Innovative drugs, chemicals, and enzymes within the animal production chain
Bibliographic record
Abstract
The alarming number of recently reported human illnesses with bacterial infections resistant to multiple antibacterial agents has become a serious concern in recent years. This phenomenon is a core challenge for both the medical and animal health communities, since the use of antibiotics has formed the cornerstone of modern medicine for treating bacterial infections. The empirical benefits of using antibiotics to address animal health issues in animal agriculture (using therapeutic doses) and increasing the overall productivity of animals (using sub-therapeutic doses) are well established. The use of antibiotics to enhance profitability margins in the animal production industry is still practiced worldwide. Although many technical and economic reasons gave rise to these practices, the continued emergence of antimicrobial resistant bacteria is furthering the need to reduce the use of medically important antibiotics. This will require improving on-farm management and biosecurity practices, and the development of effective antibiotic alternatives that will reduce the dependence on antibiotics within the animal industry in the foreseeable future. A number of approaches are being closely scrutinized and optimized to achieve this goal, including the development of promising antibiotic alternatives to control bacterial virulence through quorum-sensing disruption, the use of synthetic polymers and nanoparticles, the exploitation of recombinant enzymes/proteins (such as glucose oxidases, alkaline phosphatases and proteases), and the use of phytochemicals. This review explores the most recent approaches within this context and provides a summary of practical mitigation strategies for the extensive use of antibiotics within the animal production chain in addition to several future challenges that need to be addressed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".