The Diversity of Escherichia coli Pathotypes and Vaccination Strategies against This Versatile Bacterial Pathogen
Bibliographic record
Abstract
Escherichia coli (E. coli) is a gram-negative bacillus and resident of the normal intestinal microbiota. However, some E. coli strains can cause diseases in humans, other mammals and birds ranging from intestinal infections, for example, diarrhea and dysentery, to extraintestinal infections, such as urinary tract infections, respiratory tract infections, meningitis, and sepsis. In terms of morbidity and mortality, pathogenic E. coli has a great impact on public health, with an economic cost of several billion dollars annually worldwide. Antibiotics are not usually used as first-line treatment for diarrheal illness caused by E. coli and in the case of bloody diarrhea, antibiotics are avoided due to the increased risk of hemolytic uremic syndrome. On the other hand, extraintestinal infections are treated with various antibiotics depending on the site of infection and susceptibility testing. Several alarming papers concerning the rising antibiotic resistance rates in E. coli strains have been published. The silent pandemic of multidrug-resistant bacteria including pathogenic E. coli that have become more difficult to treat favored prophylactic approaches such as E. coli vaccines. This review provides an overview of the pathogenesis of different pathotypes of E. coli, the virulence factors involved and updates on the major aspects of vaccine development against different E. coli pathotypes.
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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 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".