Dataset Information

Parametric optimization of electric field strength for cancer electrochemotherapy on a chip-based model.

ABSTRACT: Electrochemotherapy (ECT), as one of the very few available treatments for cutaneous and subcutaneous tumors when surgery and radiotherapy are no longer available, requires applying a proper electric field to the tumor to realize electroporation-mediated cytotoxic drug delivery. It is impossible to exhaust all possible electrical parameters on patients to realize the optimal tradeoff between tumor suppression and adverse effects. To address this issue, this study provides a feasible solution by developing a four-leaf micro-electrode chip (F-MEC) in which the electric field was specially designed by linear distribution to cover all possible electric field strengths for ECT. Methods: We developed a F-MEC that provides a linearly varied electric field and a capacity for in situ observation of cell status. By culturing tumor cells on the F-MEC surface and in situ monitoring the cell responses to ECT drugs, the optimal electric field strength for any given cell type could be rapidly and accurately calculated in a few, or even only one, simple assay. Results: Using this chip, we monitored MCF-7 and A315 cell responses to ECT and determined the optimum ECT voltage. More importantly, we successfully verified that the in vitro determined voltage coincided with the optimal value for in vivo ECT in mice. Conclusion: In this proof-of-concept study, the in vivo tumor suppression assays proved that the optimal parameters acquired from in vitro F-MEC assay could be used for in vivo ECT.

SUBMITTER: Zhao D

PROVIDER: S-EPMC5743553 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Parametric optimization of electric field strength for cancer electrochemotherapy on a chip-based model.

Zhao Deyao D Wu Mengxi M Huang Dong D Liang Zicai Z Wei Zewen Z Li Zhihong Z

Theranostics 20180101 2

Electrochemotherapy (ECT), as one of the very few available treatments for cutaneous and subcutaneous tumors when surgery and radiotherapy are no longer available, requires applying a proper electric field to the tumor to realize electroporation-mediated cytotoxic drug delivery. It is impossible to exhaust all possible electrical parameters on patients to realize the optimal tradeoff between tumor suppression and adverse effects. To address this issue, this study provides a feasible solution by ...[more]

PMID: 29290813

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Project description:The electrical discharge machining (EDM) process is one of the most efficient non-conventional precise material removal processes. It is a smart process used to intricately shape hard metals by creating spark erosion in electroconductive materials. Sparking occurs in the gap between the tool and workpiece. This erosion removes the material from the workpiece by melting and vaporizing the metal in the presence of dielectric fluid. In recent years, EDM has evolved widely on the basis of its electrical and non-electrical parameters. Recent research has sought to investigate the optimal machining parameters for EDM in order to make intricate shapes with greater accuracy and better finishes. Every method employed in the EDM process has intended to enhance the capability of machining performance by adopting better working conditions and developing techniques to machine new materials with more refinement. This new research aims to optimize EDM's electrical parameters on the basis of multi-shaped electrodes in order to obtain a good surface finish and high dimensional accuracy and to improve the post-machining hardness in order to improve the overall quality of the machined profile. The optimization of electrical parameters, i.e., spark voltage, current, pulse-on time and depth of cut, has been achieved by conducting the experimentation on die steel D2 with a specifically designed multi-shaped copper electrode. An experimental design is generated using a statistical tool, and actual machining is performed to observe the surface roughness, variations on the surface hardness and dimensional stability. A full factorial design of experiment (DOE) approach has been followed (as there are more than two process parameters) to prepare the samples via EDM. Regression analysis and analysis of variance (ANOVA) for the interpretation and optimization of results has been carried out using Minitab as a statistical tool. The validation of experimental findings with statistical ones confirms the significance of each operating parameter on the output parameters. Hence, the most optimized relationships were found and presented in the current study.

Project description:BackgroundDuring transcranial magnetic stimulation (TMS) a coil placed on the scalp is used to non-invasively modulate activity of targeted brain networks via a magnetically induced electric field (E-field). Ideally, the E-field induced during TMS is concentrated on a targeted cortical region of interest (ROI). Determination of the coil position and orientation that best achieve this objective presently requires a large computational effort.ObjectiveTo improve the accuracy of TMS we have developed a fast computational auxiliary dipole method (ADM) for determining the optimum coil position and orientation. The optimum coil placement maximizes the E-field along a predetermined direction or, alternatively, the overall E-field magnitude in the targeted ROI. Furthermore, ADM can assess E-field uncertainty resulting from precision limitations of TMS coil placement protocols.MethodADM leverages the electromagnetic reciprocity principle to compute rapidly the TMS induced E-field in the ROI by using the E-field generated by a virtual constant current source residing in the ROI. The framework starts by solving for the conduction currents resulting from this ROI current source. Then, it rapidly determines the average E-field induced in the ROI for each coil position by using the conduction currents and a fast-multipole method. To further speed-up the computations, the coil is approximated using auxiliary dipoles enabling it to represent all coil orientations for a given coil position with less than 600 dipoles.ResultsUsing ADM, the E-fields generated in an MRI-derived head model when the coil is placed at 5900 different scalp positions and 360 coil orientations per position (over 2.1 million unique configurations) can be determined in under 15 min on a standard laptop computer. This enables rapid extraction of the optimum coil position and orientation as well as the E-field variation resulting from coil positioning uncertainty. ADM is implemented in SimNIBS 3.2.ConclusionADM enables the rapid determination of coil placement that maximizes E-field delivery to a specific brain target. This method can find the optimum coil placement in under 15 min enabling its routine use for TMS. Furthermore, it enables the fast quantification of uncertainty in the induced E-field due to limited precision of TMS coil placement protocols, enabling minimization and statistical analysis of the E-field dose variability.

Dataset Information

Parametric optimization of electric field strength for cancer electrochemotherapy on a chip-based model.

Publications

Parametric optimization of electric field strength for cancer electrochemotherapy on a chip-based model.

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