Impedance-based sensors discriminate among different types of blood thrombi with very high specificity and sensitivity.

BACKGROUND: Intracranial occlusion recanalization fails in 20% of endovascular thrombectomy procedures, and thrombus composition is likely to be an important factor. In this study, we demonstrate that the combination of electrical impedance spectroscopy (EIS) and machine learning constitutes a novel and highly accurate method for the identification of different human thrombus types. METHODS: 134 samples, subdivided into four categories, were analyzed by EIS: 29 'White', 26 'Mixed', 12 'Red' thrombi, and 67 liquid 'Blood' samples. Thrombi were generated in vitro using citrated human blood from five healthy volunteers. Histological analysis was performed to validate the thrombus categorization based on red blood cell content. A machine learning prediction model was trained on impedance data to differentiate blood samples from any type of thrombus and in between the four sample categories. RESULTS: Histological analysis confirmed the similarity between the composition of in vitro generated thrombi and retrieved human thrombi. The prediction model yielded a sensitivity/specificity of 90%/99% for distinguishing blood samples from thrombi and a global accuracy of 88% for differentiating among the four sample categories. CONCLUSIONS: Combining EIS measurements with machine learning provides a highly effective approach for discriminating among different thrombus types and liquid blood. These findings raise the possibility of developing a probe-like device (eg, a neurovascular guidewire) integrating an impedance-based sensor. This sensor, placed in the distal part of the smart device, would allow the characterization of the probed thrombus on contact. The information could help physicians identify optimal thrombectomy strategies to improve outcomes for stroke patients.

Monitoring the evolution of boron doped porous diamond electrode on flexible retinal implant by OCT and in vivo impedance spectroscopy.

Nanocrystalline Boron doped Diamond proved to be a very attractive material for neural interfacing, especially with the retina, where reduce glia growth is observed with respect to other materials, thus facilitating neuro-stimulation over long terms. In the present study, we integrated diamond microelectrodes on a polyimide substrate and investigated their performances for the development of neural prosthesis. A full description of the microfabrication of the implants is provided and their functionalities are assessed using cyclic voltammetry and electrochemical impedance spectroscopy. A porous structure of the electrode surface was thus revealed and showed promising properties for neural recording or stimulation. Using the flexible implant, we showed that is possible to follow in vivo the evolution of the electric contact between the diamond electrodes and the retina over 4months by using electrochemical impedance spectroscopy. The position of the implant was also monitored by optical coherence tomography to corroborate the information given by the impedance measurements. The results suggest that diamond microelectrodes are very good candidates for retinal prosthesis.

3D-nanostructured boron-doped diamond for microelectrode array neural interfacing.

The electrode material is a key element in the design of long-term neural implants and neuroprostheses. To date, the ideal electrode material offering high longevity, biocompatibility, low-noise recording and high stimulation capabilities remains to be found. We show that 3D-nanostructured boron doped diamond (BDD), an innovative material consisting in a chemically stable material with a high aspect ratio structure obtained by encapsulation of a carbon nanotube template within two BDD nanolayers, allows neural cell attachment, survival and neurite extension. Further, we developed arrays of 20-µm-diameter 3D-nanostructured BDD microelectrodes for neural interfacing. These microelectrodes exhibited low impedances and low intrinsic recording noise levels. In particular, they allowed the detection of low amplitude (10-20 µV) local-field potentials, single units and multiunit bursts neural activity in both acute whole embryonic hindbrain-spinal cord preparations and long-term hippocampal cell cultures. Also, cyclic voltammetry measurements showed a wide potential window of about 3 V and a charge storage capacity of 10 mC.cm(-2), showing high potentiality of this material for neural stimulation. These results demonstrate the attractiveness of 3D-nanostructured BDD as a novel material for neural interfacing, with potential applications for the design of biocompatible neural implants for the exploration and rehabilitation of the nervous system.