Biomaterials by design: Harnessing data for future development

Ke-min, Xue; Wang, Fuke; Suwardi, Ady; Han, Ming-Yong; Teo, Peili; Wang, Pei; Wang, Shijie; Ye, Enyi; Liu, Yupeng; Loh, Xian Jun

doi:10.1016/j.mtbio.2021.100165

Cited by 24 publications

(12 citation statements)

References 139 publications

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“…Theoretically, if sufficient biomedical and patient datasets are provided, machine learning can accurately diagnose early AKI by unlocking the potential of “ground truth” data, where the correlation between data and outcomes is known ( Figure 2 ). However, data collection has become a critical bottleneck for machine learning 34 . On the one hand, the training effect of machine learning is limited by the size of the dataset capacity.…”

Section: Machine Learningmentioning

confidence: 99%

Emerging early diagnostic methods for acute kidney injury

Xiao¹,

Huang²,

Yang³

et al. 2022

Theranostics

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Many factors such as trauma and COVID-19 cause acute kidney injury (AKI). Late AKI have a very high incidence and mortality rate. Early diagnosis of AKI provides a critical therapeutic time window for AKI treatment to prevent progression to chronic renal failure. However, the current clinical detection based on creatinine and urine output isn't effective in diagnosing early AKI. In recent years, the early diagnosis of AKI has made great progress with the advancement of information technology, nanotechnology, and biomedicine. These emerging methods are mainly divided into two aspects: First, predicting AKI through models construct by machine learning; Second, early diagnosis of AKI through detection of newly-discovered early biomarkers. Currently, these methods have shown great potential and become an attractive tool for the early diagnosis of AKI. Therefore, it is very important to discuss and summarize these methods for the early diagnosis of AKI. In this review, we first systematically summarize the application of machine learning in AKI prediction algorithms and specific scenarios. In addition, we introduce the key role of early biomarkers in the progress of AKI, and then comprehensively summarize the application of emerging detection technologies for early AKI. Finally, we discuss current challenges and prospects of machine learning and biomarker detection. The review is expected to provide new insights for early diagnosis of AKI, and provided important inspiration for the design of early diagnosis of other major diseases.

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Section: Machine Learningmentioning

confidence: 99%

Emerging early diagnostic methods for acute kidney injury

Xiao¹,

Huang²,

Yang³

et al. 2022

Theranostics

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“…Much of the work in decreasing thermal conductivity is aimed towards κ lat , utilizing strategies such as downscaling and isovalent substitution, which hinder the transport of heat-carrying acoustic phonons [22][23][24][25][26][27][28][29][30][31][32][33][34]. More recently, machine learning has been popularly used in conjunction with materials science discovery and air materials development [35][36][37][38]. However, the effectiveness of these strategies is limited according to the defect type and the wavelengths of phonons [18,[38][39][40][41][42].…”

Section: Thermoelectric Devicesmentioning

confidence: 99%

Potential of Recycled Silicon and Silicon-Based Thermoelectrics for Power Generation

et al. 2022

Self Cite

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Thermoelectrics can convert waste heat to electricity and vice versa. The energy conversion efficiency depends on materials figure of merit, zT, and Carnot efficiency. Due to the higher Carnot efficiency at a higher temperature gradient, high-temperature thermoelectrics are attractive for waste heat recycling. Among high-temperature thermoelectrics, silicon-based compounds are attractive due to the confluence of light weight, high abundance, and low cost. Adding to their attractiveness is the generally defect-tolerant nature of thermoelectrics. This makes them a suitable target application for recycled silicon waste from electronic (e-waste) and solar cell waste. In this review, we summarize the usage of high-temperature thermoelectric generators (TEGs) in applications such as commercial aviation and space voyages. Special emphasis is placed on silicon-based compounds, which include some recent works on recycled silicon and their thermoelectric properties. Besides materials design, device designing considerations to further maximize the energy conversion efficiencies are also discussed. The insights derived from this review can be used to guide sustainable recycling of e-waste into thermoelectrics for power harvesting.

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“…1,2 New technology requires more complex solutions, including the combination of multifunctional, manageable, sustainable and reliable materials with computer science. 3 To meet this requirement, machine learning algorithms, as one of the most advanced tools of artificial intelligence, are playing an increasingly essential role in the material design of complex and widely applicable biomedical polymers. 4,5 Generated from the Internet, machine learning was originally developed as a means of transforming intelligent programs into global engineering and scientific tools.…”

Section: Introductionmentioning

confidence: 99%

Perspectives on development of biomedical polymer materials in artificial intelligence age

Xie

2023

J Biomater Appl

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Polymer materials are widely used in biomedicine, chemistry and material science, whose traditional preparations are mainly based on experience, intuition and conceptual insight, having been applied to the development of many new materials, but facing great challenges due to the vast design space for biomedical polymers. So far, the best way to solve these problems is to accelerate material design through artificial intelligence, especially machine learning. Herein, this paper will introduce several successful cases, and analyze the latest progress of machine learning in the field of biomedical polymers, then discuss the opportunities of this novel method. In particular, this paper summarizes the material database, open-source determination tools, molecular generation methods and machine learning models that have been used for biopolymer synthesis and property prediction. Overall, machine learning could be more effectively deployed on the material design of biomedical polymers, and it is expected to become an extensive driving force to meet the huge demand for customized designs.

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Biomaterials by design: Harnessing data for future development

Cited by 24 publications

References 139 publications

Emerging early diagnostic methods for acute kidney injury

Emerging early diagnostic methods for acute kidney injury

Potential of Recycled Silicon and Silicon-Based Thermoelectrics for Power Generation

Perspectives on development of biomedical polymer materials in artificial intelligence age

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