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https://www.selleckchem.com/products/ldk378.html Spiking activity was recorded from 81% of the microelectrodes approaching the MGB. Histological examination showed localized surgical trauma along the implant. The extent of haemorrhage surrounding the track was measured and found to be significantly reduced with the miniature slim electrodes (541±455 µm vs. 827±647 µm; P less then 0.001). Scoring of the trauma, focusing on tissue disruption, haemorrhage, oedema of glial parenchyma and pyknosis, revealed a significantly lower trauma score for the slim electrodes (P less then 0.0001). SIGNIFICANCE The slim electrodes reduced the extent of acute trauma, while still providing adequate electrode impedance for both stimulating and recording, and providing the option to target stimulate smaller volumes of tissue. The incorporation of microelectrodes into the electrode array may allow for a simplified, single-step surgical approach where confirmatory micro-targeting is done with the same lead used for permanent implantation. © 2020 IOP Publishing Ltd.This work proposes to use Artificial Neural Networks (ANN) for the regression of dosimetric quantities employed in mammography. The data were generated by Monte Carlo simulations using a modified and validated version of PENELOPE (v. 2014) + penEasy (v. 2015) code. A breast model of homogeneous mixture of adipose and glandular tissue was adopted. The ANN were constructed with Keras and scikit-learn libraries for Mean Glandular Dose (MGD) and Air Kerma (Kair) regressions, respectively. In total, seven parameters were considered, including the incident photon energies (from 8.25 to 48.75 keV), the breast geometry, breast glandularity and Kair acquisition geometry. Two ensembles of 5 ANN networks each were formed to calculate MGD and Kair. The Normalized Glandular Dose coefficients (DgN) are calculated by the ratio of the ensembles outputs for MGD and Air Kerma. Polyenergetic DgN values were calculated weighting monoenergetic value

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