Role of biofilms in antimicrobial resistance of the bacterial bovine respiratory disease complex
Bibliographic record
Abstract
An increase in chronic, non-responsive bovine respiratory disease (BRD) infections in North American feedlot cattle is observed each fall, a time when cattle are administered multiple antimicrobial treatments for BRD. A number of factors are responsible for BRD antimicrobial treatment failure, with formation of biofilms possibly being one. It is widely accepted that biofilms play a role in chronic infections in humans and it has been hypothesized that they are the default lifestyle of most bacteria. However, research on bacterial biofilms associated with livestock is scarce and significant knowledge gaps exist in our understanding of their role in AMR of the bacterial BRD complex. The four main bacterial species of the BRD complex, Mannheimia haemolytica, Pasteurella multocida, Histophilus somni, and Mycoplasma bovis are able to form biofilms in vitro and there is evidence that at least H. somni retains this ability in vivo. However, there is a need to elucidate whether their biofilm-forming ability contributes to pathogenicity and antimicrobial treatment failure of BRD. Overall, a better understanding of the possible role of BRD bacterial biofilms in clinical disease and AMR could assist in the prevention and management of respiratory infections in feedlot cattle. We review and discuss the current knowledge of BRD bacteria biofilm biology, study methodologies, and their possible relationship to AMR.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".