Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy for Rapid Identification of Non-Fermenting Gram-Negative Bacilli Isolated from Patients with Cystic Fibrosis
Bibliographic record
Abstract
Chronic respiratory infections with non-fermenting Gram-negative bacilli are a key feature of cystic fibrosis (CF). For microbiology laboratories, rapid and accurate identification of these bacteria is often challenging and labor intensive. This study was undertaken to evaluate whether attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy could rapidly discriminate Pseudomonas aeruginosa (mucoid and non-mucoid), Burkholderia cepacia complex, Burkholderia gladioli, Achromobacter spp. and Stenotrophomonas maltophilia. A total of 263 well-characterized clinical strains isolated from respiratory samples of patients with CF attending the CHU Sainte-Justine CF clinic were included in this study, consisting of 70 P. aeruginosa, 83 Burkholderia spp., 52 Achromobacter spp. and 58 Stenotrophomonas maltophilia isolates from the biobank. Isolates were thawed and sub-cultured twice on sheep blood (5%) agar. ATR-FTIR spectral acquisition was performed in triplicate for each isolate. Multivariate statistical analysis of the ATR-FTIR spectra was performed by hierarchical cluster analysis (HCA) and principal component analysis (PCA) in conjunction with the use of a feature selection algorithm. An ATR-FTIR spectral database consisting of 789 spectra of P. aeruginosa, Burkholderia spp., Achromobacter spp. and Stenotrophomonas maltophilia was created in this study. Complete discrimination among all four genera as well as among three species within the B. cepacia complex and B. gladioli was achieved based on HCA and PCA of the spectra in the database. ATR-FTIR analysis of a validation set consisting of 30 isolates was conducted in parallel with identification by MALDI-TOF mass spectrometry and yielded >95% concordance between the two techniques. ATR-FTIR spectroscopy is a promising tool for rapid, inexpensive and accurate identification of non-fermenting Gram-negative bacilli. Additional work is needed to further expand the spectral database, particularly with mucoid strains. All authors: No reported disclosures.
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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 | 0.000 | 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 | 0.001 | 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".