How Noninvasive Pathogens Induce Disease: Lessons from Enteropathogenic and Enterohemorrhagic <i>Escherichia Coli</i>
Bibliographic record
Abstract
A novel focus of the work has been on defining the molecular and cellular mechanisms underlying the interactions between bacterial pathogens and host cells. During infection, enteropathogenic Escherichia coli (EPEC) and enterohemorrhagic E. coli (EHEC) induce a characteristic “attaching and effacing” (A/E) histopathology on gut enterocytes. Since studies investigating the function of EPEC's virulence factors are the most advanced, this chapter deals with EPEC as the prototype for the family of A/E-inducing pathogens. EPEC infection is estimated to cause the deaths of several hundred thousand children per year owing to dehydration and other complications. First widely recognized as the causative agent of hamburger disease, EHEC is a zoonotic pathogen that appears to be asymptomatically carried by various ruminants. Mutants lacking the bundle-forming pilus (BFP) plasmid still attach to host cells, but do not form microcolonies and produce fewer A/E lesions than wild-type EPEC. Immunofluorescence studies have shown that in addition to membrane-bound Tir, the tips of EPEC pedestals contain predominantly filamentous (F)-actin, as well as talin, α- actinin, ezrin, and several other cytoskeletal proteins. Diarrhea is undoubtedly the most prominent and widespread symptom associated with both EPEC and EHEC infection. Approaches using molecular biology, genetics, and cell biology have provided many new insights into how EPEC and related pathogens interact with and exploit host cells during the course of infection and how this ultimately leads to disease.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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".