Advances in Phage Therapy: Targeting the Burkholderia cepacia Complex
Bibliographic record
Abstract
The increasing prevalence and worldwide distribution of multidrug-resistant bacterial pathogens is an imminent danger to public health and threatens virtually all aspects of modern medicine. Particularly concerning, yet insufficiently addressed, are the members of the Burkholderia cepacia complex (Bcc), a group of at least twenty opportunistic, hospital-transmitted, and notoriously drug-resistant species, which infect and cause morbidity in patients who are immunocompromised and those afflicted with chronic illnesses, including cystic fibrosis (CF) and chronic granulomatous disease (CGD). One potential solution to the antimicrobial resistance crisis is phage therapy—the use of phages for the treatment of bacterial infections. Although phage therapy has a long and somewhat checkered history, an impressive volume of modern research has been amassed in the past decades to show that when applied through specific, scientifically supported treatment strategies, phage therapy is highly efficacious and is a promising avenue against drug-resistant and difficult-to-treat pathogens, such as the Bcc. In this review, we discuss the clinical significance of the Bcc, the advantages of phage therapy, and the theoretical and clinical advancements made in phage therapy in general over the past decades, and apply these concepts specifically to the nascent, but growing and rapidly developing, field of Bcc phage therapy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".