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DOI

Biomaterials by design: Harnessing data for future development

23 Nov 2021-Vol. 12, pp 100165
TL;DR: In this paper, the authors discuss the recent work on the use of machine learning in the discovery and design of biomaterials, including new polymeric, metallic, ceramics, and nanomaterials.
Abstract: Biomaterials is an interdisciplinary field of research to achieve desired biological responses from new materials, regardless of material type. There have been many exciting innovations in this discipline, but commercialization suffers from a lengthy discovery to product pipeline, with many failures along the way. Success can be greatly accelerated by harnessing machine learning techniques to comb through large amounts of data. There are many potential benefits of moving from an unstructured empirical approach to a development strategy that is entrenched in data. Here, we discuss the recent work on the use of machine learning in the discovery and design of biomaterials, including new polymeric, metallic, ceramics, and nanomaterials, and how machine learning can interface with emerging use cases of 3D printing. We discuss the steps for closer integration of machine learning to make this exciting possibility a reality.
Citations
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Journal ArticleDOI
TL;DR: This review comprehensively summarizes the application of machine learning in AKI prediction algorithms and specific scenarios, and introduces the key role of early biomarkers in the progress of AKI, and comprehensively summarize theApplication of emerging detection technologies for early AKI.
Abstract: 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.

14 citations

Journal ArticleDOI
TL;DR: The Google Colab notebook as discussed by the authors provides a step-by-step guide to the use of machine learning in biomaterials development, using data from a real biomaterial design challenge based on group's research.
Abstract: The development of novel biomaterials is a challenging process, complicated by a design space with high dimensionality. Requirements for performance in the complex biological environment lead to difficult a priori rational design choices and time-consuming empirical trial-and-error experimentation. Modern data science practices, especially artificial intelligence (AI)/machine learning (ML), offer the promise to help accelerate the identification and testing of next-generation biomaterials. However, it can be a daunting task for biomaterial scientists unfamiliar with modern ML techniques to begin incorporating these useful tools into their development pipeline. This Perspective lays the foundation for a basic understanding of ML while providing a step-by-step guide to new users on how to begin implementing these techniques. A tutorial Python script has been developed walking users through the application of an ML pipeline using data from a real biomaterial design challenge based on group’s research. This tutorial provides an opportunity for readers to see and experiment with ML and its syntax in Python. The Google Colab notebook can be easily accessed and copied from the following URL: www.gormleylab.com/MLcolab

5 citations

Journal ArticleDOI
22 Feb 2022-Crystals
TL;DR: In this paper , the authors summarize the usage of high-temperature thermoelectric generators (TEGs) in applications such as commercial aviation and space voyages, which can be used to guide sustainable recycling of e-waste into TEGs for power harvesting.
Abstract: 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.

4 citations

Journal ArticleDOI
TL;DR: In this paper , the authors present a review of physical models of hydrogel release and discuss the interesting potential and challenges for programming release, and potential implications with the advent of machine learning.
Abstract: Hydrogels are a promising drug delivery system for biomedical applications due to their biocompatibility and similarity to native tissue. Programming the release rate from hydrogels is critical to ensure release of desired dosage over specified durations, particularly with the advent of more complicated medical regimens such as combinatorial drug therapy. While it is known how hydrogel structure affects release, the parameters that can be explicitly controlled to modulate release ab initio could be useful for hydrogel design. In this review, we first survey common physical models of hydrogel release. We then extensively go through the various input parameters that we can exercise direct control over, at the levels of synthesis, formulation, fabrication and environment. We also illustrate some examples where hydrogels can be programmed with the input parameters for temporally and spatially defined release. Finally, we discuss the exciting potential and challenges for programming release, and potential implications with the advent of machine learning.

3 citations

Journal ArticleDOI
TL;DR: A recent review as discussed by the authors highlights the novel methodologies, emerging strategies, and areas of potential growth within this rapidly evolving area of research, including machine-optimized biomaterial design, predictive modeling of scaffold fabrication, and spatiotemporal analysis of cell and tissue systems.
Abstract: Machine learning (ML) and artificial intelligence have accelerated scientific discovery, augmented clinical practice, and deepened fundamental understanding of many biological phenomena. ML technologies have now been applied to diverse areas of tissue engineering research, including biomaterial design, scaffold fabrication, and cell/tissue modeling. Emerging ML-empowered strategies include machine-optimized polymer synthesis, predictive modeling of scaffold fabrication processes, complex analyses of structure-function relationships, and deep learning of spatialized cell phenotypes and tissue composition. The emergence of ML in tissue engineering, while relatively recent, has already enabled increasingly complex and multivariate analyses of the relationships between biological, chemical, and physical factors in driving tissue regenerative outcomes. This review highlights the novel methodologies, emerging strategies, and areas of potential growth within this rapidly evolving area of research. Impact statement Machine learning (ML) has accelerated scientific discovery and augmented clinical practice across multiple fields. Now, ML has driven exciting new paradigms in tissue engineering research, including machine-optimized biomaterial design, predictive modeling of scaffold fabrication, and spatiotemporal analysis of cell and tissue systems. The emergence of ML in tissue engineering, while relatively recent, has already enabled increasingly complex analyses of the relationships between biological, chemical, and physical factors in driving tissue regenerative outcomes. This review highlights the novel methodologies, emerging strategies, and areas of potential growth within this rapidly evolving area of research.

