Pig vaccination strategies based on enterotoxigenic Escherichia coli toxins
Bibliographic record
Abstract
Enterotoxigenic Escherichia coli (ETEC) are responsible for diarrhea in humans as well as in farm animals. ETEC infections in newborn, suckling, and especially in post-weaning piglets are associated with reduced growth rate, morbidity, and mortality. ETEC express virulence factors as adhesin and enterotoxins that play a central role in the pathogenic process. Adhesins associated with pigs are of diverse type being either fimbrial or non-fimbrial. Enterotoxins belong to two groups: heat-labile (LT) and heat-stable (ST). Heterogeneity of ETEC strains encompass expression of various fimbriae (F4, F5, F6, F18, and F41) and enterotoxins (LT, STa, STb, and EAST1). In the late years, attempts to immunize animals against neonatal and post-weaning diarrhea were focused on the development of anti-adhesin strategies as this is the initial step of ETEC pathogenesis. Although those vaccines demonstrated some protection against ETEC infections, as enterotoxins are pivotal to the virulence of ETEC, a new generation of vaccinal molecules, which include adhesin and one or more enterotoxins, were recently tested. Some of these newly developed chimeric fusion proteins are intended to control as well human diarrhea as enterotoxins are more or less common with the ones found in pigs. As these could not be tested in the natural host (human), either a mouse or pig model was substituted to evaluate the protection efficacy. For the advancement of pig vaccine, mice were sometimes used for preliminary testing. This review summarizes advances in the anti-enterotoxin immunization strategies considered in the last 10 years.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".