Drug-resistant <i>Acinetobacter baumannii</i> : mortality, emerging treatments, and future pharmacological targets for a WHO priority pathogen
Bibliographic record
Abstract
SUMMARY Acinetobacter baumannii has emerged as a formidable global health concern and is a major contributor to infection-related mortality in critically ill patients worldwide. This versatile Gram-negative bacterium is notorious for its highly plastic genome, which enables the rapid emergence and dissemination of multidrug-resistant (MDR) and extensively drug-resistant (XDR) strains, severely limiting the available treatment options. The genetic flexibility of A. baumannii underpins its arsenal of molecular mechanisms, enabling it to resist a range of antibiotics, from traditional agents to the latest therapeutic advancements available. With the progress made in treatments against Acinetobacter infections and various drugs undergoing clinical trials, the effectiveness of these treatments is often outpaced by the pathogen’s swift evolution of resistance, resulting in alarmingly high rates of treatment failure. In this systematic review of literature spanning 2004–2024, we highlight the high mortality rates associated with infections caused by XDR strains and carbapenem-resistant A. baumannii (CRAB). This review provides a comprehensive examination of the resistance mechanisms deployed by A. baumannii , encompassing both conventional antibiotics and novel agents used in global healthcare settings. In addition, we discuss emerging druggable targets and the inherent challenges in their development, offering strategic insights into next-generation therapeutic programs. A deep profound understanding of the pathogen’s molecular defenses is essential to guide the design of innovative therapies aimed at mitigating the escalating threat posed by A. baumannii .
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".