Novel and disruptive biological strategies for resolving gut health challenges in monogastric food animal production
Bibliographic record
Abstract
Use of feed antibiotics as growth promoters for control of pathogens associated with monogastric food animal morbidity and mortality has contributed to the development of antimicrobial resistance, which has now become a threat to public health on a global scale. Presently, a number of alternative feed additives have been developed and are divided into two major categories, including 1) the ones that are supposed to directly and indirectly control pathogenic bacterial proliferation; and 2) the other ones that are intended to up-regulate host gut mucosal trophic growth, whole body growth performance and active immunity. A thorough review of literature reports reveal that efficacy responses of current alternative feed additives in replacing feed antibiotics to improve performances and gut health are generally inconsistent dependent upon experimental conditions. Current alternative feed additives typically have no direct detoxification effects on endotoxin lipopolysaccharides (LPS) and this is likely the major reason that their effects are limited. It is now understood that pathogenic bacteria mediate their negative effects largely through LPS interactions with toll-like receptor 4, causing immune responses and infectious diseases. Therefore, disruptive biological strategies and a novel and new generation of feed additives need to be developed to replace feed antibiotic growth promoters and to directly and effectively detoxify the endotoxin LPS and improve gut health and performance in monogastric food animals.
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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".