Evaluation of Fourier transform infrared spectroscopy for the rapid identification of glycopeptide-intermediate Staphylococcus aureus
Bibliographic record
Abstract
OBJECTIVES: To evaluate Fourier transform infrared (FTIR) spectroscopy as a rapid method for distinguishing glycopeptide-intermediate Staphylococcus aureus (GISA) from glycopeptide-susceptible methicillin-resistant S. aureus (MRSA) and to compare three data analysis methods. METHODS: First-derivative normalized spectra of dried films of bacterial growth on Que-Bact Universal Medium No. 2 were examined by singular value decomposition to identify key spectral regions. Region selection was analysed by principal component analysis (PCA), self-organizing maps (SOMs) and the K-nearest neighbour (KNN) algorithm. The initial data set included 35 GISA (including GISA Mu50 and heterogeneous GISA Mu3) and 25 epidemic MRSA. The regions were then tested using enlarged data sets that included 22 sporadic and 85 additional epidemic MRSA. RESULTS: Epidemic MRSA and GISA/hGISA were separated into two distinct clusters on the basis of spectral data from regions 1352-1315 and 1480-1460 cm(-1), the former providing 100% correct classification by all three analyses and the latter providing 96.67% correct by PCA, 98.34% by SOM and 100% by KNN. The 1480-1460 cm(-1) region was more effective for distinguishing GISA/hGISA from a set combining sporadic and epidemic MRSA, with two GISA/hGISA and four sporadic MRSA misclassified by PCA and SOM (92.69% correct), while the KNN method misclassified three of the four sporadic MRSA (93.90% correct). The addition of 85 other epidemic MRSA this set increased the fraction of correctly classified isolates to 96.41% and 97.01% by PCA, SOM and KNN, respectively. CONCLUSIONS: As only 6 of 167 isolates were misclassified, FTIR spectroscopy may provide means of rapid and accurate identification of GISA and hGISA among isolates of MRSA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 teacher head, 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".