The potential of bacteriophage cocktail in eliminating Methicillin-resistant Staphylococcus aureus biofilms in terms of different extracellular matrices expressed by PIA, ciaA-D and FnBPA genes
Bibliographic record
Abstract
BACKGROUND: This study assessed novel approach of using highly lytic phages against methicillin-susceptible Staphylococcus aureus (MSSA) and methicillin-resistant Staphylococcus aureus (MRSA) biofilms with and without biofilm extracellular matrix- disrupting chemical. METHOD: The resultant phage-based control was assessed in relation to the type of biofilm extracellular matrix namely, polysaccharide intercellular adhesion (PIA) or proteinacious fibronectin-binding protein A (FnBPA). The biofilms were formed in vitro by 24 h incubation of bacteria in 96 wells microtiter plates at room temperature. The formed biofilms were assessed by tissue culture plate (TCP). Moreover, the nature of the biofilm was assessed by scanning electron microscopy (SEM) and PCR assay for detecting PIA genes, ciaA-D and FnBPA genes. RESULTS: this study showed that applied phages with 0.08 % benezenthonium chloride, for PIA biofilms, and 0.06 % ethanol, for proteinacious FnBPA biofilms, exerted 100 % eradication for MSSA biofilms and about 78 % of MRSA biofilms. The phage-based control of biofilms with chemical adjuvant showed significantly higher efficiency than that without adjuvant (P < 0.05). Moreover, FnBPA biofilms were more common in MRSA than in MSSA while PIA biofilms were more common in MSSA than in MRSA. And the most resistant type of biofilms to phage-based control was FnBPA in MRSA where 50 % of biofilms were reduced but not eradicated completely. CONCLUSIONS: It is concluded that PIA-disturbing agent and protein denaturing alcohol can increase the efficiency of attacking phages in accessing host cell walls and lysing them which in turn lead to much more efficient MRSA and MSSA biofilm treatment and prevention.
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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) | 0.001 | 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".