Rapid identification of Burkholderia cepacia complex species including strains of the novel Taxon K, recovered from cystic fibrosis patients by intact cell MALDI-ToF mass spectrometry
Bibliographic record
Abstract
Two approaches based on intact cell matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (IC-MALDI-ToF MS) have been evaluated in order to discriminate and identify nine former Burkholderia cepacia complex (Bcc) species, Burkholderia contaminans belonging to the novel Taxon K, Burkholderia gladioli, and the most relevant non-fermentative (NF) Gram-negative rods recovered from cystic fibrosis (CF) sputum cultures. In total, 146 clinical isolates and 26 reference strains were analysed. IC mass spectra were obtained with high reproducibility applying a recently developed inactivation protocol which is based on the extraction of microbial proteins by trifluoroacetic acid (TFA). In a first approach, spectral analysis was carried out by means of a gel-view representation of mass spectra, which turned out to be useful to recognize specific identifying biomarker proteins (SIBPs). A series of prominent mass peaks, mainly assigned to constitutively expressed proteins, were selected as SIBPs for identifications at the genus and species level. Two distinctive mass peaks present in B. contaminans spectra (7501 and 7900 Da) were proposed as SIBPs for the identification of this novel species. A second approach of spectral analysis based on data reduction, feature selection and subsequent hierarchical cluster analysis was used to obtain an objective discrimination of all species analysed. Both complementary modalities of analyzing complex IC-MALDI-ToF MS data open the path towards a rapid, accurate and objective means of routine clinical microbiology diagnosis of pathogens from sputum samples of CF patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".