Advances in Microfluidics: Technical Innovations and Applications in Diagnostics and Therapeutics

Analytical Chemistry, Journal Year: 2023, Volume and Issue: 95(1), P. 444 - 467

Published: Jan. 10, 2023

ADVERTISEMENT RETURN TO ISSUEPREVReviewNEXTAdvances in Microfluidics: Technical Innovations and Applications Diagnostics TherapeuticsGuillaume AubryGuillaume AubrySchool of Chemical & Biomolecular Engineering, Georgia Institute Technology, Atlanta, 30332, United StatesMore by Guillaume AubryView Biographyhttps://orcid.org/0000-0001-7828-8508, Hyun Jee LeeHyun LeeSchool LeeView Biographyhttps://orcid.org/0000-0001-9662-2063, Hang Lu*Hang LuSchool StatesPetit for Bioengineering Bioscience, States*Email: [email protected]More LuView Biographyhttps://orcid.org/0000-0002-6881-660XCite this: Anal. Chem. 2023, 95, 1, 444–467Publication Date (Web):January 10, 2023Publication History Published online10 January 2023Published inissue 10 2023https://pubs.acs.org/doi/10.1021/acs.analchem.2c04562https://doi.org/10.1021/acs.analchem.2c04562review-articleACS PublicationsCopyright © 2023 American SocietyRequest reuse permissionsArticle Views3385Altmetric-Citations2LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum full text article downloads since November 2008 (both PDF HTML) across all institutions individuals. These metrics regularly updated to reflect usage leading up last few days.Citations number other articles citing this article, calculated Crossref daily. Find more information about citation counts.The Altmetric Attention Score is a quantitative measure attention that research has received online. Clicking on donut icon will load page at altmetric.com with additional details score social media presence given article. how calculated. Share Add toView InAdd Full Text ReferenceAdd Description ExportRISCitationCitation abstractCitation referencesMore Options onFacebookTwitterWechatLinked InRedditEmail Other access optionsGet e-Alertsclose SUBJECTS:3D printing,Biotechnology,Fluid dynamics,Liquids,Sensors Get e-Alerts

Language: Английский

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Spatial redundancy transformer for self-supervised fluorescence image denoising

Nature Computational Science, Journal Year: 2023, Volume and Issue: 3(12), P. 1067 - 1080

Published: Dec. 11, 2023

Abstract Fluorescence imaging with high signal-to-noise ratios has become the foundation of accurate visualization and analysis biological phenomena. However, inevitable noise poses a formidable challenge to sensitivity. Here we provide spatial redundancy denoising transformer (SRDTrans) remove from fluorescence images in self-supervised manner. First, sampling strategy based on is proposed extract adjacent orthogonal training pairs, which eliminates dependence speed. Second, designed lightweight spatiotemporal architecture capture long-range dependencies high-resolution features at low computational cost. SRDTrans can restore high-frequency information without producing oversmoothed structures distorted traces. Finally, demonstrate state-of-the-art performance single-molecule localization microscopy two-photon volumetric calcium imaging. does not contain any assumptions about process sample, thus be easily extended various modalities applications.

Language: Английский

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Deep intravital brain tumor imaging enabled by tailored three-photon microscopy and analysis

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: Sept. 10, 2024

Language: Английский

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Surmounting photon limits and motion artifacts for biological dynamics imaging via dual-perspective self-supervised learning

PhotoniX, Journal Year: 2024, Volume and Issue: 5(1)

Published: Jan. 5, 2024

Abstract Visualizing rapid biological dynamics like neuronal signaling and microvascular flow is crucial yet challenging due to photon noise motion artifacts. Here we present a deep learning framework for enhancing the spatiotemporal relations of optical microscopy data. Our approach leverages correlations mirrored perspectives from conjugated scan paths, training model suppress blur by restoring degraded spatial features. Quantitative validation on vibrational calcium imaging validates significant gains in correlation (2.2×), signal-to-noise ratio (9–12 dB), structural similarity (6.6×), tolerance compared raw We further apply diverse viv o experiments mouse cerebral hemodynamics zebrafish cardiac dynamics. This enables clear visualization nutrient (30 mm/s) microcirculation systolic diastolic processes heartbeat (2.7 cycle/s), as well cellular vascular structure cortex. Unlike techniques relying temporal correlations, inherent priors avoids motion-induced self-supervised strategy flexibly enhances live under photon-limited motion-prone regimes.

