Biomimetic computer-to-brain communication enhancing naturalistic touch sensations via peripheral nerve stimulation.

Artificial communication with the brain through peripheral nerve stimulation shows promising results in individuals with sensorimotor deficits. However, these efforts lack an intuitive and natural sensory experience. In this study, we design and test a biomimetic neurostimulation framework inspired by nature, capable of "writing" physiologically plausible information back into the peripheral nervous system. Starting from an in-silico model of mechanoreceptors, we develop biomimetic stimulation policies. We then experimentally assess them alongside mechanical touch and common linear neuromodulations. Neural responses resulting from biomimetic neuromodulation are consistently transmitted towards dorsal root ganglion and spinal cord of cats, and their spatio-temporal neural dynamics resemble those naturally induced. We implement these paradigms within the bionic device and test it with patients (ClinicalTrials.gov identifier NCT03350061). He we report that biomimetic neurostimulation improves mobility (primary outcome) and reduces mental effort (secondary outcome) compared to traditional approaches. The outcomes of this neuroscience-driven technology, inspired by the human body, may serve as a model for advancing assistive neurotechnologies.

Motion grading of high-resolution quantitative computed tomography supported by deep convolutional neural networks.

Image quality degradation due to subject motion confounds the precision and reproducibility of measurements of bone density, morphology and mechanical properties from high-resolution peripheral quantitative computed tomography (HR-pQCT). Time-consuming operator-based scoring of motion artefacts remains the gold standard to determine the degree of acceptable motion. However, due to the subjectiveness of manual grading, HR-pQCT scans of poor quality, which cannot be used for analysis, may be accepted upon initial review, leaving patients with incomplete or inaccurate imaging results. Convolutional Neural Networks (CNNs) enable fast image analysis with relatively few pre-processing requirements in an operator-independent and fully automated way for image classification tasks. This study aimed to develop a CNN that can predict motion scores from HR-pQCT images, while also being aware of uncertain predictions that require manual confirmation. The CNN calculated motion scores within seconds and achieved a high F1-score (86.8 ± 2.8 %), with good precision (87.5 ± 2.7 %), recall (86.7 ± 2.9 %) and a substantial agreement with the ground truth measured by Cohen's kappa (κ = 68.6 ± 6.2 %); motion scores of the test dataset were predicted by the algorithm with comparable accuracy, precision, sensitivity and agreement as by the operators (p > 0.05). This post-processing approach may be used to assess the effect of motion scores on microstructural analysis and can be immediately implemented into clinical protocols, significantly reducing the time for quality assessment and control of HR-pQCT scans.

Aptamer Conformational Change Enables Serotonin Biosensing with Nanopipettes.

We report artificial nanopores in the form of quartz nanopipettes with ca. 10 nm orifices functionalized with molecular recognition elements termed aptamers that reversibly recognize serotonin with high specificity and selectivity. Nanoscale confinement of ion fluxes, analyte-specific aptamer conformational changes, and related surface charge variations enable serotonin sensing. We demonstrate detection of physiologically relevant serotonin amounts in complex environments such as neurobasal media, in which neurons are cultured in vitro. In addition to sensing in physiologically relevant matrices with high sensitivity (picomolar detection limits), we interrogate the detection mechanism via complementary techniques such as quartz crystal microbalance with dissipation monitoring and electrochemical impedance spectroscopy. Moreover, we provide a novel theoretical model for structure-switching aptamer-modified nanopipette systems that supports experimental findings. Validation of specific and selective small-molecule detection, in parallel with mechanistic investigations, demonstrates the potential of conformationally changing aptamer-modified nanopipettes as rapid, label-free, and translatable nanotools for diverse biological systems.