Bibliographic record
Abstract
Antibiotic resistance continues to plague antimicrobial chemotherapy of infectious disease. And while true biocide resistance is as yet unrealized, in vitro and in vivo episodes of reduced biocide susceptibility are common and the history of antibiotic resistance should not be ignored in the development and use of biocidal agents. Efflux mechanisms of resistance, both drug specific and multidrug, are important determinants of intrinsic and/or acquired resistance to these antimicrobials, with some accommodating both antibiotics and biocides. This latter raises the spectre (as yet generally unrealized) of biocide selection of multiple antibiotic-resistant organisms. Multidrug efflux mechanisms are broadly conserved in bacteria, are almost invariably chromosome-encoded and their expression in many instances results from mutations in regulatory genes. In contrast, drug-specific efflux mechanisms are generally encoded by plasmids and/or other mobile genetic elements (transposons, integrons) that carry additional resistance genes, and so their ready acquisition is compounded by their association with multidrug resistance. While there is some support for the latter efflux systems arising from efflux determinants of self-protection in antibiotic-producing Streptomyces spp. and, thus, intended as drug exporters, increasingly, chromosomal multidrug efflux determinants, at least in Gram-negative bacteria, appear not to be intended as drug exporters but as exporters with, perhaps, a variety of other roles in bacterial cells. Still, given the clinical significance of multidrug (and drug-specific) exporters, efflux must be considered in formulating strategies/approaches to treating drug-resistant infections, both in the development of new agents, for example, less impacted by efflux and in targeting efflux directly with efflux inhibitors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".