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Accessible Platforms for Anomalous Cell Detection Beyond Laboratory Boundaries.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Accessible Platforms for Anomalous Cell Detection Beyond Laboratory Boundaries./
作者:	Deshmukh, Shreya.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	218 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-09, Section: B.
Contained By:	Dissertations Abstracts International83-09B.
標題:	Infections. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29003887
ISBN:	9798209788416

Accessible Platforms for Anomalous Cell Detection Beyond Laboratory Boundaries.
Deshmukh, Shreya.

Accessible Platforms for Anomalous Cell Detection Beyond Laboratory Boundaries. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 218 p.

Source: Dissertations Abstracts International, Volume: 83-09, Section: B.

Thesis (Ph.D.)--Stanford University, 2021.

This item must not be sold to any third party vendors.

Resource limitations and socioeconomic challenges pose obstacles to equitable healthcare access worldwide. Accurate diagnosis is an important early step in which technical innovation can help to bridge some gaps. Towards this goal, I worked on developing anomalous cell detection tools to enable broader access to health information for resource-constrained clinical settings. In this thesis, I explore portable tools for detecting malaria in blood samples and screening forensic swab samples. Quantifying the malaria-causing parasite Plasmodium falciparum relies on isolating the ring-stage infected red blood cells, which are challenging to differentiate from the majority of uninfected cells. I used magnetic levitation to perform label-free biophysical separation and detection of malaria-infected cells, by cumulatively leveraging density and magnetic susceptibility differences in the cells. Next, I built an accessible platform to process, image, and analyse whole blood patient samples in field settings, in a malaria-endemic region of Uganda. I combined cell lysis, suspension, and machine learning on cellphone images to develop an automated morphology-based classifier for blood samples. I also explored a different application for portable image-based screening, to address a sample processing backlog in sequencing forensic samples in the sexual assault justice workflow. I developed an automated algorithm for differentiating sperm from epithelial cells in sexual assault swab samples, using morphology features captured in cellphone imaging of microchips. Overall, this work demonstrates examples of anomalous cell detection technology built specifically for under-resourced settings. Such interdisciplinary techniques could help to overcome existing gaps in access to biological data, with the aims of supporting accurate diagnosis and surveillance of malaria, or screening of backlogged sexual assault samples.

ISBN: 9798209788416Subjects--Topical Terms:

1621997
Infections.

Accessible Platforms for Anomalous Cell Detection Beyond Laboratory Boundaries.
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