Discovering the Potentials of Four Phage Endolysins to Combat Gram-Negative Infections
Bibliographic record
Abstract
Endolysin-based therapeutics are promising antibacterial agents and can successfully supplement the existing antibacterial drugs array. It is specifically important in the case of Gram-negative pathogens, e.g., ESKAPE group bacteria, which includes Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species, and are highly inclined to gain multiple antibiotic resistance. Despite numerous works devoted to the screening of new lytic enzymes and investigations of their biochemical properties, there are significant breaches in some aspects of their operating characteristics, including safety issues of endolysin use. Here, we provide a comprehensive study of the antimicrobial efficacy aspects of four Gram-negative bacteria-targeting endolysins LysAm24, LysAp22, LysECD7, and LysSi3, their in vitro and in vivo activity, and their biological safety. These endolysins possess a wide spectrum of action, are active against planktonic bacteria and bacterial biofilms, and are effective in wound and burn skin infection animal models. In terms of safety, these enzymes do not contribute to the development of short-term resistance, are not cytotoxic, and do not significantly affect the normal intestinal microflora in vivo. Our results provide a confident base for the development of effective and safe candidate dosage forms for the treatment of local and systemic infections caused by Gram-negative bacterial species.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".