Label-free detection of Giardia lamblia cysts using a deep learning-enabled portable imaging flow cytometer.
| Autor: | Göröcs Z; Electrical and Computer Engineering Department, University of California, Los Angeles, 420 Westwood Plaza, Engineering IV. 68-119, Los Angeles, CA 90095, USA. zoligorocs@g.ucla.edu davidandrewbaum@gmail.com fangsong@ucla.edu kdehaan@ucla.edu hceylan@ucla.edu qiuyunzhe94@gmail.com veda.tslk@gmail.com chem.tama@gmail.com ozcan@ucla.edu., Baum D, Song F, de Haan K, Ceylan Koydemir H, Qiu Y, Cai Z, Skandakumar T, Peterman S, Tamamitsu M, Ozcan A |
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| Jazyk: | angličtina |
| Zdroj: | Lab on a chip [Lab Chip] 2020 Nov 24; Vol. 20 (23), pp. 4404-4412. |
| DOI: | 10.1039/d0lc00708k |
| Abstrakt: | We report a field-portable and cost-effective imaging flow cytometer that uses deep learning and holography to accurately detect Giardia lamblia cysts in water samples at a volumetric throughput of 100 mL h-1. This flow cytometer uses lens free color holographic imaging to capture and reconstruct phase and intensity images of microscopic objects in a continuously flowing sample, and automatically identifies Giardia lamblia cysts in real-time without the use of any labels or fluorophores. The imaging flow cytometer is housed in an environmentally-sealed enclosure with dimensions of 19 cm × 19 cm × 16 cm and weighs 1.6 kg. We demonstrate that this portable imaging flow cytometer coupled to a laptop computer can detect and quantify, in real-time, low levels of Giardia contamination (e.g., <10 cysts per 50 mL) in both freshwater and seawater samples. The field-portable and label-free nature of this method has the potential to allow rapid and automated screening of drinking water supplies in resource limited settings in order to detect waterborne parasites and monitor the integrity of the filters used for water treatment. |
| Databáze: | MEDLINE |
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