Machine Learning-Driven Multiobjective Optimization: An Opportunity of Microfluidic Platforms Applied in Cancer Research

Liu, Yi; Li, Sijing; Liu, Yaling

doi:10.3390/cells11050905

Cited by 13 publications

(10 citation statements)

References 54 publications

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“…As already mentioned, imFC facilitates the collection of the images of all cells in an analyzed fraction. Such huge library of normal cells vs. CTCs can potentially support an introduction of artificial intelligence methods to discriminate between those cells [ 55 , 56 , 57 , 58 , 59 ]. Finally, imFC with its high magnification option (i.e., 60×) might potentially allow the deepening of CTCs characterization by the detection of genetic aberrations assessed by fluorescent in situ hybridization (FISH) alone or in combination with protein markers [ 60 , 61 , 62 ].…”

Section: Imaging Flow Cytometry—pros and Cons In Ctc Fieldmentioning

confidence: 99%

Detection and Characterization of Circulating Tumor Cells Using Imaging Flow Cytometry—A Perspective Study

Muchlińska

Smentoch

Żaczek

et al. 2022

Cancers

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Tumor dissemination is one of the most-investigated steps of tumor progression, which in recent decades led to the rapid development of liquid biopsy aiming to analyze circulating tumor cells (CTCs), extracellular vesicles (EVs), and circulating nucleic acids in order to precisely diagnose and monitor cancer patients. Flow cytometry was considered as a method to detect CTCs; however, due to the lack of verification of the investigated cells’ identity, this method failed to reach clinical utility. Meanwhile, imaging flow cytometry combining the sensitivity and high throughput of flow cytometry and image-based detailed analysis through a high-resolution microscope might open a new avenue in CTC technologies and provide an open-platform system alternative to CellSearch®, which is still the only gold standard in this field. Hereby, we shortly review the studies on the usage of flow cytometry in CTC identification and present our own representative images of CTCs envisioned by imaging flow cytometry providing rationale that this novel technology might be a good tool for studying tumor dissemination, and, if combined with a high CTC yield enrichment method, could upgrade CTC-based diagnostics.

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Section: Imaging Flow Cytometry—pros and Cons In Ctc Fieldmentioning

confidence: 99%

Detection and Characterization of Circulating Tumor Cells Using Imaging Flow Cytometry—A Perspective Study

Muchlińska

Smentoch

Żaczek

et al. 2022

Cancers

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“…The structure of microchannels and the impact of the phase viscosity and flow rate (of the liquid) on the formation of LC orientation based on the microfluidic technology need to be further investigated, so that the LC orientation transition process can be optimized. Fabrications of microfluidic chips can be modularized using the machine learning technologies [ 93 , 141 ]. Future research can also involve integrating WGM resonator, light source, and detector onto a single photonic chip to provide a cheap, portable, stable, and multifunctional LC biosensing platform [ 142 ].…”

Section: Conclusion and Outlooksmentioning

confidence: 99%

Liquid Crystal Biosensors: Principles, Structure and Applications

Wang

et al. 2022

Biosensors

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Liquid crystals (LCs) have been widely used as sensitive elements to construct LC biosensors based on the principle that specific bonding events between biomolecules can affect the orientation of LC molecules. On the basis of the sensing interface of LC molecules, LC biosensors can be classified into three types: LC–solid interface sensing platforms, LC–aqueous interface sensing platforms, and LC–droplet interface sensing platforms. In addition, as a signal amplification method, the combination of LCs and whispering gallery mode (WGM) optical microcavities can provide higher detection sensitivity due to the extremely high quality factor and the small mode volume of the WGM optical microcavity, which enhances the interaction between the light field and biotargets. In this review, we present an overview of the basic principles, the structure, and the applications of LC biosensors. We discuss the important properties of LC and the principle of LC biosensors. The different geometries of LCs in the biosensing systems as well as their applications in the biological detection are then described. The fabrication and the application of the LC-based WGM microcavity optofluidic sensor in the biological detection are also introduced. Finally, challenges and potential research opportunities in the development of LC-based biosensors are discussed.

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“…52 One aspect of machine learning gaining attraction is for efficient design and control of microfluidics devices. 52 Apart from these, machine learning in microfluidics is speeding explorations in materials research and biomedicine, 53 droplet microfluidics, 54 drug discovery, 55 cancer research for biomarker detection and cell metathesis, 56 etc. Also, machine learning, in a combination of modeling and high-throughput experimentation (Table 2), is finding applications in polymerization research these days for self-optimization and finding out structure−property relationships for the polymerization process and increasing the speed of discovery.…”

Section: Machine-learning-assisted Automated Microfluidics For Polyol...mentioning

confidence: 99%

Perspectives on Polyolefin Catalysis in Microfluidics for High-Throughput Screening: A Minireview

Sharma

Hartman

2022

Energy Fuels

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Polyolefins are the largest produced plastics in the world, wherein continuous stirred tank reactors and fluidized bed reactors are traditionally employed for commercial production. The operating condition, reaction kinetics, and molecular interactions inside the reactor strongly affect the polyolefin properties, which require a stringent process control in conventional procedures. Understanding the catalytic pathway, behavior of polymer particles, and effect of reactor conditions is essential for designing specific polymer properties, namely, the molecular weight, chain length, polydispersity, etc. Microfluidics can play a significant role in designing polymers tailored to the user needs. Smaller channel dimensions help obtain uniform reaction conditions over the length of the microfluidic reactor in a controlled environment. With real-time monitoring techniques in microfluidics, even single-particle growth of polymer can be studied to understand the parameters affecting the polymer properties. High-throughput microfluidics can help catalyst screening in a short duration with less consumption of reagents, generating less waste. When supplemented with efficient machine-learning algorithms, automated high-throughput microfluidics has the potential to rapidly optimize the process and facilitate the development of new knowledge even with a limited data set. When trained on data sets generated using microfluidic experiments that are designed efficiently with working knowledge of the process, machine-learning algorithms can provide the relationship between the multivariable parameters space and polymer properties, which is not possible with the traditional statistical methods and interpolation techniques. The rise in the utilization of microfluidics, with the advancement of machine-learning algorithms, for polyolefin catalysis, highlights the importance of microfluidics for catalyst discovery, parameter optimization, and understanding the reaction pathway for producing polymers with specific properties for specialized applications.

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Machine Learning-Driven Multiobjective Optimization: An Opportunity of Microfluidic Platforms Applied in Cancer Research

Cited by 13 publications

References 54 publications

Detection and Characterization of Circulating Tumor Cells Using Imaging Flow Cytometry—A Perspective Study

Detection and Characterization of Circulating Tumor Cells Using Imaging Flow Cytometry—A Perspective Study

Liquid Crystal Biosensors: Principles, Structure and Applications

Perspectives on Polyolefin Catalysis in Microfluidics for High-Throughput Screening: A Minireview

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