Thermomagnetic Resonance Effect of the Extremely Low Frequency Electromagnetic Field on Three-Dimensional Cancer Models

In our recent studies, we have developed a thermodynamic biochemical model able to select the resonant frequency of an extremely low frequency electromagnetic field (ELF-EMF) specifically affecting different types of cancer, and we have demonstrated its effects in vitro. In this work, we investigate the cellular response to the ELF electromagnetic wave in three-dimensional (3D) culture models, which mimic the features of tumors in vivo. Cell membrane was modelled as a resistor-capacitor circuit and the specific thermal resonant frequency was calculated and tested on two-dimensional (2D) and three-dimensional (3D) cell cultures of human pancreatic cancer, glioblastoma and breast cancer. Cell proliferation and the transcription of respiratory chain and adenosine triphosphate synthase subunits, as well as uncoupling proteins, were assessed. For the first time, we demonstrate that an ELF-EMF hampers growth and potentiates both the coupled and uncoupled respiration of all analyzed models. Interestingly, the metabolic shift was evident even in the 3D aggregates, making this approach particularly valuable and promising for future application in vivo, in aggressive cancer tissues characterized by resistance to treatments

Microneurography as a tool to develop decoding algorithms for peripheral neuro-controlled hand prostheses

The usability of dexterous hand prostheses is still hampered by the lack of natural and effective control strategies. A decoding strategy based on the processing of descending efferent neural signals recorded using peripheral neural interfaces could be a solution to such limitation. Unfortunately, this choice is still restrained by the reduced knowledge of the dynamics of human efferent signals recorded from the nerves and associated to hand movements.Findings: To address this issue, in this work we acquired neural efferent activities from healthy subjects performing hand- related tasks using ultrasound-guided microneurography, a minimally invasive technique, which employs needles, inserted percutaneously, to record from nerve fibers. These signals allowed us to identify neural features correlated with force and velocity of finger movements that were used to decode motor intentions. We developed computational models, which confirmed the potential translatability of these results showing how these neural features hold in absence of feedback and when implantable intrafascicular recording, rather than microneurography, is performed.Conclusions: Our results are a proof of principle that microneurography could be used as a useful tool to assist the development of more effective hand prostheses

Whole-wafer fabrication process for three-terminal double stacked tunnel junctions

A new fabrication process for three-terminal superconducting devices consisting of two Josephson junctions in a stacked configuration is reported. The process is based on the deposition of the whole Nb/AlxOy/Nb-Al/AlxOy/Nb multilayer on a Si crystalline wafer without any vacuum breaking. Lift-off techniques, anodization processes and a SiO film deposition have been adopted for patterning and insulating the two tunnel stacked junctions. Devices have been characterized in terms of current-voltage (I-V) curves and Josephson critical current vs. the externally applied magnetic field. They show high quality factors (Vm values up to 65 mV at 4.2 K), and good current uniformity

Evaluation of supervised methods for the classification of major tissues and subcortical structures in multispectral brain magnetic resonance images

This work investigates the capability of supervised classification methods in detecting both major tissues and subcortical structures using multispectral brain magnetic resonance images. First, by means of a realistic digital brain phantom, we investigated the classification performance of various Discriminant Analysis methods, K-Nearest Neighbor and Support Vector Machine. Then, using phantom and real data, we quantitatively assessed the benefits of integrating anatomical information in the classification, in the form of voxels coordinates as additional features to the intensities or tissue probabilistic atlases as priors. In addition we tested the effect of spatial correlations between neighboring voxels and image denoising. For each brain tissue we measured the classification performance in terms of global agreement percentage, false positive and false negative rates and kappa coefficient. The effectiveness of integrating spatial information or a tissue probabilistic atlas has been demonstrated for the aim of accurately classifying brain magnetic resonance images