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For: Isozaki A, Harmon J, Zhou Y, Li S, Nakagawa Y, Hayashi M, Mikami H, Lei C, Goda K. AI on a chip. Lab Chip 2020;20:3074-90. [PMID: 32644061 DOI: 10.1039/d0lc00521e] [Cited by in Crossref: 23] [Cited by in F6Publishing: 4] [Article Influence: 23.0] [Reference Citation Analysis]
Number Citing Articles
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6 Harmon J, Findinier J, Ishii NT, Herbig M, Isozaki A, Grossman A, Goda K. Intelligent image-activated sorting of Chlamydomonas reinhardtii by mitochondrial localization. Cytometry A 2022. [PMID: 35643943 DOI: 10.1002/cyto.a.24661] [Reference Citation Analysis]
7 Acharyya S, Nag S, Guha PK. Ultra-selective tin oxide-based chemiresistive gas sensor employing signal transform and machine learning techniques. Analytica Chimica Acta 2022. [DOI: 10.1016/j.aca.2022.339996] [Reference Citation Analysis]
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9 Deng Y, Duque JA, Su C, Zhou Y, Nishikawa M, Xiao T, Yatomi Y, Hou HW, Goda K. Understanding stenosis-induced platelet aggregation on a chip by high-speed optical imaging. Sensors and Actuators B: Chemical 2022;356:131318. [DOI: 10.1016/j.snb.2021.131318] [Reference Citation Analysis]
10 Pouyanfar N, Harofte SZ, Soltani M, Siavashy S, Asadian E, Ghorbani-bidkorbeh F, Keçili R, Hussain CM. Artificial intelligence-based microfluidic platforms for the sensitive detection of environmental pollutants: Recent advances and prospects. Trends in Environmental Analytical Chemistry 2022. [DOI: 10.1016/j.teac.2022.e00160] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
11 Bhuiyan NH, Hong JH, Uddin MJ, Shim JS. Artificial Intelligence-Controlled Microfluidic Device for Fluid Automation and Bubble Removal of Immunoassay Operated by a Smartphone. Anal Chem 2022. [PMID: 35179372 DOI: 10.1021/acs.analchem.1c04827] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 5.0] [Reference Citation Analysis]
12 Li W, Zhou Y, Deng Y, Khoo BL. Early Predictor Tool of Disease Using Label-Free Liquid Biopsy-Based Platforms for Patient-Centric Healthcare. Cancers 2022;14:818. [DOI: 10.3390/cancers14030818] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
13 Yin D, Li Y, Xia L, Li W, Chu W, Yu J, Wu M, Cheng Y, Hu M. Automated synthesis of gadopentetate dimeglumine through solid-liquid reaction in femtosecond laser fabricated microfluidic chips. Chinese Chemical Letters 2022;33:1077-80. [DOI: 10.1016/j.cclet.2021.05.073] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
14 Dittrich P, Kraus D, Ehrhardt E, Henkel T, Notni G. Multispectral Imaging Flow Cytometry with Spatially and Spectrally Resolving Snapshot-Mosaic Cameras for the Characterization and Classification of Bioparticles. Micromachines 2022;13:238. [DOI: 10.3390/mi13020238] [Reference Citation Analysis]
15 Del Giudice F. A Review of Microfluidic Devices for Rheological Characterisation. Micromachines 2022;13:167. [DOI: 10.3390/mi13020167] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 7.0] [Reference Citation Analysis]
16 Anandakumaran PN, Ayers AG, Muranski P, Creusot RJ, Sia SK. Rapid video-based deep learning of cognate versus non-cognate T cell-dendritic cell interactions. Sci Rep 2022;12:559. [PMID: 35017558 DOI: 10.1038/s41598-021-04286-5] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
17 Zheng J, Cole T, Zhang Y, Kim J, Tang SY. Exploiting machine learning for bestowing intelligence to microfluidics. Biosens Bioelectron 2021;194:113666. [PMID: 34600338 DOI: 10.1016/j.bios.2021.113666] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
18 Nishikawa M, Kanno H, Zhou Y, Xiao TH, Suzuki T, Ibayashi Y, Harmon J, Takizawa S, Hiramatsu K, Nitta N, Kameyama R, Peterson W, Takiguchi J, Shifat-E-Rabbi M, Zhuang Y, Yin X, Rubaiyat AHM, Deng Y, Zhang H, Miyata S, Rohde GK, Iwasaki W, Yatomi Y, Goda K. Massive image-based single-cell profiling reveals high levels of circulating platelet aggregates in patients with COVID-19. Nat Commun 2021;12:7135. [PMID: 34887400 DOI: 10.1038/s41467-021-27378-2] [Reference Citation Analysis]
19 Liu L, Bi M, Wang Y, Liu J, Jiang X, Xu Z, Zhang X. Artificial intelligence-powered microfluidics for nanomedicine and materials synthesis. Nanoscale 2021;13:19352-66. [PMID: 34812823 DOI: 10.1039/d1nr06195j] [Reference Citation Analysis]
