Determining the Rate of Development of Antibiotic Resistance to Streptomycin and Doxycycline in Escherichia coli
Bibliographic record
Abstract
Antibiotic resistance is a pressing issue in the medical field today. It is important to understand the development of bacterial resistance to implement effective preventative measures against antibiotic resistant bacteria. This study investigated the rate at which Escherichia coli (E. coli), a common pathogen, developed resistance to streptomycin and doxycycline, as Oz et al. (2014) showed differing levels of resistance in E. coli to these two antibiotics. The development of antibiotic resistance was measured by adding E. coli to 96-well plates in the presence of increasing doses of doxycycline, streptomycin, or a combination treatment. Successive generations were added to the same treatments to see whether they would grow at higher concentrations of antibiotic. The change in minimum inhibitory concentration for streptomycin and doxycycline was determined as the bacteria became increasingly resistant to each antibiotic. The fastest rate of antibiotic resistance was observed for streptomycin, with doxycycline resistance exhibiting a slower rate of development. The rate of resistance development for the combination treatment was the slowest, potentially due to small differences in target domains. Some cross-resistance was also observed. This study provides a small-scale methodological basis and preliminary insight on antibiotic resistance trends for two antibiotic classes and a combination treatment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".