Combination of liquid crystal and deep learning reveals distinct signatures of Parkinson's disease‐related wild‐type α‐synuclein and six pathogenic mutants

Yang, Zhongqiang; Yang, Xiuxiu; Zhao, Hansen; Zhang, Sichun; Zhang, Claire Xi

doi:10.1002/asia.202101251

Cited by 4 publications

(1 citation statement)

References 53 publications

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“…Through the combination of deep learning, the distinct features formed in LC images by wild-type and six familial mutant α-synucleins were recognized with a high accuracy of 98.3 ± 1.3%. It provided a reliable platform for α-synuclein mutation identification in clinical applications [ 168 ].…”

Section: Machine-learning-assisted Lc Sensormentioning

confidence: 99%

State-of-the-Art Development in Liquid Crystal Biochemical Sensors

et al. 2022

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As an emerging stimuli-responsive material, liquid crystal (LC) has attracted great attentions beyond display applications, especially in the area of biochemical sensors. Its high sensitivity and fast response to various biological or chemical analytes make it possible to fabricate a simple, real-time, label-free, and cost-effective LC-based detection platform. Advancements have been achieved in the development of LC-based sensors, both in fundamental research and practical applications. This paper briefly reviews the state-of-the-art research on LC sensors in the biochemical field, from basic properties of LC material to the detection mechanisms of LC sensors that are categorized into LC-solid, LC–aqueous, and LC droplet platforms. In addition, various analytes detected by LCs are presented as a proof of the application value, including metal ions, nucleic acids, proteins, glucose, and some toxic chemical substances. Furthermore, a machine-learning-assisted LC sensing platform is realized to provide a foundation for device intelligence and automatization. It is believed that a portable, convenient, and user-friendly LC-based biochemical sensing device will be achieved in the future.

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Section: Machine-learning-assisted Lc Sensormentioning

confidence: 99%

State-of-the-Art Development in Liquid Crystal Biochemical Sensors

et al. 2022

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Aquatic Functional Liquid Crystals: Design, Functionalization, and Molecular Simulation

Kato,

Uchida,

Ishii

et al. 2023

Advanced Science

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Aquatic functional liquid crystals, which are ordered molecular assemblies that work in water environment, are described in this review. Aquatic functional liquid crystals are liquid‐crystalline (LC) materials interacting water molecules or aquatic environment. They include aquatic lyotropic liquid crystals and LC based materials that have aquatic interfaces, for example, nanoporous water treatment membranes that are solids preserving LC order. They can remove ions and viruses with nano‐ and subnano‐porous structures. Columnar, smectic, bicontinuous LC structures are used for fabrication of these 1D, 2D, 3D materials. Design and functionalization of aquatic LC sensors based on aqueous/LC interfaces are also described. The ordering transitions of liquid crystals induced by molecular recognition at the aqueous interfaces provide distinct optical responses. Molecular orientation and dynamic behavior of these aquatic functional LC materials are studied by molecular dynamics simulations. The molecular interactions of LC materials and water are key of these investigations. New insights into aquatic functional LC materials contribute to the fields of environment, healthcare, and biotechnology.

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The use of artificial intelligence in liquid crystal applications: A review

Chattha,

Chan,

Upreti

2024

Can J Chem Eng

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Recent advancements in artificial intelligence (AI) have significantly influenced scientific discovery and analysis, including liquid crystals. This paper reviews the use of AI in predicting the properties of liquid crystals and improving their sensing applications. Typically, liquid crystals are utilized as sensors in biomedical detection and diagnostics, and in the detection of heavy metal ions and gases. Traditional methods of analysis used in these applications are often subjective, expensive, and time‐consuming. To surmount these challenges, AI methods such as convolutional neural networks (CNN) and support vector machines (SVM) have been recently utilized to predict liquid crystal properties and improve the resulting performance of the sensing applications. Large amounts of data are, however, required to fully realize the potential of AI methods, which would also need adequate ethical oversight. In addition to experiments, modelling approaches utilizing first principles as well as AI may be employed to supplement and furnish the data. In summary, the review indicates that AI methods hold great promise in the further development of the liquid crystal technology.

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Combination of liquid crystal and deep learning reveals distinct signatures of Parkinson's disease‐related wild‐type α‐synuclein and six pathogenic mutants

Cited by 4 publications

References 53 publications

State-of-the-Art Development in Liquid Crystal Biochemical Sensors

State-of-the-Art Development in Liquid Crystal Biochemical Sensors

Aquatic Functional Liquid Crystals: Design, Functionalization, and Molecular Simulation

The use of artificial intelligence in liquid crystal applications: A review

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