Ready, Set, Type! Proteomics vs Agglutination for Escherichia coli H Antigen Confirmation
Bibliographic record
Abstract
Initial detection and reporting by clinical microbiology laboratories is a sentinel marker for foodborne outbreak surveillance systems. Initiation of a public health investigation is reliant on the rapid initial identification of pathogens of interest (Fig. 1). Diagnostics for Escherichia coli have evolved to reduce identification turnaround time, incorporating technologies for rapid identification (MALDI-TOF MS) and serogrouping (O157 antiserum or latex agglutination) (1). Reporting of these isolates to a public health agency may initiate further laboratory investigations, such as pulsed-field gel electrophoresis, for confirmation that isolates may be related to a common source (clonal population). In addition, H antigen flagellar antigen testing with serotyping, or whole-genome sequencing, can be performed to confirm E. coli O157:H7 and other outbreak-associated strains. Fig. 1. Investigation of an E. coli outbreak [adapted from CDC (5)]. Although methods used at either end of the outbreak investigation spectrum—initial bacterial identification by MALDI-TOF MS and confirmation of clonality by next-generation sequencing—have evolved considerably in the last decade, the intermediate steps of identifying possible outbreak organisms with O and H antigen typing have remained relatively unchanged since the 1940s. This is where novel approaches, such as the mass spectrometric H antigen typing protocol (MS-H)2 presented in this issue of Clinical Chemistry by Cheng and colleagues (2) can complement and update existing workflows for outbreak investigations. MS-H is a qualitative proteomics approach for typing H …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.090 | 0.160 |
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".