2 citations

References
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Journal ArticleDOI
TL;DR: The Materials Project (www.materialsproject.org) is a core program of the Materials Genome Initiative that uses high-throughput computing to uncover the properties of all known inorganic materials as discussed by the authors.
Abstract: Accelerating the discovery of advanced materials is essential for human welfare and sustainable, clean energy. In this paper, we introduce the Materials Project (www.materialsproject.org), a core program of the Materials Genome Initiative that uses high-throughput computing to uncover the properties of all known inorganic materials. This open dataset can be accessed through multiple channels for both interactive exploration and data mining. The Materials Project also seeks to create open-source platforms for developing robust, sophisticated materials analyses. Future efforts will enable users to perform ‘‘rapid-prototyping’’ of new materials in silico, and provide researchers with new avenues for cost-effective, data-driven materials design. © 2013 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution 3.0 Unported License.

6,566 citations

Journal ArticleDOI
TL;DR: This review summarizes the main advances published over the last 15 years, outlining the synthesis, biodegradability and biomedical applications ofBiodegradable synthetic and natural polymers.

3,801 citations

Journal ArticleDOI
TL;DR: A theoretical model to explain the interfacial bonding is based upon in-vitro studies of glass-ceramic solubility in interfacial hydroxyapatite crystallization mechanisms, compared with in- vivo rat femur implant histology and ultrastructure results.
Abstract: The development of a bone-bonding calcia-phosposilicate glass-ceramic is discussed. A theoretical model to explain the interfacial bonding is based upon in-vitro studies of glass-ceramic solubility in interfacial hydroxyapatite crystallization mechanisms, compared with in-vivo rat femur implant histology and ultrastructure results.

2,619 citations

Journal ArticleDOI
TL;DR: Kevin Shakesheff investigates new methods of engineering polymer surfaces and the application of these engineered materials in drug delivery and tissue engineering.
Abstract: s, and 360 patents, and edited 12 books. He has also received over 80 major awards including the Gairdner Foundation International Award, Lemelson-MIT prize, ACS’s Applied Polymer Science and Polymer Chemistry Awards, AICHE’s Professional Progress, Bioengineering, Walker and Stine Materials Science and Engineering Awards. In 1989, Dr. Langer was elected to the Institute of Medicine of the National Academy of Sciences, and in 1992 he was elected to both the National Academy of Engineering and the National Academy of Sciences. He is the only active member of all three National Academies. Kevin Shakesheff was born in Ashington, Northumberland, U.K., in 1969. He received his Bacheclor of Pharmacy degree from the University of Nottingham in 1991 and a Ph.D. from the same institution in 1995. In 1996 he became a NATO Postdoctoral Fellow at MIT, Department of Chemical Engineering. He is currently an EPSRC Advanced Fellow at the School of Pharmaceutical Sciences, The University of Nottingham. His research group investigates new methods of engineering polymer surfaces and the application of these engineered materials in drug delivery and tissue engineering. 3182 Chemical Reviews, 1999, Vol. 99, No. 11 Uhrich et al.

2,532 citations

Journal ArticleDOI
TL;DR: This Review discusses how different mechanisms interact and can be integrated to exert fine control in time and space over the drug presentation, and collects experimental release data from the literature and presents quantitative comparisons between different systems to provide guidelines for the rational design of hydrogel delivery systems.
Abstract: Hydrogel delivery systems can leverage therapeutically beneficial outcomes of drug delivery and have found clinical use. Hydrogels can provide spatial and temporal control over the release of various therapeutic agents, including small-molecule drugs, macromolecular drugs and cells. Owing to their tunable physical properties, controllable degradability and capability to protect labile drugs from degradation, hydrogels serve as a platform in which various physiochemical interactions with the encapsulated drugs control their release. In this Review, we cover multiscale mechanisms underlying the design of hydrogel drug delivery systems, focusing on physical and chemical properties of the hydrogel network and the hydrogel-drug interactions across the network, mesh, and molecular (or atomistic) scales. We discuss how different mechanisms interact and can be integrated to exert fine control in time and space over the drug presentation. We also collect experimental release data from the literature, review clinical translation to date of these systems, and present quantitative comparisons between different systems to provide guidelines for the rational design of hydrogel delivery systems.

2,457 citations

Trending Questions (1)
What are the machine learning tools can be used for biomaterial design and development?

Machine learning techniques can be used for biomaterial design and development, as discussed in the paper "Biomaterials by design: Harnessing data for future development."