Phenotypic H-Antigen Typing by Mass Spectrometry Combined with Genetic Typing of H Antigens, O Antigens, and Toxins by Whole-Genome Sequencing Enhances Identification of Escherichia coli Isolates
Bibliographic record
Abstract
Mass spectrometry-based phenotypic H-antigen typing (MS-H) combined with whole-genome-sequencing-based genetic identification of H antigens, O antigens, and toxins (WGS-HOT) was used to type 60 clinical Escherichia coli isolates, 43 of which were previously identified as nonmotile, H type undetermined, or O rough by serotyping or having shown discordant MS-H and serotyping results. Whole-genome sequencing confirmed that MS-H was able to provide more accurate data regarding H antigen expression than serotyping. Further, enhanced and more confident O antigen identification resulted from gene cluster based typing in combination with conventional typing based on the gene pair comprising wzx and wzy and that comprising wzm and wzt The O antigen was identified in 94.6% of the isolates when the two genetic O typing approaches (gene pair and gene cluster) were used in conjunction, in comparison to 78.6% when the gene pair database was used alone. In addition, 98.2% of the isolates showed the existence of genes for various toxins and/or virulence factors, among which verotoxins (Shiga toxin 1 and/or Shiga toxin 2) were 100% concordant with conventional PCR based testing results. With more applications of mass spectrometry and whole-genome sequencing in clinical microbiology laboratories, this combined phenotypic and genetic typing platform (MS-H plus WGS-HOT) should be ideal for pathogenic E. coli typing.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".