Post weaning diarrhea in pigs: risk factors and non-colistin-based control strategies
Bibliographic record
Abstract
Post-weaning diarrhea (PWD) is one of the most serious threats for the swine industry worldwide. It is commonly associated with the proliferation of enterotoxigenic Escherichia coli in the pig intestine. Colistin, a cationic antibiotic, is widely used in swine for the oral treatment of intestinal infections caused by E. coli, and particularly of PWD. However, despite the effectiveness of this antibiotic in the treatment of PWD, several studies have reported high rates of colistin resistant E. coli in swine. Furthermore, this antibiotic is considered of very high importance in humans, being used for the treatment of infections due to multidrug-resistant (MDR) Gram-negative bacteria (GNB). Moreover, the recent discovery of the mcr-1 gene encoding for colistin resistance in Enterobacteriaceae on a conjugative stable plasmid has raised great concern about the possible loss of colistin effectiveness for the treatment of MDR-GNB in humans. Consequently, it has been proposed that the use of colistin in animal production should be considered as a last resort treatment only. Thus, to overcome the economic losses, which would result from the restriction of use of colistin, especially for prophylactic purposes in PWD control, we believe that an understanding of the factors contributing to the development of this disease and the putting in place of practical alternative strategies for the control of PWD in swine is crucial. Such alternatives should improve animal gut health and reduce economic losses in pigs without promoting bacterial resistance. The present review begins with an overview of risk factors of PWD and an update of colistin use in PWD control worldwide in terms of quantities and microbiological outcomes. Subsequently, alternative strategies to the use of colistin for the control of this disease are described and discussed. Finally, a practical approach for the control of PWD in its various phases is proposed.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".