ArticleBiomedical optics express2020
Cell phone digital microscopy using an oil droplet.
Article in Biomedical optics express, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Optical Technologies for Improving Healthcare in Low-Resource Settings: introduction to the feature issue.Biomedical optics express · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We introduce an accessible cell phone imaging method using small droplets of microscope immersion oil and consumer-grade oils. Oil droplets were more resistant to evaporation than water droplets, and they resolved cellular structures that were visible using a 20x/0.75 objective. We optically characterized the droplets using a cell phone screen and resolution target. We further obtained cellular resolution images of an onion epidermis and a zea stem cross-section sample. Our droplet-based method enables stable optical imaging for diagnostic and educational purposes without custom setups, specialized components, or manufacturing processes.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.