In vivo microscopy for the rapid, needle-free diagnosis of malaria and other blood-borne illnesses
| dc.contributor.advisor | Richards-Kortum, Rebecca | en_US |
| dc.creator | Burnett, Jennifer | en_US |
| dc.date.accessioned | 2017-08-01T15:28:07Z | en_US |
| dc.date.available | 2017-12-01T06:01:04Z | en_US |
| dc.date.created | 2016-12 | en_US |
| dc.date.issued | 2016-10-26 | en_US |
| dc.date.submitted | December 2016 | en_US |
| dc.date.updated | 2017-08-01T15:28:07Z | en_US |
| dc.description.abstract | Malaria is a serious parasitic infection causing intermittent fever and chills. In resource-limited regions, young children infected with malaria have high mortality; therefore, children with flu-like symptoms are often presumptively treated with anti-malarial drugs. This practice can prematurely deplete drug resources and ultimately increase the potential for drug resistance. Current malaria diagnostic tests rely on fingerprick blood samples, requiring consumables that increase per-test costs. There is a clear need for a rapid malaria diagnostic test amenable to the point-of-care. This thesis describes a needle-free method to diagnose malaria. A portable microscope system was designed and constructed to image blood cells circulating through the microvasculature in vivo. Malaria-infected cells were detected using the endogenous malaria biomarker hemozoin. This approach was assessed in increasingly complex biological environments, demonstrating the ability to detect P. yoelii infection in vivo in a mouse model of malaria over a clinically relevant range of parasitemia. An automated image processing algorithm was developed to rapidly identify and quantify circulating hemozoin particles circulating in vivo. The diagnostic performance of the automated algorithm was comparable to manual detection, with accuracy of 89% using blood smear microscopy as the gold standard. Next, an algorithm was developed to classify infected-red blood cells from hemozoin-containing white blood cells which persist after infection has resolved. Discrimination using hemozoin signal features measured in vitro yielded an algorithm with an area under the receiver operating characteristic curve of 0.92 and 0.93 for P. yoelii and P. falciparum respectively. This algorithm successfully discriminated between active and recent malaria infections in vivo in an animal model, furthering the diagnostic accuracy of this approach. Additionally, in vivo microscopy was evaluated with optical tissue phantoms for the ability to detect other blood-related diseases, specifically microfilarial infections and anemia. Microfilariae were detected over a clinically relevant dynamic range with a positive linear correlation with blood smear microscopy.Hemoglobin absorbance measured by in vivo microscopy yielded hemoglobin concentrations within ±1.5 g/dL, using a point-of-care device as the gold standard. Collectively these results demonstrate the potential for a robust diagnostic platform for blood-borne parasitic infections and anemia amenable to the point-of-care. | en_US |
| dc.embargo.terms | 2017-12-01 | en_US |
| dc.format.mimetype | application/pdf | en_US |
| dc.identifier.citation | Burnett, Jennifer. "In vivo microscopy for the rapid, needle-free diagnosis of malaria and other blood-borne illnesses." (2016) Diss., Rice University. <a href="https://hdl.handle.net/1911/95970">https://hdl.handle.net/1911/95970</a>. | en_US |
| dc.identifier.uri | https://hdl.handle.net/1911/95970 | en_US |
| dc.language.iso | eng | en_US |
| dc.rights | Copyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder. | en_US |
| dc.subject | malaria | en_US |
| dc.subject | hemozoin | en_US |
| dc.subject | in vivo microscopy | en_US |
| dc.subject | point-of-care diagnostics | en_US |
| dc.title | In vivo microscopy for the rapid, needle-free diagnosis of malaria and other blood-borne illnesses | en_US |
| dc.type | Thesis | en_US |
| dc.type.material | Text | en_US |
| thesis.degree.department | Bioengineering | en_US |
| thesis.degree.discipline | Engineering | en_US |
| thesis.degree.grantor | Rice University | en_US |
| thesis.degree.level | Doctoral | en_US |
| thesis.degree.major | Applied Physics | en_US |
| thesis.degree.name | Doctor of Philosophy | en_US |
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