Cystic fibrosis microbiology: Advances in antimicrobial therapy
Bibliographic record
Abstract
Much of the improvement in the survival of individuals with cystic fibrosis (CF) is due to advancements in antimicrobial treatments. New aerosolized antibiotic formulations have recently been introduced (such as inhaled aztreonam), and others are in development (inhaled levofloxacin and liposomal amikacin). Licensed dry powder formulations include tobramycin inhalation powder and dry powder colistimethate (available in Europe). Although inhaled antibiotics have the advantage of being able to deliver high intrapulmonary concentrations of drug, antimicrobial resistance can still develop and is a concern in CF. Antimicrobial resistance might be mitigated by using non-antibiotic treatments, antibiotic adjuvants, which have activity against bacteria. Examples include agents such as gallium, antimicrobial peptides and anti-biofilm compounds such as alginate oligosaccharides (OligoG) and garlic. Vaccination strategies and antibody therapy (IgY) against Pseudomonas aeruginosa have also been attempted to prevent initial infection with this organism in CF. Although aggressive and long-term use of antibiotics has been crucial in slowing lung function decline and improving survival in people with CF, it has added a significant burden of care and associated toxicities in these individuals. Careful surveillance and the use of preventative strategies for antibiotic related toxicity (such as nephrotoxicity and ototoxicity) are essential. Continued development of effective antimicrobial agents that can function in the conditions encountered in the CF lung, such as against bacterial biofilm growth and under anaerobic conditions, is needed.
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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".