Rapid identification of community-associated methicillin-resistant Staphylococcus aureus by Fourier transform infrared spectroscopy
Bibliographic record
Abstract
The emergence of community-associated methicillin-resistant Staphylococcus aureus (CA-MRSA) carrying Panton-Valentine leukocidin is a worldwide problem. Their identification is based currently on costly and complicated molecular methods. This article describes a simple method for differentiating CA-MRSA from hospital-associated (HA) epidemic MRSA pulsed-field gel electrophoresis types using Fourier transform infrared (FTIR) spectroscopy. The 47 CA-MRSA isolates included 3 Southwest Pacific (resembling USA1100), 24 CMRSA7 (resembling USA400/MW2), 19 CMRSA10 (resembling USA300), and 1 European ST80, while HA-MRSA were represented by 27, 16, 11, 15, 7, and 8 Canadian epidemic isolates CMRSA1 through CMRSA6 respectively, plus 25 nontyped Canadian HA-MRSA. Principal component analysis (PCA), self-organized maps (SOMs), and the K-nearest neighbor (KNN) method were used to cluster the isolates based on chemometric analysis of FTIR spectra of dried films of stationary-phase cells grown on Que-Bact® Universal Medium No. 2 (Quelab Laboratories, Montreal, QC, Canada). First-derivative normalized data from a single narrow spectral region (1361-1236 cm(-1), suggesting differences in protein amide III and nucleic acid phosphodiester contents) allowed 98% correct classification by KNN, 93% by SOMs, and 92% by PCA. FTIR spectroscopic analysis of cells grown on Que-Bact® Universal Medium No. 2 offers a rapid and simple alternative to molecular methods for routine identification of CA-MRSA epidemic isolates.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".