Bacteriophage treatment is effective against carbapenem-resistant <i>Klebsiella pneumoniae</i> (KPC) in a neutropenic murine model of gastrointestinal translocation and renal infection
Bibliographic record
Abstract
ABSTRACT Carbapenemase-producing Klebsiella pneumoniae (KPC) are globally emerging pathogens that cause life-threatening infections. Novel treatment alternatives are urgently needed. We therefore investigated the effectiveness of three novel bacteriophages (Spivey, Pharr, and Soft) in a neutropenic murine model of KPC gastrointestinal colonization, translocation, and disseminated infection. Bacteriophage efficacy was determined by residual bacterial burden of KPC (CFU/g) in kidneys. Parallel studies were conducted of bacteriophage pharmacokinetics and resistance. Treatment of mice with 5 × 10 9 PFU of phage cocktail via intraperitoneal injection was effective in significantly reducing renal KPC CFU by 100-fold ( P < 0.01) when administered every 24 h and 1000-fold ( P < 0.01) every 12 h. Moreover, a combination of bacteriophage and ceftazidime-avibactam produced a synergistic effect, resulting in a 10 5 -fold reduction in bacterial burden in cecum and kidney ( P < 0.001 in both tissues). Prophylactic administration of bacteriophages via oral gavage did not prevent KPC translocation to the kidneys. Bacteriophage decay determined by linear regression of the ln of mean concentrations demonstrated R 2 values in plasma of 0.941, kidney 0.976, and cecum 0.918, with half-lives of t 1/2 = 2.5 h. Furthermore, a phage-resistant mutant displayed increased sensitivity to serum killing in vitro , but did not show significant defects in renal infection in vivo . A combination of bacteriophages demonstrated significant efficacy alone and synergy with ceftazidime/avibactam in the treatment of experimental disseminated KPC infection in neutropenic mice.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".