A High-Throughput and Genomics-Based Approach to Combat Antimicrobial Resistance
Bibliographic record
Abstract
Antimicrobial resistance (AMR) is becoming an increasingly large threat to global health and economics. In 2019, there were approximately 1.27 million deaths directly attributable to bacterial AMR and 4.95 million deaths associated with bacterial AMR. These numbers are expected to increase to 10 million by the year 2050. The use of Adjuvant therapeutics has been proposed as a strategy to mitigate antimicrobial resistance. Adjuvants can help resensitize resistant bacteria to clinically-relevant antibiotics, while also prolonging resistance from occurring. \n \nHere I present two high-throughput screens: one that identifies robust adjuvant compounds that target resistant bacteria, and one that repurposes drug-like compounds for antimicrobial use against Gram-negative bacteria. From these screens, one lead adjuvant candidate and four repurposed drug-like antimicrobials were taken forward for a mix of analog generation studies, mechanistic studies, resistance evolution studies and genomic analysis. \n \nThis work will help play a role in bringing novel therapies to the clinic and prolong the evolution of resistance from occurring.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".