Mass Spectrometry–Based Escherichia coli H Antigen/Flagella Typing: Validation and Comparison with Traditional Serotyping
Bibliographic record
Abstract
BACKGROUND: Escherichia coli H antigen typing with antisera, a useful method for flagella clinical identification and classification, is a time-consuming process because of the need to induce flagella growth and the occurrence of undetermined strains. We developed an alternative rapid and analytically sensitive mass spectrometry (MS) method, termed MS-based H antigen typing (MS-H), and applied it at the protein sequence level for H antigen typing. We also performed a comparison with traditional serotyping on reference strains and clinical isolates. METHODS: On the basis of international guidelines, the analytical selectivity and sensitivity, imprecision, correlation, repeatability, and reproducibility of the MS-H platform was evaluated using reference strains. Comparison of MS-H typing and serotyping was performed using 302 clinical isolates from 5 Canadian provinces, and discrepant results between the 2 platforms were resolved through whole genome sequencing. RESULTS: Repeated tests on reference strain EDL933 demonstrated a lower limit of the measuring interval at the subsingle colony (16.97 μg or 1.465 × 10(7) cells) level and close correlation (r(2) > 0.99) between cell culture biomass and sequence coverage. The CV was <10.0% among multiple repeats with 4 reference strains. Intra- and interlaboratory tests demonstrated that the MS-H method was robust and reproducible under various sample preparation and instrumentation conditions. Using discrepancy analysis via whole genome sequencing, performed on isolates with discrepant results, MS-H accurately identified 12.3% more isolates than conventional serotyping. CONCLUSIONS: MS-H typing of E. coli is useful for fast and accurate flagella typing and could be very useful during E. coli outbreaks.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".