Predisposition factors and control strategies of avian pathogenic Escherichia coli in laying hens
Bibliographic record
Abstract
Shift in laying hens housing from conventional cage-based systems to alternatives has impacted their health and performance. Microorganisms colonize young chick in the early stages of their physiological and immune development. These colonizing microbes originate from parent and the environment. Escherichia coli is among the normal gut colonizing bacteria however, some E. coli strains known as avian pathogenic E. coli (APEC), cause local or systemic infections (colibacillosis) responsible of significant economic losses to the poultry industry. Potential APEC strains and other poultry gut microbiota are influenced by several factors such as housing system, and the use of feed additives (prebiotics, probiotics, symbiotic, among others). This review will discuss the status of pullets and layers immunity, gut health, and predisposing factors of colibacillosis. Dietary interventions and some colibacillosis mitigation strategies in pullets and laying hens are reviewed and discussed. With the development of sequencing technologies and the use of feed additives as alternatives to antibiotics, future studies need to understand some of the complex associations between the feed additives, the rearing environment, and their selective pressure on gut microbiota, including E. coli, and their impacts on immune development in pullets and hens.
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) | 0.001 | 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) | 0.002 | 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".