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- Dahlberg, Tobias, 1990-
(författare)
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KNOW YOUR ENEMY : Characterizing Pathogenic Biomaterials Using Laser Tweezers
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Diseases caused by pathogenic agents such as bacteria and viruses result in devastating costs on personal and societal levels. However, it is not just the emergence of new diseases that is problematic. Antibiotic resistance among bacteria makes uncomplicated infections difficult and lethal. Resilient disease-causing spores spread in hospitals, the food industry, and water supplies requiring effective detection and disinfection methods. Further, we face complex neurological diseases where no effective treatment or diagnostic methods exist. Thus, we must increase our fundamental understanding of these diseases to develop effective diagnostic, detection, disinfection, and treatment methods.Classically, the methods used for detecting and studying the underlying mechanics of pathogenic agents work on a large scale, measuring the average macroscopic behavior and properties of these pathogens. However, just as with humans, the average behavior is not always representative of individual behavior. Therefore, it is also essential to investigate the characteristics of these pathogens on a single cell or particle level. This thesis develops and applies optical techniques to characterize pathogenic biomaterial on a single cell or particle level. At the heart of all these studies is our Optical Tweezers (OT) instrument. OT are a tool that allows us to reach into the microscopic world and interact with it. Finally, by combining OT with other experimental techniques, we can chemically characterize biomaterials and develop assays that mimic different biological settings. Using these tools, we investigate bacterial adhesion, disinfection, and detection of pathogenic spores and proteins.Hopefully, the insights of these studies can lessen the burden on society caused by diseases by helping others develop effective treatment, diagnostic, detection, and disinfection methods in the future.
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| 2. |
- Geli Rolfhamre, Patricia, 1979-
(författare)
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From Penicillin Binding Proteins to Community Interventions : Mathematical and Statistical Models Related to Antibiotic Resistance
- 2009
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Antibiotic resistance has become a major public health concern and mathematical models are important analytical tools for the understanding, evaluation and prediction of the resistance problem and related control strategies.The risk of emerging antibiotic resistance and selection has rarely been a concern in the design of antibiotic drug dosing regimens. In the first paper, a selection of antibiotic resistant subpopulations for different antibiotic dosing regimens was studied in vitro. The demonstrated complex relationship was influenced by both the rise of new mutants and a postantibiotic effect (PAE) (continued inhibition of bacterial growth after removal of the antibiotic drug). By constructing a mathematical model that incorporated biologically relevant parameters, we were able to assess the risks of resistance development under different dosing strategies.In the second paper, the model for PAEs is further developed to determine the implications for different dosing regimens. The result challenges the conventional notion that long PAEs promote extended drug dosing intervals and it allows new hypotheses to be tested experimentally based on the findings from the theoretical framework.Since PAE experiments often are time-consuming and laborious, very few studies have been reporting variation for this phenomenon. In the third paper, an extension to capture the stochastic behavior of bacterial population growth under drug exposure is made. The stochastic nature of the model is also an important complement to the existing deterministic models on drug dose drug effect relationships.The last paper describes a controlled clinical intervention study aiming at determining whether the frequency of trimethoprim resistance in E. coli can be decreased by a sudden and drastic reduction in trimethoprim use. In addition to evaluating the intervention effect, the model, given estimated parameters, is also used for predicting other interesting outcomes.
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