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Deciphering controversial results of cell proliferation on TiO₂ nanotubes using machine learning.

ABSTRACT: With the rapid development of biomedical sciences, contradictory results on the relationships between biological responses and material properties emerge continuously, adding to the challenge of interpreting the incomprehensible interfacial process. In the present paper, we use cell proliferation on titanium dioxide nanotubes (TNTs) as a case study and apply machine learning methodologies to decipher contradictory results in the literature. The gradient boosting decision tree model demonstrates that cell density has a higher impact on cell proliferation than other obtainable experimental features in most publications. Together with the variation of other essential features, the controversy of cell proliferation trends on various TNTs is understandable. By traversing all combinational experimental features and the corresponding forecast using an exhausted grid search strategy, we find that adjusting cell density and sterilization methods can simultaneously induce opposite cell proliferation trends on various TNTs diameter, which is further validated by experiments. This case study reveals that machine learning is a burgeoning tool in deciphering controversial results in biomedical researches, opening up an avenue to explore the structure-property relationships of biomaterials.

SUBMITTER: Shen Z

PROVIDER: S-EPMC8218935 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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Deciphering controversial results of cell proliferation on TiO<sub>2</sub> nanotubes using machine learning.

Shen Ziao Z Wang Si S Shen Zhenyu Z Tang Yufei Y Xu Junbin J Lin Changjian C Chen Xun X Huang Qiaoling Q

Regenerative biomaterials 20210621 4

With the rapid development of biomedical sciences, contradictory results on the relationships between biological responses and material properties emerge continuously, adding to the challenge of interpreting the incomprehensible interfacial process. In the present paper, we use cell proliferation on titanium dioxide nanotubes (TNTs) as a case study and apply machine learning methodologies to decipher contradictory results in the literature. The gradient boosting decision tree model demonstrates ...[more]

PMID: 34168893

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Project description:Understanding the electrical conduction properties of a single nanostructure is essential for gaining insight into the fundamental charge transport through 1D materials and also for exploring the collective behavior of an array of such nanostructures. TiO2 nanostructures, such as electrochemically grown nanotubes, have been widely studied in recent times for several applications. The electrolyte plays a vital role in deciding the morphology, which, in turn, governs the charge transport behavior. Here we present a comparative study of the charge transport through a single TiO2 nanotube grown by electrochemical anodization using ethylene glycol and dimethyl sulphoxide electrolytes. The individual nanotubes are assembled into nanodevices using photolithography without relying on complex and sophisticated process like electron beam lithography or focused ion beam deposition. The electric field dependent charge transport properties show Schottky emission at a lower field regime and Poole-Frenkel emission in the higher region. The temperature-dependent electrical conduction (110 K-410 K) is mediated by two thermal activation processes, attributed to shallow impurities in the low-temperature range (T < 230 K) and to the donors at deep intermediate levels at higher temperatures (T > 230 K). The activation energies for EG based nanotubes are found to be higher than those for DMSO nanotubes owing to the double wall morphology of the formed tubes. Also, the study of the electrical breakdown phenomena of these nanotubes reveals three distinct categories of collapse. 'Model A' type breakdown is characterized by a stepwise rise of the current up to the breakdown point and a fall to zero following a non-uniform step by step decrease, which is driven by crack formation near the electrode interface and its propagation. 'Model B' shows a transient rise and fall in current, leading to breakdown due to electromigration, whereas 'Model C' type breakdown observed in a bundle of nanotubes shows a mixed trend of 'Model A' and 'Model B'. The data and analysis provide insight into the current limit through an individual nanotube or bundle of nanotubes and will be useful for designing prototype nanodevices from titania nanostructures.

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Deciphering controversial results of cell proliferation on TiO₂ nanotubes using machine learning.

Publications

Deciphering controversial results of cell proliferation on TiO<sub>2</sub> nanotubes using machine learning.

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Deciphering controversial results of cell proliferation on TiO2 nanotubes using machine learning.

Publications

Deciphering controversial results of cell proliferation on TiO<sub>2</sub> nanotubes using machine learning.

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OmicsDI is part of the ELIXIR infrastructure

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Deciphering controversial results of cell proliferation on TiO₂ nanotubes using machine learning.