Animal Enterotoxigenic<i>Escherichia coli</i>
Bibliographic record
Abstract
Enterotoxigenic Escherichia coli (ETEC) is the most common cause of E. coli diarrhea in farm animals. ETEC are characterized by the ability to produce two types of virulence factors: adhesins that promote binding to specific enterocyte receptors for intestinal colonization and enterotoxins responsible for fluid secretion. The best-characterized adhesins are expressed in the context of fimbriae, such as the F4 (also designated K88), F5 (K99), F6 (987P), F17, and F18 fimbriae. Once established in the animal small intestine, ETEC produce enterotoxin(s) that lead to diarrhea. The enterotoxins belong to two major classes: heat-labile toxins that consist of one active and five binding subunits (LT), and heat-stable toxins that are small polypeptides (STa, STb, and EAST1). This review describes the disease and pathogenesis of animal ETEC, the corresponding virulence genes and protein products of these bacteria, their regulation and targets in animal hosts, as well as mechanisms of action. Furthermore, vaccines, inhibitors, probiotics, and the identification of potential new targets by genomics are presented in the context of animal ETEC.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.003 | 0.003 |
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".