Origins, Acquisition and Dissemination of Antibiotic Resistance Determinants
Bibliographic record
Abstract
Since the introduction of antibiotics in the late 1940s there has been an inexorable propagation of antibiotic resistance genes in bacterial pathogens (and their relatives). This survival phenomenon was first characterized as the appearance of point mutations that altered drug targets, but in the mid-1950s transmissible antibiotic resistance genes were reported in Japan. Since this time both resistance strategies have been used, often in concert. For some types of antibiotic, only resistance by mutation has been identified, for others only resistance by plasmid acquisition. There is conflicting evidence with respect to the presence of antibiotic resistance in bacterial pathogens in the 'pre-antibiotic' era; however, it is likely that the evolution of antibiotic resistance occurred over short periods. Thus, antibiotic resistance gene must be common in the environment, but their derivation remains to be established conclusively. This paper examines the proposals that antibiotic resistance genes originated in the bacterial population, either as bona fide resistance genes or genes encoding metabolic functions. In addition, the acquisition of heterologous resistance determinants by different genetic elements, their intergeneric exchange mechanisms, and the possible roles of antibiotics in the processes are discussed. Are there prospects for drug intervention that eliminate or retard these natural evolutionary processes?
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".