe and confocal microscopy to localize A. pleuropneumoniae in the lungs of two naturally infected pigs. Actinobacillus pleuropneumoniae was detected by fluorescence in situ hybridization and observed to grow as aggregates (~30-45 μm) during a natural infection. As the A. pleuropneumoniae aggregates observed in porcine lungs differed from the biofilms grown on a solid surface obtained in vitro, we designed a new biofilm assay using agarose, a porous substrate, favouring the formation of aggregates. In this study, we described for the first time the mode of growth of A. pleuropneumoniae during a natural infection in pigs. We also propose an in vitro biofilm assay for A. pleuropneumoniae using a porous substrate which allows the formation of aggregates. This assay might be more representative of the in vivo situation, at least in terms of the size of the bacterial aggregates and the presence of a porous matrix, and could potentially be used to test the susceptibility of A. pleuropneumoniae aggregates to antibiotics and disinfectants.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".