Direct Repeat Unit ( <i>dru</i> ) Typing of Methicillin-Resistant Staphylococcus pseudintermedius from Dogs and Cats
Bibliographic record
Abstract
Methicillin-resistant Staphylococcus pseudintermedius (MRSP) has emerged in a remarkable manner as an important problem in dogs and cats. However, limited molecular epidemiological information is available. The aims of this study were to apply direct repeat unit (dru) typing in a large collection of well-characterized MRSP isolates and to use dru typing to analyze a collection of previously uncharacterized MRSP isolates. Two collections of MRSP isolates from dogs and cats were included in this study. The first collection comprised 115 well-characterized MRSP isolates from North America and Europe. The data for these isolates included multilocus sequence typing (MLST) and staphylococcal protein A gene (spa) typing results as well as SmaI macrorestriction patterns after pulsed-field gel electrophoresis (PFGE). The second collection was a convenience sample of 360 isolates from North America. The dru region was amplified by PCR, sequenced, and analyzed. For the first collection, the discriminatory indices of the typing methods were calculated. All isolates were successfully dru typed. The discriminatory power for dru typing (D = 0.423) was comparable to that of spa typing (D = 0.445) and of MLST (D = 0.417) in the first collection. Occasionally, dru typing was able to further discriminate between isolates that shared the same spa type. Among all 475 isolates, 26 different dru types were identified, with 2 predominant types (dt9a and dt11a) among 349 (73.4%) isolates. The results of this study underline that dru typing is a useful tool for MRSP typing, being an objective, standardized, sequence-based method that is relatively cost-efficient and easy to perform.
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".