Early-stage Colonisation of Chicken Gut Microbiota: A Novel Approach to Poultry Health Management
Bibliographic record
Abstract
The poultry industry is an important part of the world economy. The demand for high-quality, affordable meat products is rising due to the increasing world population. Poultry meat is essential as it provides food security to developing countries and is an alternative to expensive beef and pork products. In Australia, the poultry industry is a major contributing factor to the economy. Several issues can affect the yield of chicken meat. These issues include disease outbreaks and the spread of antimicrobial resistance. Maintenance and improvement of chicken health are important, as this can help the poultry industry by improving the quality of products and thus boosting the economy. Gut health is one of the major factors influencing the overall health of the chicken. The poultry gut microbiota is important for nutrient digestion and absorption, development of a robust immune system, competitive exclusion of pathogens, and promoting overall gut health and preventing infections. A healthy gut microbiota can lead to better feed efficiency, higher meat yield and a better quality of life for the birds. Therefore, studying the chicken gut and the microorganisms that colonise it is important. The different microorganisms that colonise the gut can alter the conditions of the gut, influencing the health of the birds. This thesis deals with the concept of controlled colonisation of the chicken gut. Colonisation refers to the process by which the microorganisms establish themselves within the gastrointestinal tract, adhering to the intestinal lining and modulating the conditions to support various gut functions. Studies have shown that the gut is colonised soon after the birds hatch and are exposed to the environment. Once the gut is colonised, any products or feed additives supplied through the feed and water have minimal effect on the gut microbiota. Due to the total removal of maternal microbiota transfer options in modern poultry production, there was an urgent and unmet need to assist in industrial poultry gut colonisation. While faecal transplants have shown great success in human health and can restore significantly damaged intestinal health, attempts to utilise this approach in poultry via spraying caecal content had experimental promise but were impractical to apply in hatcheries. The first product created to address this issue and ensure that hatchlings are colonised with chicken microbiota rather than random environmental microbes incapable of providing functions needed for health and performance was Aviguard. This is a specialised poultry product designed to establish and maintain a healthy gut microbiota in birds. It consists of a freeze-dried preparation of live, beneficial intestinal bacteria sourced from healthy adult chickens. The product contains over 200 different bacterial species that are integral to a balanced avian gut community. The aim of this project and the animal trials was to test the product Aviguard (Lallemand Animal Nutrition, Canada) for its ability to colonise the chicken gut when administered immediately after hatching and its impact on growth, feed efficiency and intestinal microbiota development. Chapter 1 gives a general introduction to the poultry industry and includes a literature review. This detailed review discusses the problems that are plaguing the poultry industry. Utilizing the information from the review, Chapter 2 describes the in-house animal trial and the effect that Aviguard had on the cloacal microbiota and on the performance parameters. Chapter 3 is a detailed study of the effect of Aviguard on different gut sections, including the crop, gizzard, duodenum, jejunum and cecum. This chapter also deals with the changes in litter quality due to the administration of Aviguard. Chapter 4 is a study about the effect of Aviguard on the gene expression profiles, metabolic pathways, histomorphological changes, and the production of short-chain fatty acids. The next chapter, Chapter 5, presents a broiler trial conducted at a commercial farm to determine the effects of at-hatch administration of Aviguard on chicken gut colonisation in an industrial hatchery setting. The final chapter, Chapter 6, provides conclusions and suggests further studies to build upon the current research.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".