Insights into the Emergent Bacterial Pathogen Cronobacter spp., Generated by Multilocus Sequence Typing and Analysis
Bibliographic record
Abstract
Cronobacter spp. (previously known as Enterobacter sakazakii) is a bacterial pathogen affecting all age groups, with particularly severe clinical complications in neonates and infants. One recognized route of infection being the consumption of contaminated infant formula. As a recently recognized bacterial pathogen of considerable importance and regulatory control, appropriate detection, and identification schemes are required. The application of multilocus sequence typing (MLST) and analysis (MLSA) of the seven alleles atpD, fusA, glnS, gltB, gyrB, infB, and ppsA (concatenated length 3036 base pairs) has led to considerable advances in our understanding of the genus. This approach is supported by both the reliability of DNA sequencing over subjective phenotyping and the establishment of a MLST database which has open access and is also curated; http://www.pubMLST.org/cronobacter. MLST has been used to describe the diversity of the newly recognized genus, instrumental in the formal recognition of new Cronobacter species (C. universalis and C. condimenti) and revealed the high clonality of strains and the association of clonal complex 4 with neonatal meningitis cases. Clearly the MLST approach has considerable benefits over the use of non-DNA sequence based methods of analysis for newly emergent bacterial pathogens. The application of MLST and MLSA has dramatically enabled us to better understand this opportunistic bacterium which can cause irreparable damage to a newborn baby's brain, and has contributed to improved control measures to protect neonatal health.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".