An internet of things-based intensity and time-resolved fluorescence reader for point-of-care testing

Alonso, Oscar; Franch, Nil; Canals, Joan; Arias-Alpízar, Kevin; Serna, Erica de la; Baldrich, Eva; Diéguez, Ángel

doi:10.1016/j.bios.2020.112074

Cited by 15 publications

(13 citation statements)

References 27 publications

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“…Combining with Förster Resonance Energy Transfer (FRET) techniques, FLIM can be a powerful "quantum ruler" to measure protein conformations and interactions [9][10][11][12]. Compared with fluorescence intensity imaging, FLIM is independent of the signal intensity and fluorophore concentrations, making FLIM a powerful quantitative imaging technique for applications in life sciences [13], medical diagnosis [14][15][16], drug developments [17][18][19], and flow diagnosis [20][21][22]. FLIM techniques can build on time-correlated single-photon counting (TCSPC) [23][24][25], time-gating [26][27][28], or streak cameras [29]; they record time-resolved fluorescence intensity profiles to extract lifetimes with a lifetime determination algorithm (LDA) [1].…”

Section: Introductionmentioning

confidence: 99%

Investigations on Average Fluorescence Lifetimes for Visualizing Multi-Exponential Decays

Sapermsap

Chen

et al. 2020

Front. Phys.

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Intensity-and amplitude-weighted average lifetimes, denoted as τ I and τ A hereafter, are useful indicators for revealing Förster resonance energy transfer (FRET) or fluorescence quenching behaviors. In this work, we discussed the differences between τ I and τ A and presented several model-free lifetime determination algorithms (LDA), including the center-of-mass, phasor, and integral equation methods for fast τ I and τ A estimations. For model-based LDAs, we discussed the model-mismatch problems, and the results suggest that a bi-exponential model can well approximate a signal following a multi-exponential model. Depending on the application requirements, suggestions about the LDAs to be used are given. The instrument responses of the imaging systems were included in the analysis. We explained why only using the τ I model for FRET analysis can be misleading; both τ I and τ A models should be considered. We also proposed using τ A /τ I as a new indicator on two-photon fluorescence lifetime images, and the results show that τ A /τ I is an intuitive tool for visualizing multi-exponential decays.

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Section: Introductionmentioning

confidence: 99%

Investigations on Average Fluorescence Lifetimes for Visualizing Multi-Exponential Decays

Sapermsap

Chen

et al. 2020

Front. Phys.

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“…Six studies focused on point-of-care (POC) diagnostic testing enabled with cloud-assisted IoT, including Kalasin et al [ 59 ], Kalasin et al [ 60 ], Alonso et al [ 61 ], Ma et al [ 62 ], Zhu et al [ 63 ], and Wang et al [ 64 ]. Bibi et al [ 65 ] applied a deep learning algorithm with IoT to diagnose leukemia and subtypes with blood smears.…”

Section: Resultsmentioning

confidence: 99%

A Systematic Review of Internet of Things in Clinical Laboratories: Opportunities, Advantages, and Challenges

Munir

Akbar

Ahmed

et al. 2022

Sensors

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The Internet of Things (IoT) is the network of physical objects embedded with sensors, software, electronics, and online connectivity systems. This study explores the role of IoT in clinical laboratory processes; this systematic review was conducted adhering to the PRISMA Statement 2020 guidelines. We included IoT models and applications across preanalytical, analytical, and postanalytical laboratory processes. PubMed, Cochrane Central, CINAHL Plus, Scopus, IEEE, and A.C.M. Digital library were searched between August 2015 to August 2022; the data were tabulated. Cohen’s coefficient of agreement was calculated to quantify inter-reviewer agreements; a total of 18 studies were included with Cohen’s coefficient computed to be 0.91. The included studies were divided into three classifications based on availability, including preanalytical, analytical, and postanalytical. The majority (77.8%) of the studies were real-tested. Communication-based approaches were the most common (83.3%), followed by application-based approaches (44.4%) and sensor-based approaches (33.3%) among the included studies. Open issues and challenges across the included studies included scalability, costs and energy consumption, interoperability, privacy and security, and performance issues. In this study, we identified, classified, and evaluated IoT applicability in clinical laboratory systems. This study presents pertinent findings for IoT development across clinical laboratory systems, for which it is essential that more rigorous and efficient testing and studies be conducted in the future.

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“…The fast development of mobile networks, internet of things (IoT), machine learning and AI has facilitated the development of POC devices towards remote assay operation and interpretation, more user-friendly and convenient operation, fully automated and intelligent detection, and high-efficient result readout, enabling personalized diagnosis and therapy in a fast, low cost and high-efficient way [509] , [510] , [511] , [512] , [513] , [514] . By using IoT, POC devices would be able to communicate with centralized location remotely, provide important information to the operators, and speed up the data transmission for big data analysis, which can contribute greatly to the prevention and control of pandemic such as COVID-19 [515] , [516] .…”

Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

Point-of-care diagnostics for infectious diseases: From methods to devices

et al. 2021

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An internet of things-based intensity and time-resolved fluorescence reader for point-of-care testing

Cited by 15 publications

References 27 publications

Investigations on Average Fluorescence Lifetimes for Visualizing Multi-Exponential Decays

Investigations on Average Fluorescence Lifetimes for Visualizing Multi-Exponential Decays

A Systematic Review of Internet of Things in Clinical Laboratories: Opportunities, Advantages, and Challenges

Point-of-care diagnostics for infectious diseases: From methods to devices

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