Distribution of Staphylococcus aureus drug resistance genes, biofilm formation and cell wall characteristics in dairy cattle from dairy farms in Northeast China
Bibliographic record
Abstract
The spread of drug-resistant Staphylococcus aureus(S.aureus) in cattle herds not only threatens livestock production but also becomes an important route for the transmission of drug-resistant bacteria to humans, posing significant public health risks and epidemiological significance. In this study, a total of 282 clinical samples were collected form cattle farms in Heilongjiang, Jilin, and Inner Mongolia, and isolation of S. aureus from 62 samples. Through spa typing and disk diffusion assays of 62 isolates, the predominant spa type was identified as t034. Notably, 54 isolates exhibited multidrug resistance, with a penicillin resistance rate as high as 87.1%, and 34 isolates were identified as methicillin-resistant S. aureus (MRSA). Among the 11 resistance genes tested, the detection rate of tetM gene was 100%. The Congo red plates and crystal violet staining assay showed that the isolated strains all produced biofilms, with no significant changes in the cell wall. In addition, icaA, SarA, and CidA genes identified as critical regulators of biofilm formation. Results from this study suggest that the feces and milk of dairy cows in Northeast China were contaminated with S. aureus including MRSA strains, and these strains carry a variety of resistance genes and have the ability to form biofilms, showing a resistance phenotype to a variety of antimicrobial agents.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".