Antibacterial and Anti-Virulence Effects of Furazolidone on Trueperella Pyogenes and Pseudomonas Aeruginosa
Bibliographic record
Abstract
Abstract Background: Trueperella pyogenes and Pseudomonas aeruginosa are two important bacterial pathogens closely relating to the occurrence and development of forest musk deer respiratory purulent disease. Although T. pyogenes is the causative agent of the disease, the subsequently invaded P. aeruginosa will predominate the infection by producing a substantial amount of quorum-sensing (QS)-controlled virulence factors, and co-infection of them usually creates serious difficulties for veterinary treatment. In order to find a potential drug that targets both T. pyogenes and P. aeruginosa, the antibacterial and anti-virulence capacities of 55 compounds, which have similar core structure to the signal molecules of P. aeruginosa QS system, were tested in this study. By performing a series of in vitro screening experiments to assess the effects of these compounds.Results: We identified that furazolidone could significantly inhibit the growth of mono-cultured T. pyogenes or in the co-culture with P. aeruginosa. Although the growth of P. aeruginosa could also be moderately inhibited by furazolidone, the results of phenotypic identification and transcriptomic analysis further revealed that furazolidone had remarkable inhibitory effect on the biofilm production, motility, and QS system of P. aeruginosa. Moreover, furazolidone could efficiently protect Caenorhabditis elegans from P. aeruginosa infection under both fast-killing and slow-killing conditions.Conclusions: This study reports the antibacterial and anti-virulence abilities of furazolidone on T. pyogenes and P. aeruginosa, and provides a promising strategy and molecular basis for the development of novel anti-infectious drugs to dealing with forest musk deer purulent disease, or other diseases caused by T. pyogenes and P. aeruginosa co-infection.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".