Recent Advances in Genetic Tools for Acinetobacter baumannii
Bibliographic record
Abstract
Acinetobacter baumannii is classified as a top priority pathogen by the World Health Organization (WHO) because of its widespread resistance to all classes of antibiotics. This makes the need for understanding the mechanisms of resistance and virulence critical. Therefore, tools that allow genetic manipulations are vital to unravel the mechanisms of multidrug resistance (MDR) and virulence in A. baumannii. A host of current strategies are available for genetic manipulations of A. baumannii laboratory-strains, including ATCC® 17978TM and ATCC® 19606T, but depending on susceptibility profiles, these strategies may not be sufficient when targeting strains newly obtained from clinic, primarily due to the latter’s high resistance to antibiotics that are commonly used for selection during genetic manipulations. This review highlights the most recent methods for genetic manipulation of A. baumannii including CRISPR based approaches, transposon mutagenesis, homologous recombination strategies, reporter systems and complementation techniques with the spotlight on those that can be applied to MDR clinical isolates.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".