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Distinguishing the sources of silica nanoparticles by dual isotopic fingerprinting and machine learning.

ABSTRACT: One of the key shortcomings in the field of nanotechnology risk assessment is the lack of techniques capable of source tracing of nanoparticles (NPs). Silica is the most-produced engineered nanomaterial and also widely present in the natural environment in diverse forms. Here we show that inherent isotopic fingerprints offer a feasible approach to distinguish the sources of silica nanoparticles (SiO2 NPs). We find that engineered SiO2 NPs have distinct Si-O two-dimensional (2D) isotopic fingerprints from naturally occurring SiO2 NPs, due probably to the Si and O isotope fractionation and use of isotopically different materials during the manufacturing process of engineered SiO2 NPs. A machine learning model is developed to classify the engineered and natural SiO2 NPs with a discrimination accuracy of 93.3%. Furthermore, the Si-O isotopic fingerprints are even able to partly identify the synthetic methods and manufacturers of engineered SiO2 NPs.

SUBMITTER: Yang X

PROVIDER: S-EPMC6453897 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Distinguishing the sources of silica nanoparticles by dual isotopic fingerprinting and machine learning.

Yang Xuezhi X Liu Xian X Zhang Aiqian A Lu Dawei D Li Gang G Zhang Qinghua Q Liu Qian Q Jiang Guibin G

Nature communications 20190408 1

One of the key shortcomings in the field of nanotechnology risk assessment is the lack of techniques capable of source tracing of nanoparticles (NPs). Silica is the most-produced engineered nanomaterial and also widely present in the natural environment in diverse forms. Here we show that inherent isotopic fingerprints offer a feasible approach to distinguish the sources of silica nanoparticles (SiO<sub>2</sub> NPs). We find that engineered SiO<sub>2</sub> NPs have distinct Si-O two-dimensional ...[more]

PMID: 30962437

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Project description:Nanoparticle-based drug delivery systems are among the most popular research topics in recent years. Compared with traditional drug carriers, mesoporous silica nanoparticles (MSN) offer modifiable surfaces, adjustable pore sizes and good biocompatibility. Nanoparticle-based drug delivery systems have become a research direction for many scientists. With the active target factionalized, scientists could deliver drug carriers into cancer cells successfully. However, drugs in cancer cells could elicit drug resistance and induce cell exocytosis. Thus, the drug cannot be delivered to its pharmacological location, such as the nucleus. Therefore, binding the cell membrane and the nuclear target on the nanomaterial so that the anticancer drug can be delivered to its pharmacological action site is our goal. In this study, MSN-EuGd was synthesized by doping Eu3+ and Gd3+ during the synthesis of MSN. The surface of the material was then connected to the TAT peptide as the nucleus target for targeting the cancer nucleus and then loaded with the anticancer drug camptothecin (CPT). Then, the surface of MSN-EuGd was bonded to the hyaluronic acid as an active target and gatekeeper. With this system, it is possible and desirable to achieve dual imaging and dual targeting, as well as to deliver drugs to the cell nucleus under a hyaluronidase-controlled release. The experimental approach is divided into three parts. First, we conferred the material with fluorescent and magnetic dual-imaging property by doping Eu3+ and Gd3+ into the MSN. Second, modification of the cell membrane target molecule and the nucleus target molecule occurred on the surface of the nanoparticle, making the nanoparticle a target drug carrier. Third, the loading of drug molecules into the carrier gave the entire carrier a specific target profile and enabled the ability to treat cancer. In this study, we investigated the basic properties of the drug carrier, including physical properties, chemical properties, and in vitro tests. The result showed that we have successfully designed a drug delivery system that recognizes normal cells and cancer cells and has good anticancer effects.

Dataset Information

Distinguishing the sources of silica nanoparticles by dual isotopic fingerprinting and machine learning.

Publications

Distinguishing the sources of silica nanoparticles by dual isotopic fingerprinting and machine learning.

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