Nanoparticle-Enabled Combination Therapy Showed Superior Activity against Multi-Drug Resistant Bacterial Pathogens in Comparison to Free Drugs
Bibliographic record
Abstract
The emergence of multidrug-resistant (MDR) bacterial pathogens in farm animals and their zoonotic spread is a concern to both animal agriculture and public health. Apart from antimicrobial resistance (AMR), bacterial pathogens from the genera of Salmonella and Staphylococcus take refuge inside host cells, thereby demanding intervention strategies that can eliminate intracellular MDR pathogens. In this study, seven clinical isolates of Salmonella and Staphylococcus from swine farms were characterized for antibiotic (n = 24) resistance, resistance mechanisms, and virulence characteristics. All isolates showed resistance to one or more antibiotics and S. enterica ser. Typhimurium isolate had the highest resistance to the panel of antibiotics tested. Major resistance mechanisms identified were efflux pump and beta-lactamase enzyme activities. Staphylococcus isolates showed complete hemolysis and strong biofilm formation, while Salmonella isolates caused partial hemolysis, but showed no or weak biofilm formation. MDR isolates of S. aureus M12 and S. enterica ser. Typhimurium bacteria were subsequently tested against combinations of antibiotics and potentiating adjuvants for improved antibacterial efficacy using a checkerboard assay, and their fractional inhibitory concentration index (FICI) was calculated. A combination of chitosan and silica nanoparticles containing tetracycline (TET) and efflux pump inhibitor chlorpromazine (CPZ), respectively, was characterized for physicochemical properties and effectiveness against MDR Salmonella enterica ser. Typhimurium isolate. This combination of nano-encapsulated drugs improved the antibacterial efficacy by inhibiting AMR mechanisms (efflux activity, beta-lactamase enzyme activity, and hydrogen sulfide (H2S) production) and reducing intracellular pathogen load by 83.02 ± 14.35%. In conclusion, this study sheds light on the promising applicability of nanoparticle-enabled combination therapy to combat multidrug-resistant pathogens encountered in animal agriculture.
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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) | 0.001 | 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".