20 Feng Y, Cheng Z, Chai H, He W, Huang L, Wang W. Neural network-enhanced real-time impedance flow cytometry for single-cell intrinsic characterization. Lab Chip 2021. [PMID: 34849522 DOI: 10.1039/d1lc00755f] [Cited by in Crossref: 9] [Cited by in F6Publishing: 4] [Article Influence: 9.0] [Reference Citation Analysis]
21 Lee SM, Balakrishnan HK, Yuan D, Nai YH, Guijt RM. Perspective - what constitutes a quality analytical paper: Microfluidics and Flow analysis. Talanta Open 2021;4:100055. [DOI: 10.1016/j.talo.2021.100055] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
22 Srikanth S, Dubey SK, Javed A, Goel S. Droplet based microfluidics integrated with machine learning. Sensors and Actuators A: Physical 2021;332:113096. [DOI: 10.1016/j.sna.2021.113096] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
23 Song P, Guo C, Jiang S, Wang T, Hu P, Hu D, Zhang Z, Feng B, Zheng G. Optofluidic ptychography on a chip. Lab Chip 2021;21:4549-56. [PMID: 34726219 DOI: 10.1039/d1lc00719j] [Reference Citation Analysis]
24 Xin L, Xiao W, Che L, Liu J, Miccio L, Bianco V, Memmolo P, Ferraro P, Li X, Pan F. Label-Free Assessment of the Drug Resistance of Epithelial Ovarian Cancer Cells in a Microfluidic Holographic Flow Cytometer Boosted through Machine Learning. ACS Omega 2021;6:31046-57. [PMID: 34841147 DOI: 10.1021/acsomega.1c04204] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
25 Manteca A, Gadea A, Van Assche D, Cossard P, Gillard-Bocquet M, Beneyton T, Innis CA, Baret JC. Directed Evolution in Drops: Molecular Aspects and Applications. ACS Synth Biol 2021;10:2772-83. [PMID: 34677942 DOI: 10.1021/acssynbio.1c00313] [Reference Citation Analysis]
26 Puthongkham P, Wirojsaengthong S, Suea-Ngam A. Machine learning and chemometrics for electrochemical sensors: moving forward to the future of analytical chemistry. Analyst 2021;146:6351-64. [PMID: 34585185 DOI: 10.1039/d1an01148k] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
27 Loo MH, Nakagawa Y, Kim SH, Isozaki A, Goda K. High-throughput sorting of nanoliter droplets enabled by a sequentially addressable dielectrophoretic array. Electrophoresis 2021. [PMID: 34599837 DOI: 10.1002/elps.202100057] [Reference Citation Analysis]
28 Koide H, Okishima A, Hoshino Y, Kamon Y, Yoshimatsu K, Saito K, Yamauchi I, Ariizumi S, Zhou Y, Xiao TH, Goda K, Oku N, Asai T, Shea KJ. Synthetic hydrogel nanoparticles for sepsis therapy. Nat Commun 2021;12:5552. [PMID: 34548486 DOI: 10.1038/s41467-021-25847-2] [Reference Citation Analysis]
29 Thakur S, Dasmahapatra AK, Bandyopadhyay D. Functional liquid droplets for analyte sensing and energy harvesting. Adv Colloid Interface Sci 2021;294:102453. [PMID: 34120038 DOI: 10.1016/j.cis.2021.102453] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
30 De Chiara F, Ferret-Miñana A, Ramón-Azcón J. The Synergy between Organ-on-a-Chip and Artificial Intelligence for the Study of NAFLD: From Basic Science to Clinical Research. Biomedicines 2021;9:248. [PMID: 33801289 DOI: 10.3390/biomedicines9030248] [Reference Citation Analysis]
31 Lawson M, Elf J. Imaging-based screens of pool-synthesized cell libraries. Nat Methods 2021;18:358-65. [PMID: 33589838 DOI: 10.1038/s41592-020-01053-8] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
32 Chen PC, Lin YT, Truong CM, Chen PS, Chiang HK. Development of an Automated Optical Inspection System for Rapidly and Precisely Measuring Dimensions of Embedded Microchannel Structures in Transparent Bonded Chips. Sensors (Basel) 2021;21:698. [PMID: 33498437 DOI: 10.3390/s21030698] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
33 Kameyama R, Takizawa S, Hiramatsu K, Goda K. Dual-Comb Coherent Raman Spectroscopy with near 100% Duty Cycle. ACS Photonics 2021;8:975-81. [DOI: 10.1021/acsphotonics.0c01656] [Cited by in Crossref: 12] [Cited by in F6Publishing: 7] [Article Influence: 6.0] [Reference Citation Analysis]
34 Ahmadi F, Quach ABV, Shih SCC. Is microfluidics the "assembly line" for CRISPR-Cas9 gene-editing? Biomicrofluidics 2020;14:061301. [PMID: 33262863 DOI: 10.1063/5.0029846] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
35 Tang R, Zhang Z, Chen X, Waller L, Zhang AC, Chen J, Han Y, An C, Cho SH, Lo Y. 3D side-scattering imaging flow cytometer and convolutional neural network for label-free cell analysis. APL Photonics 2020;5:126105. [DOI: 10.1063/5.0024151] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.5] [Reference Citation Analysis]