Identification of antibiotic-resistant staphylococci and epidemiological typing of methicillin-resistant Staphylococcus aureus by Fourier transform infrared spectroscopy
Bibliographic record
Abstract
Staphylococci strains are among the most widespread multidrug-resistant nosocomial pathogens in Canada. Rapid and accurate identification and epidemiological typing of methicillin-resistant S. aureus (MRSA) and its discrimination from coagulase-negative staphylococci (CNS) and glycopeptide-intermediate S. aureus (GISA) are crucial for appropriate therapy and for monitoring and limiting intra- and inter-hospital spread of epidemic MRSA strains. Although pulsed-field gel electrophoresis and polymerase chain reaction methods for the identification of MRSA are reliable, they are technically demanding, time-consuming and inappropriate for routine clinical diagnosis. Moreover, no reliable method exists for discrimination of epidemic MRSA from sporadic MRSA and from GISA strains. The objective of the research described in this thesis was to investigate whether Fourier transform infrared (FTIR) spectroscopy could be used to distinguish MRSA from methicillin-susceptible S. aureus, borderline oxacillin-resistant S. aureus (BORSA), CNS, including methicillin-resistant CNS, and GISA. The application of FTIR spectroscopy for epidemiological typing of Canadian epidemic MRSA (CMRSA) strains as well as their discrimination from sporadic MRSA was also assessed. FTIR spectra were recorded from intact stationary-phase cells grown on Universal Medium (UM™) and deposited and dried on a ZnSe optical window, normalized, and converted to first-derivative spectra. Various chemometric approaches were employed to cluster the different phenotypes of staphylococci species and to subtype five CMRSA strains based on the similarity of their infrared spectral fingerprints in narrow spectral regions selected by visual inspection and by employing a singularvalue decomposition (SVD) algorithm. Pairwise separation of MRSA from MSSA, BORSA, CNS, MRCNS, and GISA was accomplished by using principal component analysis (PCA), self-organizing maps (SOM), and the K-nearest neighbors (KNN) algorithm. These chemometric techniques were also successfully employed for epidemiological typing of the five CMRSA strains and their discrimination from sporadic MRSA strains using a combination of different optimal spectral regions selected by SVD. These results demonstrate that FTIR spectroscopy has considerable potential as a rapid method for the identification of different phenotypes of staphylococci and epidemiological typing of MRSA.
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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 | 0.001 |
| 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) | 0.001 | 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".