Molecular epidemiology and distribution of antimicrobial resistance genes of Staphylococcus species isolated from Chinese dairy cows with clinical mastitis
Bibliographic record
Abstract
Staphylococcus species, categorized into Staphylococcus aureus and non-aureus staphylococci (NAS), are frequent causes of mastitis in dairy cattle around the world. Current treatments using antimicrobials are under increasing scrutiny due to rising prevalence of multi-drug resistance in S. aureus. Objectives of this study were to determine: (1) genetic diversity of Staphylococcus species isolated from clinical mastitis in cows from large Chinese dairy farms; and (2) prevalence and distribution of antimicrobial resistance genes (ARG) in these isolates. Staphylococcus aureus (n = 96) were isolated from 26 herds located in 12 provinces of China, whereas NAS (n = 112) were isolated from 59 herds located in 18 provinces of China. The NAS were identified at the species level using a partial 16S rRNA sequencing method, whereas random amplification of polymorphic DNA (RAPD) PCR was done to determine genetic relationships of isolates. Finally, PCR was used to detect resistance and biofilm formation genes. Staphylococcus chromogenes (33%) was the most common NAS species, followed by Staphylococcus sciuri (17%) and Staphylococcus epidermidis (8%). Staphylococcus aureus was grouped in 12 genotypes, of which 2 types represented 56% of isolates. Staphylococcus chromogenes (n = 37) clustered into 8 RAPD types, with 2 prevalent types containing 73% of isolates. The most prevalent ARG in S. aureus isolates was blaZ (95%), followed by tetM (33%), tetK (31%), ermT (26%), and aacA-aphD (23%). The mecA and vanA were detected in 16 and 4% of isolates, respectively. In NAS, blaZ (100%), mecA (73%), tetK (79%), tetM (96%), mphC (63%), and msrA (54%) were frequently detected. Antimicrobial resistance genes mecA, tetK, tetL, tetM, dfrG, ermB, msrA, mphC, aadD, and aphA3 were more commonly detected in NAS than in S. aureus. Biofilm formation genes (icaA and icaD) were frequently detected in staphylococci isolated from bovine clinical mastitis. The existence of predominant RAPD types in S. aureus and S. chromogenes isolates across Chinese dairy farms indicated that specific genotypes had disseminated within herds and become more udder-adapted. High prevalence of ARG, especially in NAS, highlighted the risk of selection of multi-drug resistant staphylococci with potential as a reservoir of ARG.
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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 | 0.001 |
| 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 | 0.001 |
| 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".