Evaluation of MALDI-TOF for identification of Vibrio cholerae and Vibrio parahaemolyticus from growth on agar media
Bibliographic record
Abstract
Two methods were compared for their ability to accurately identify Vibrio species of interest: whole genome sequencing as the reference method and MALDI-TOF MS (matrix-assisted laser desorption/ionization-time of flight mass spectrometry) proteome fingerprinting. The accuracy of mass spectrometry-based identification method was evaluated for its ability to accurately identify isolates of Vibrio cholerae and Vibrio parahaemolyticus. Identification result of each isolate obtained by mass spectrometry was compared to identification by whole genome sequencing (WGS). The MALDI-TOF MS system had excellent performance for identification of V. cholerae and V. parahaemolyticus isolates grown on a non-selective solid agar media. Unlike the biochemical characterization performed by API20E. In this study, 161 isolates (V. cholerae, n = 33; V. parahaemolyticus, n = 102; V. spp., n = 23; other enteropathogens, Salmonella and E. coli, n = 3) were used to assess accuracy. The MALDI-TOF MS system was able to accurately identify 100% (33/33) of the V. cholerae isolates and 99.9% (101/102) of V. parahaemolyticus isolates, with 100% for both sensitivity and specificity for V. cholerae and 99% sensitivity and 98% specificity for V. parahaemolyticus. Thus, mass spectrometry for bacterial identification is comparable to the WGS. Furthermore, in comparison to a biochemical characterization, the use of MALDI-TOF MS system shortens the analysis time from over 72 h to less than 24 h. KEY POINTS: • V. cholerae and V. parahaemolyticus were successfully ID-ed by MALDI-TOF • MALDI-TOF sensitivity and specificity parallels the WGS method of identification • MALDI-TOF is several days faster than the battery of culture-dependent methods.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".