Matrix-assisted laser desorption ionization time-of-flight mass spectrometry and database for identification of <i>Legionella</i> species <sup>1</sup>This study was presented in part at the 110th American Society for Microbiology Annual Meeting, 23–27 May 2010, San Diego, California.
Bibliographic record
Abstract
More than 20 species of Legionella have been identified in relation to human infections. Rapid detection and identification of Legionella isolates is clinically useful to differentiate between infection and contamination and to determine treatment regimens. We explored the use of matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) Biotyper system (Bruker Daltonik GmbH, Bremen, Germany) for the identification of Legionella species. The MALDI MS spectra were generated and compared with the Biotyper database, which includes 25 Legionella strains covering 22 species and four Legionella pneumophila serogroups. A total of 83 blind-coded Legionella strains, consisting of 54 reference and 29 clinical strains, were analyzed in the study. Overall, the Biotyper system correctly identified 51 (61.4%) of all strains and isolates to the species level. For species included in the Biotyper database, the method identified 51 (86.4%) strains out of 59 Legionella strains to the correct species level, including 24 (100%) L. pneumophila and 27 (77.1%) non-L. pneumophila strains. The remaining 24 Legionella strains, belonging to species not covered by the Biotyper database, were either identified to the Legionella genus level or had no reliable identification. The Biotyper system produces constant and reproducible MALDI MS spectra for Legionella strains and can be used for rapid and accurate Legionella identification. More Legionella strains, especially the non-L. pneumophila strains, need to be included in the current Biotyper database to cover varieties of Legionella species and to increase identification accuracy.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".