Language: Английский

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Connecto-informatics at the mesoscale: current advances in image processing and analysis for mapping the brain connectivity

Brain Informatics, Journal Year: 2024, Volume and Issue: 11(1)

Published: June 4, 2024

Abstract Mapping neural connections within the brain has been a fundamental goal in neuroscience to understand better its functions and changes that follow aging diseases. Developments imaging technology, such as microscopy labeling tools, have allowed researchers visualize this connectivity through high-resolution brain-wide imaging. With this, image processing analysis become more crucial. However, despite wealth of images generated, access an integrated pipeline process these data is challenging due scattered information on available tools methods. To map connections, registration atlases feature extraction segmentation signal detection are necessary. In review, our provide updated overview recent advances image-processing methods, with particular focus fluorescent mouse brain. Our outline pathway toward tailored for connecto-informatics. An workflow will facilitate researchers’ approach mapping complex networks their underlying functions. By highlighting fluroscent brain, review contribute deeper grasp connecto-informatics, paving way comprehension implications.

Language: Английский

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Ultrarobust and Precise Luminescence Thermometry Enabled by the Combination of Reassembled Emission Spectra With Denoising Neural Network

Laser & Photonics Review, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 16, 2025

Abstract Nanomaterial‐based luminescence thermometry enables non‐invasive in vivo temperature measurement with high spatial resolution, which is crucial for driving advancement diagnostic and therapeutic technologies. However, spectral distortions signal attenuation resulting from complex light‐tissue interactions pose substantial challenges to the practical application of this method. Here, a new strategy presented, termed reassembled emission spectra (RaES) thermometry, ultrarobust thermal sensing biological environments. RaES integrates temperature‐sensitive features sub‐spectra multiple luminescent centers, creating thermometric parameter that exclusively governed by temperature. To enhance accuracy further, deep learning‐based denoising preliminarily incorporated into thermometry. A U‐shaped convolutional neural network model performance constructed data augmentation recover significant noise minimal bias. Empowered model, proposed approach achieves excellent results even challenging experiments, such as measurements under static blood solution interference (Δ T = 0.23 °C) real‐time monitoring during dynamic diffusion 0.37 °C), where conventional method proves completely ineffective. Being independent specific materials equipment, offers versatile adaptable harsh

Language: Английский

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High throughput analysis of rare nanoparticles with deep-enhanced sensitivity via unsupervised denoising

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: Feb. 20, 2025

The large-scale multiparametric analysis of individual nanoparticles is increasingly vital in the diverse fields biology, medicine, and materials science. However, current methods struggle with tradeoff between measurement scalability sensitivity, especially when identifying rare heterogeneous mixtures. By developing combining an unsupervised deep learning-based denoising method optofluidic device tuned for nanoparticle detection, we realize a analyzer that simultaneously achieves high scalability, throughput, sensitivity levels; name this approach "Deep Nanometry" (DNM). DNM detects polystyrene beads detection limit 30 nm at throughput over 100,000 events/second. sensitive scalable directly target extracellular vesicles (EVs) non-purified serum, making up as little 0.002% 1,214,392 total particles. Moreover, accurately sufficiently counts diagnostic marker EVs present only 0.93% 0.17% particle detections sera colorectal cancer patients healthy controls, demonstrating its potential application to early cancer.

Language: Английский

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High-Throughput, Unbiased Single-Molecule Displacement Mapping with Deep Learning Reveals Spatiotemporal Heterogeneities in Intracellular Diffusivity

ACS Photonics, Journal Year: 2025, Volume and Issue: unknown

Published: April 16, 2025

Language: Английский

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Fast and accessible morphology-free functional fluorescence imaging analysis

Published: April 20, 2025

Abstract Optical calcium imaging is a powerful tool for recording neural activity across wide range of spatial scales, from dendrites and spines to whole-brain through two-photon widefield microscopy. Traditional methods analyzing functional data rely heavily on features, such as the compact shapes somas, extract regions interest their associated temporal traces. This dependency can introduce biases in time trace estimation limit applicability these different neuronal morphologies scales. To address limitations, Graph Filtered Temporal Dictionary Learning (GraFT) uses graph-based approach identify components based shared rather than proximity, enhancing generalizability diverse datasets. Here we present significant advancements GraFT algorithm, including integration more efficient solver L1 least absolute shrinkage selection operator (LASSO) problem application compressive sensing techniques reduce computational complexity. By employing random projections dimensionality, achieve substantial speedups while maintaining analytical accuracy. These significantly accelerate making it scalable larger complex Moreover, increase accessibility, developed graphical user interface facilitate running outputs GraFT. Finally, demonstrate utility beyond meso-scale imaging, vascular axonal imaging.

Language: Английский

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Revolutionizing microfluidics with artificial intelligence: a new dawn for lab-on-a-chip technologies

Lab on a Chip, Journal Year: 2023, Volume and Issue: 23(17), P. 3737 - 3740

Published: Jan. 1, 2023

Keisuke Goda, Hang Lu, Peng Fei, and Jochen Guck introduce the AI in Microfluidics themed collection, on revolutionizing microfluidics with artificial intelligence: a new dawn for lab-on-a-chip technologies.

Language: Английский

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