Untargeted accurate identification of highly pathogenic bacteria directly from blood culture flasks
Bibliographic record
Abstract
To improve the preparedness against exposure to highly pathogenic bacteria and to anticipate the wide variety of bacteria that can cause bloodstream infections (BSIs), a safe, unbiased and highly accurate identification method was developed. Our liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based method can identify highly pathogenic bacteria, their near-neighbors and bacteria that are common causes of BSIs directly from positive blood culture flasks. The developed Peptide-Based Microbe Detection Engine (http://proteome2pathogen.com) relies on a two-step workflow: a genus-level search followed by a species-level search. This strategy enables the rapid identification of microorganisms based on the analyzed proteome. This method was successfully used to identify strains of Bacillus anthracis, Brucella abortus, Brucella melitensis, Brucella suis, Burkholderia pseudomallei, Burkholderia mallei, Francisella tularensis, Yersinia pestis and closely related species from simulated blood culture flasks. This newly developed LC-MS/MS method is a safe and rapid method for accurately identifying bacteria directly from positive blood culture flasks.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".