Dataset Information

Smartphone-based low light detection for bioluminescence application.

ABSTRACT: We report a smartphone-based device and associated imaging-processing algorithm to maximize the sensitivity of standard smartphone cameras, that can detect the presence of single-digit pW of radiant flux intensity. The proposed hardware and software, called bioluminescent-based analyte quantitation by smartphone (BAQS), provides an opportunity for onsite analysis and quantitation of luminescent signals from biological and non-biological sensing elements which emit photons in response to an analyte. A simple cradle that houses the smartphone, sample tube, and collection lens supports the measuring platform, while noise reduction by ensemble averaging simultaneously lowers the background and enhances the signal from emitted photons. Five different types of smartphones, both Android and iOS devices, were tested, and the top two candidates were used to evaluate luminescence from the bioluminescent reporter Pseudomonas fluorescens M3A. The best results were achieved by OnePlus One (android), which was able to detect luminescence from ~10⁶?CFU/mL of the bio-reporter, which corresponds to ~10⁷ photons/s with 180?seconds of integration time.

SUBMITTER: Kim H

PROVIDER: S-EPMC5220360 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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Publications

Smartphone-based low light detection for bioluminescence application.

Kim Huisung H Jung Youngkee Y Doh Iyll-Joon IJ Lozano-Mahecha Roxana Andrea RA Applegate Bruce B Bae Euiwon E

Scientific reports 20170109

We report a smartphone-based device and associated imaging-processing algorithm to maximize the sensitivity of standard smartphone cameras, that can detect the presence of single-digit pW of radiant flux intensity. The proposed hardware and software, called bioluminescent-based analyte quantitation by smartphone (BAQS), provides an opportunity for onsite analysis and quantitation of luminescent signals from biological and non-biological sensing elements which emit photons in response to an analy ...[more]

PMID: 28067287

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Project description:BackgroundFueled by rapid technological advances over the past decade, there is growing interest in the use of smartphones to aid in smoking cessation. Hundreds of applications have been developed for this purpose, but little is known about how these applications are accessed and used by smokers or what features smokers believe would be most useful.PurposeThe present study sought to understand the prevalence of smartphone ownership and patterns of use among smokers as well as the perceived utility of various smartphone application features for smoking cessation that are currently in development or already available.MethodsDaily cigarette smokers (n = 224) reported on smartphone ownership, their patterns of smartphone usage, and perceived utility of features. Features were ranked according to perceived utility and differences in both perceived utility and general smartphone use patterns were examined as a function of demographic and smoking-related variables.ResultsMost smokers (80.4%) own a smartphone, but experience with smoking cessation applications is extremely rare (6.1%). Ownership and patterns of usage differed as a function of demographic and smoking-related variables. Overall, gain-framed features were rated as most useful, while loss-framed and interpersonal features were rated as least useful.ConclusionsMobile health interventions have the potential to reach a large number of smokers but are currently underutilized. Additional effort is needed to ensure parity in treatment access. Gain-framed messages may be especially useful for engaging smokers, even if other features ultimately drive treatment effects.ImplicationsThis study describes patterns of smartphone usage among smokers and identifies the smartphone application features smokers believe would be most useful during a quit attempt. Findings indicate which subgroups of smokers are most likely to be reached with mobile health interventions and suggests that inclusion of specific features may be helpful for engaging smokers in the smoking cessation process.

Dataset Information

Smartphone-based low light detection for bioluminescence application.

Publications

Smartphone-based low light detection for bioluminescence application.

Similar Datasets

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