Bibliographic record
Abstract
Resistance to antibiotics can be achieved through a number of different strategies.For the ribosome targeting aminoglycoside antibiotics, such as kanamycin and amikacin, the clinically most frequently observed mechanism of resistance is enzymatic modification of the drug.More than a hundred different enzymes have been identified in pathogenic bacteria that confer resistance to aminoglycosides.The enzymes can be classified into three main groups: those that phosphorylate, those that acetylate and those that adenylate the drugs.As a single enzyme can often provide resistance to multiple aminoglycosides, and it is not uncommon that a single pathogen harbours several different aminoglycoside modifying enzymes, resistance to these antibiotics is rampant.While the situation is dire, it must be realized that some enzymes are far more frequently encountered in hospital settings than others, and thus clinically relevant resistance to specific aminoglycoside antibiotics is often only mediated by three to five unique enzymes.We have performed crystallographic studies of aminoglycoside modifying enzymes that confer clinically observed resistance to two of the most widely used aminoglycoside antibiotics: tobramycin and gentamicin.Structural analyses of the tobramycin and gentamicin clinical resistomes reveals surprising diversity in observed drug-target interactions.Combining these insights with information on drug-target nteractions with the intended target, the bacterial ribosome, can furthermore inform the development of nextgeneration aminoglycoside antibiotics.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".