Custom-designed Nano-enabled Antibacterial Combination Therapy to combat Persistent Bacterial Pathogens associated with Bovine Mastitis
Bibliographic record
Abstract
Bovine intramammary infection (IMI) or bovine mastitis (BM) is a significant threat to the dairy industry worldwide. The prevalence of disease and rise of antimicrobial resistance (AMR) because of antibiotic use warrant alternate treatment and control strategies. Such an effort demands a deeper understanding of the pathogen and host-related factors involved in IMI. This thesis begins with an overview of risk factors and pathogenesis associated with BM that challenge the success of current treatment strategies and how these challenges could be overcome through nano-enabled antibacterial combination therapy (NeACT). Knowledge of the causative agents of mastitis and continuous monitoring of mastitis pathogens for AMR and virulence is vital for strategizing such treatment approaches. Accordingly, Chapters 2 and 3 of the thesis address the prevalence of AMR, AMR mechanisms, and virulence in a library of Escherichia coli and Staphylococcus aureus isolates from mastitis dairy cows (from farms across Canada) through phenotypic and genotypic studies. AMR and virulence characteristics were prevalent in both pathogens. Additionally, S. aureus isolates showed invasion of epithelial cells. The results also suggested the inadequacy of antimicrobials with a single mode of action to curtail intracellular AMR bacteria with multiple mechanisms of resistance and virulence factors. While the antimicrobial-adjuvant combination with complementary modes of action could have potential applications in BM treatment, poor stability and bioavailability, cytotoxicity, etc, pose significant challenges. Nanotechnology could resolve these issues due to their exceptional ability as cargo carriers. Therefore, Chapter 4 involves screening antimicrobial-adjuvant combinations for antibacterial synergism and custom-developing a NeACT for BM treatment. NeACT constituted conjugates of chitosan-cyclodextrin nanomaterials loaded with antibiotic (ceftiofur) and adjuvant (tannic acid and chlorpromazine) combinations. In vitro and in vivo (assessed in CD-1 mastitis mice model) investigations suggested excellent antibacterial efficiency of NeACT against intracellular AMR pathogens owing to anti-efflux pump, anti-beta-lactamase, and anti-biofilm activities. It showed enhanced physicochemical properties, slow cargo-release behavior, and no toxic responses. Overall, the thesis reported the prevalence of AMR and virulence among mastitis pathogens and the inefficiency of antimicrobials with a specific mode of action to curtail them. Nano-enabled combination therapy, or NeACT, was shown to have immense potential as an alternate treatment
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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) | 0.002 | 0.001 |
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".