0495 Genomic approaches to characterizing and reducing antimicrobial resistance in beef cattle production systems
Bibliographic record
Abstract
The current rate of resistance development against antimicrobials (AMR) available for use in human medicine is a global health threat. The discovery and design of new antibiotics is nearly at a standstill and as a result, the approximately 700,000 global deaths attributed to AMR infections yearly is expected to rise. Intensive livestock operations, including beef production systems require the use of antimicrobials to ensure animal health and to optimize growth efficiency. As a result the microbes present in the feedlot environment can be exposed to multiple classes of antimicrobials and have the potential to harbor, acquire or develop resistance. To understand the magnitude and risk of AMR in this setting, it is important to assess the prevalence and diversity of AMR determinants in the microbial population. AMR bacteria are traditionally identified by isolation and growth in the presence of selective antibiotics. However, more recent advances in genomics have enhanced the precision of AMR characterization. Whole-genome, metagenomic, and RNA sequencing provide new avenues for the rapid detection of AMR determinants in microbial communities, including unculturable organisms. Metagenomic approaches can be used to identify both previously characterized and novel AMR mechanisms in recalcitrant bacteria which may serve as an environmental reservoir of resistance genes. Metagenomic approaches can also be used to place AMR genes within the context of mobile genetic elements, providing information with regard to the likelihood of their dissemination among microbial communities. Genomics may also play a key role in mitigating and developing alternatives to antimicrobials such as probiotics. RNA-seq-based transcriptomics and Tn-seq may also provide new ways to examine the cellular mechanisms that may promote AMR or prevent it. Finally, CRISPR-Cas gene-editing shows promise as a tool to directly reduce AMR by killing AMR-resistant organisms without harming beneficial microbes. All these technological developments provide new opportunities to better identify, quantify, and mitigate resistance to antimicrobials as well as develop alternatives.
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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 | 0.002 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".