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Record W2062445136 · doi:10.1097/mog.0b013e328333d73b

Bacterial infections: new and emerging enteric pathogens

2009· review· en· W2062445136 on OpenAlexaff
Philip M. Sherman, Juan C. Ossa, Eytan Wine

Bibliographic record

VenueCurrent Opinion in Gastroenterology · 2009
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicSalmonella and Campylobacter epidemiology
Canadian institutionsUniversity of AlbertaUniversity of TorontoSickKids FoundationStollery Children's HospitalHospital for Sick Children
Fundersnot available
KeywordsCampylobacter jejuniMicrobiologyVirulenceBiologyCampylobacterEscherichia coliSalmonellaFlagellinVirologyBacteriaGene

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The aim of this review is to highlight recent advances in knowledge of bacterial enteric infections. We focus on understanding of enterohemorrhagic Escherichia coli O157:H7 and Campylobacter jejuni infections, and to link these acute events with long-term consequences in a susceptible host, including irritable bowel syndrome and chronic inflammatory bowel diseases. RECENT FINDINGS: Enterohemorrhagic E. coli and C. jejuni are zoonotic infections that are acquired from exposure to tainted food (undercooked hamburger and chicken, respectively) and contaminated drinking water. Noninvasive E. coli O157:H7 elaborates Shiga-like toxins and protein effectors that are injected, via a molecular syringe that is encoded by a bacterial type 3 secretion system, into infected eukaryotic cells. Less is known about the precise virulence properties of enteroinvasive Campylobacter strains, but both enteric pathogens are able to disrupt polarized epithelial monolayers resulting in increased uptake of macromolecules and antigens. SUMMARY: An improved understanding of the epidemiology, pathobiology and mechanisms underlying infectious enterocolitides will provide the basis for developing new intervention strategies including, for example, the use of probiotics, to interrupt the infectious process.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score0.825

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.087
GPT teacher head0.346
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations43
Published2009
Admission routes1
Has abstractyes

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