First in vivo results of an innovative sensor to detect stent-related complications

International audienc

First in vivo results of an innovative sensor to detect stent-related complications

International audienc

Optofluidic lens with tunable focal length and asphericity

Adaptive micro-lenses enable the design of very compact optical systems with tunable imaging properties. Conventional adaptive micro-lenses suffer from substantial spherical aberration that compromises the optical performance of the system. Here, we introduce a novel concept of liquid micro-lenses with superior imaging performance that allows for simultaneous and independent tuning of both focal length and asphericity. This is achieved by varying both hydrostatic pressures and electric fields to control the shape of the refracting interface between an electrically conductive lens fluid and a non-conductive ambient fluid. Continuous variation from spherical interfaces at zero electric field to hyperbolic ones with variable ellipticity for finite fields gives access to lenses with positive, zero, and negative spherical aberration (while the focal length can be tuned via the hydrostatic pressure)

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

International audienceBackground 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