Therapeutic Bacteriophages for Gram-Negative Bacterial Infections in Animals and Humans
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Drug-resistant Gram-negative bacterial pathogens are an increasingly serious health threat causing worldwide nosocomial infections with high morbidity and mortality. Of these, the most prevalent and severe are Pseudomonas aeruginosa, Klebsiella pneumoniae, Escherichia coli, Acinetobacter baumannii, and Salmonella typhimurium. The extended use of antibiotics has led to the emergence of multidrug resistance in these bacteria. Drug-inactivating enzymes produced by these bacteria, as well as other resistance mechanisms, render drugs ineffective and make treatment of such infections more difficult and complicated. This makes the development of novel antimicrobial agents an urgent necessity. Bacteriophages, which are bacteria-killing viruses first discovered in 1915, have been used as therapeutic antimicrobials in the past, but their use was abandoned due to the widespread availability of antibiotics in the 20th century. The emergence, however, of drug-resistant pathogens has re-affirmed the need for bacteriophages as therapeutic strategies. This review describes the use of bacteriophages as novel agents to combat this rapidly emerging public health crisis by comprehensively enumerating and discussing the innovative use of bacteriophages in both animal models and in patients infected by Gram-negative bacteria.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it