Current and novel therapies for management of <i>Acinetobacter baumannii</i> -associated pneumonia
Bibliographic record
Abstract
Acinetobacter baumannii is a common pathogen associated with hospital-acquired pneumonia showing increased resistance to carbapenem and colistin antibiotics nowadays. Infections with A. baumannii cause high patient fatalities due to their capability to evade current antimicrobial therapies, emphasizing the urgency of developing viable therapeutics to treat A. baumannii-associated pneumonia. In this review, we explore current and novel therapeutic options for overcoming therapeutic failure when dealing with A. baumannii-associated pneumonia. Among them, antibiotic combination therapy administering several drugs simultaneously or alternately, is one promising approach for optimizing therapeutic success. However, it has been associated with inconsistent and inconclusive therapeutic outcomes across different studies. Therefore, it is critical to undertake additional clinical trials to ascertain the clinical effectiveness of different antibiotic combinations. We also discuss the prospective roles of novel antimicrobial therapies including antimicrobial peptides, bacteriophage-based therapy, repurposed drugs, naturally-occurring compounds, nanoparticle-based therapy, anti-virulence strategies, immunotherapy, photodynamic and sonodynamic therapy, for utilizing them as additional alternative therapy while tackling A. baumannii-associated pneumonia. Importantly, these innovative therapies further require pharmacokinetic and pharmacodynamic evaluation for safety, stability, immunogenicity, toxicity, and tolerability before they can be clinically approved as an alternative rescue therapy for A. baumannii-associated pulmonary infections.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".