Genetic Regulation of Virulence and Antibiotic Resistance in Acinetobacter baumannii
Bibliographic record
Abstract
Multidrug resistant microorganisms are forecast to become the single biggest challenge to medical care in the 21st century. Over the last decades, members of the genus Acinetobacter have emerged as bacterial opportunistic pathogens, in particular as challenging nosocomial pathogens because of the rapid evolution of antimicrobial resistances. Although we lack fundamental biological insight into virulence mechanisms, an increasing number of researchers are working to identify virulence factors and to study antibiotic resistance. Here, we review current knowledge regarding the regulation of virulence genes and antibiotic resistance in Acinetobacter baumannii. A survey of the two-component systems AdeRS, BaeSR, GacSA and PmrAB explains how each contributes to antibiotic resistance and virulence gene expression, while BfmRS regulates cell envelope structures important for pathogen persistence. A. baumannii uses the transcription factors Fur and Zur to sense iron or zinc depletion and upregulate genes for metal scavenging as a critical survival tool in an animal host. Quorum sensing, nucleoid-associated proteins, and non-classical transcription factors such as AtfA and small regulatory RNAs are discussed in the context of virulence and antibiotic resistance.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".