Bacteriophage-Liposomes Complex, a Bi-therapy System to Target Streptococcus pneumonia and Biofilm: A Research Protocol
Bibliographic record
Abstract
Introduction: Streptococcus pneumoniae is a gram-positive bacterium, which is the leading cause of death for young children, elderly population, and immunocompromised patients. Its ability to mutate and become resistant to some of the strongest antibiotics makes them difficult to treat and increases the risk of disease spread. Although the development of stronger antibiotics to treat such microbes may be an option, they potentially pose a dangerous threat to the body. As such, a viable treatment option to fight against antimicrobial resistance has yet been found. Methods: The study focuses on utilizing a bi-therapy system to target S. pneumoniae in biofilm, which is the site of emerging antibiotic resistant mutants, by creating levofloxacin-liposomes carrying phages and testing them both in vitro and in vivo. Anticipated results: Using bacteriophage therapy and applying bacteriophage-antibiotic synergy, it is hoped to augment the potency of the treatment while lowering its side-effects. The Cp-1 bacteriophage-liposomes complexes are expected to be specific to the S. pneumoniae to carry antibiotics to sites of infection. Discussion: The therapy could ensure targeted bacterial lysis and site-directed delivery of low-dose drugs to decrease the toxicity effect of the antibiotics. Once the efficacy is established and is proven to be significant, its potency can be tested in BALB/cByJ mice models before bringing this therapy to animal trials then human clinical trials. Conclusion: Bacteriophages are very attractive therapeutic agents that effectively target pathogenic bacteria, safe for the human body, and highly modifiable to combat newly emerging bacterial threats. In addition to its many benefits, the use of bacteriophages could significantly reduce healthcare costs. The potential use of bacteriophages-liposomes complexes could be translated to treat respiratory infections in humans after confirming its efficacy in vitro and in vivo studies.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".