Method to grow Actinobacillus pleuropneumoniaebiofilm on a biotic surface
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.048 |
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