Carbapenem-resistant <i>Klebsiella pneumoniae</i> : the role of plasmids in emergence, dissemination, and evolution of a major clinical challenge
Bibliographic record
Abstract
Introduction Klebsiella pneumoniae is a major agent of healthcare-associated infections and a cause of some community-acquired infections, including severe bacteremic infections associated with metastatic abscesses in liver and other organs. Clinical relevance is compounded by its outstanding propensity to evolve antibiotic resistance. In particular, the emergence and dissemination of carbapenem resistance in K. pneumoniae has posed a major challenge due to the few residual treatment options, which have only recently been expanded by some new agents. The epidemiological success of carbapenem-resistant K. pneumoniae (CR-Kp) is mainly linked with clonal lineages that produce carbapenem-hydrolyzing enzymes (carbapenemases) encoded by plasmids.Areas covered Here, we provide an updated overview on the mechanisms underlying the emergence and dissemination of CR-Kp, focusing on the role that plasmids have played in this phenomenon and in the co-evolution of resistance and virulence in K. pneumoniae.Expert opinion CR-Kp have disseminated on a global scale, representing one of the most important contemporary public health issues. These strains are almost invariably associated with complex multi-drug resistance (MDR) phenotypes, which can also include recently approved antibiotics. The heterogeneity of the molecular bases responsible for these phenotypes poses significant hurdles for therapeutic and diagnostic purposes.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".