Colistin heteroresistance, mechanisms, diagnostic methods, and therapeutic options: A review
Bibliographic record
Abstract
The heteroresistance phenotype refers to the presence of bacterial subpopulations with reduced antibiotic susceptibility compared with the main population.Mathematical modelling and computer simulations suggest that heteroresistance can lead to negative treatment outcomes and finally, treatment failure.Due to the low frequency and resistance level of resistant subpopulations, detection of heteroresistance phenotype in the diagnostic laboratory is problematic.Routine laboratory tests do not have the ability to accurately detect heteroresistance, but on the other hand, specific methods are time consuming and expensive.The emergence of colistin heteroresistance is a public health concern that threatens human health.Colistin heteroresistance to date has been reported in eight pathogens including Acinetobacter spp., Klebsiella spp., Enterobacter spp., Pseudomonas spp., Escherichia coli, Salmonella enterica serovar Typhimurium (referred to as Salmonella Typhimurium), Neisseria meningitidis and Stenotrophomonas maltophilia.The growing emergence of colistin heteroresistance worldwide underscores the crucial need for coordinated global action to combat it.Understanding the mechanisms of colistin heteroresistance can help to provide better guidelines for reducing antibiotic resistance and to achieve new therapeutic approaches.Our review showed that the prevalence of colistin heteroresistance strains varies in different countries.It seems that the use of different treatment strategies, especially combination therapy, can be effective in reducing the incidence of resistant subpopulations.Also, the use of new generation diagnostic methods can have a significant impact on treatment.Our findings in this review are needed to raise the awareness of microbiologists and specialists to the colistin heteroresistance mechanisms and to achieve effective 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".