Novel Multiplex PCR Assay for Characterization and Concomitant Subtyping of Staphylococcal Cassette Chromosome<i>mec</i>Types I to V in Methicillin-Resistant<i>Staphylococcus aureus</i>
Bibliographic record
Abstract
Staphylococcal cassette chromosome mec (SCCmec) typing is essential for understanding the molecular epidemiology of methicillin-resistant Staphylococcus aureus (MRSA). SCCmec elements are currently classified into types I to V based on the nature of the mec and ccr gene complexes, and are further classified into subtypes according to their junkyard region DNA segments. Previously described traditional SCCmec PCR typing schemes require multiple primer sets and PCR experiments, while a previously published multiplex PCR assay is limited in its ability to detect recently discovered types and subtypes such as SCCmec type V and subtypes IVa, b, c, and d. We designed new sets of SCCmec type- and subtype-unique and specific primers and developed a novel multiplex PCR assay allowing for concomitant detection of the methicillin resistance (mecA gene) (also serving as an internal control) to facilitate detection and classification of all currently described SCCmec types and subtypes I, II, III, IVa, b, c, d, and V. Our assay demonstrated 100% sensitivity and specificity in accurately characterizing 54 MRSA strains belonging to the various known SCCmec types and subtypes, when compared with previously described typing methods. Further application of our assay in 453 randomly selected local clinical isolates confirmed its feasibility and practicality. This novel assay offers a rapid, simple, and feasible method for SCCmec typing of MRSA, and may serve as a useful tool for clinicians and epidemiologists in their efforts to prevent and control infections caused by this organism.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".