Machine Learning-Assisted Automatically Electrochemical Addressable Cytosensing Arrays for Anticancer Drug Screening.

The high-throughput and accurate screening of anticancer drugs is crucial to the preclinical assessment of candidate drugs and remains challenging. Herein, an automatically electrochemical addressable cytosensor (AEAC) for the efficient screening of anticancer drugs is reported. This sensor consists of sectionalized laser-induced graphene arrays decorated by the rhombohedral TiO2 and spherical Pt nanoparticles (LIG-TiO2-Pt) with high electrocatalytic activity for H2O2 and a homemade Ag/Pt electrode couple fixed onto the robot arm. The immobilization of laminin on the surface of LIG-TiO2-Pt can promote its biocompatibility for the growth and proliferation of various tumor cells, which empowers the in situ monitoring of H2O2 directly released from these live cells for drug screening. A machine learning (ML) algorithm is employed to eliminate the possible random or systematic errors of AEAC, realizing rapid, high-throughput, and accurate prediction of different types of anticancer drugs. This ML-assisted AEAC provides a powerful approach to accelerate the evolution of sensing-served tumor therapy.

Engineering Sandwiched Nanochannel Aptasensor for Efficiently Screening Cancer Cells.

Cancer cells are a class of important tumor biomarkers and are closely related to tumorous progression. It is urgent to develop a sensitive and highly efficient method for the rapid and accurate detection of cancer cells. Herein, an aptamer sandwiched nanochannel electrochemical sensor was established for the highly selective determination of cancer cells. By virtue of the porous nanochannels as the filter platform and immobilized with DNA aptamers for specifically capturing the cancer cells, the nanochannel-based electrochemical sensor denotes excellent performance for MCF-7 screening, and allowing a low limit of detection of 36 cells mL-1 . The nanochannels-based sandwich structure aptasensor not only presents an efficacious and reliable approach for cancer cell detection but also provides great advantage for preventing electrode passivation in the process of biomarkers analysis.

Reduced graphene oxide-ZnO nanocomposite based electrochemical sensor for sensitive and selective monitoring of 8-hydroxy-2'-deoxyguanosine.

Developing reliable and feasible electrochemical sensors for the detection of 8-hydroxy-2'-deoxyguanosine (8-OHdG) is important because the urinary level of 8-OHdG is related to cancer disease. Moreover, the co-existed uric acid (UA) as an interference severe affects the sensitive detection of 8-OHdG. Herein, sensitive monitoring of 8-OHdG was conducted using a nanocomposite of reduced graphene oxide (rGO) and ZnO nanoparticles (ZnO@rGO) as the sensing material. This nanocomposite was prepared via in-situ reduction of GO with Zn powders. Compared with those obtained on the unmodified glassy carbon electrode (GCE) and GO modified GCE (GO/GCE) the oxidation signals of 8-OHdG are significantly enhanced on the ZnO@rGO nanocomposite coated GCE (ZnO@rGO/GCE). Moreover, uricase has been employed successfully to eliminate the interferences of UA. A large amount of UA did not affect the oxidation signals of trace level of 8-OHdG. The linear range for the detection of 8-OHdG using ZnO@rGO/GCE was from 5.0 to 5000.0 nM. The detection limit was 1.25 nM calculated from a three-signal-to-noise ratio. The developed monitoring system is sensitive and selective for the determination of 8-OHdG and thus useful in practical applications, such as for the monitoring of 8-OHdG in the clinic urine samples.