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Envisioning the Future of High-Throughput Biomedical Assays through the Convergence of AI and Droplet Microfluidics

Journal of Biomedical Research & Environmental Sciences article abstract with citation details, DOI, publication dates, subject areas, full text links, and references.

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Open Access
Article Type Editorial
Subject Biology Group
OCLC JBRES Record
Yuanyuan Wei* and Ho-Pui Ho*
Issue: Volume5-Issue3
Pages: 233-236
Received: 2024-03-13
Accepted: 2024-03-14
Published: 2024-03-28

Abstract

The advent of droplet microfluidics has revolutionized the landscape of biomedical research, offering a platform for the precise control and manipulation of fluids at the microscale [1,2].

FullText HTML FullText PDF DOI: 10.37871/jbres1887 Crossmark

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© 2024 Wei Y. Distributed under Creative Commons CC-BY 4.0 Creative CommonsAttribution

How to cite this article

Wei Y, Ho-Pui H. Envisioning the Future of High-Throughput Biomedical Assays through the Convergence of AI and Droplet Microfl uidics. J Biomed Res Environ Sci. 2024 Mar 15; 5(3): 233-235. doi: 10.37871/jbres1887, Article ID: JBRES1887, Available at: https://www.jelsciences.com/articles/jbres1887.pdf

Subject area(s)

Biomedical Engineering

References

  1. Shang L, Cheng Y, Zhao Y. Emerging Droplet Microfluidics. Chem Rev. 2017 Jun 28;117(12):7964-8040. doi: 10.1021/acs.chemrev.6b00848. Epub 2017 May 24. PMID: 28537383.
  2. Wei Y, Cheng G, Ho HP, Ho YP, Yong KT. Thermodynamic perspectives on liquid-liquid droplet reactors for biochemical applications. Chem Soc Rev. 2020 Sep 21;49(18):6555-6567. doi: 10.1039/c9cs00541b. PMID: 32766625.
  3. Zhou J, Dong J, Hou H, Huang L, Li J. High-throughput microfluidic systems accelerated by artificial intelligence for biomedical applications. Lab Chip. 2024 Feb 27;24(5):1307-1326. doi: 10.1039/d3lc01012k. PMID: 38247405.
  4. Isozaki A, Harmon J, Zhou Y, Li S, Nakagawa Y, Hayashi M, Mikami H, Lei C, Goda K. AI on a chip. Lab on a Chip. 2020;20(17):3074-3090. doi: 10.1039/D0LC00521E.
  5. 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 Dec 2;13(46):19352-19366. doi: 10.1039/d1nr06195j. PMID: 34812823.
  6. Whitesides GM. The origins and the future of microfluidics. Nature. 2006 Jul 27;442(7101):368-73. doi: 10.1038/nature05058. PMID: 16871203.
  7. Mazutis L, Gilbert J, Ung WL, Weitz DA, Griffiths AD, Heyman JA. Single-cell analysis and sorting using droplet-based microfluidics. Nat Protoc. 2013 May;8(5):870-91. doi: 10.1038/nprot.2013.046. Epub 2013 Apr 4. PMID: 23558786; PMCID: PMC4128248.
  8. Jiang Z, Shi H, Tang X, Qin J. Recent advances in droplet microfluidics for single-cell analysis. TrAC - Trends in Analytical Chemistry. 2023.
  9. Liu H, Nan L, Chen F, Zhao Y, Zhao Y. Functions and applications of artificial intelligence in droplet microfluidics. Lab Chip. 2023 May 30;23(11):2497-2513. doi: 10.1039/d3lc00224a. PMID: 37199118.
  10. Wei Y, Abbasi SMT, Mehmood N, Li L, Qu F, Cheng G, Hu D, Ho YP, Yuan W, Ho HP. Deep-qGFP: A Generalist Deep Learning Assisted Pipeline for Accurate Quantification of Green Fluorescent Protein Labeled Biological Samples in Microreactors. Small Methods. 2023 Nov 27:e2301293. doi: 10.1002/smtd.202301293. Epub ahead of print. PMID: 38010980.
  11. Lamanna J, Scott EY, Edwards HS, Chamberlain MD, Dryden MDM, Peng J, Mair B, Lee A, Chan C, Sklavounos AA, Heffernan A, Abbas F, Lam C, Olson ME, Moffat J, Wheeler AR. Digital microfluidic isolation of single cells for -Omics. Nat Commun. 2020 Nov 11;11(1):5632. doi: 10.1038/s41467-020-19394-5. PMID: 33177493; PMCID: PMC7658233.
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