Approximately 75% of stroke survivors have movement dysfunction. Rehabilitation exercises are capable of improving physical coordination. They are mostly conducted in the home environment without guidance from therapists. It is impossible to provide timely feedback on exercises without suitable devices or therapists. Human action quality assessment in the home setting is a challenging topic for current research. In this paper, a low-cost HREA system in which wearable sensors are used to collect upper limb exercise data and a multichannel 1D-CNN framework is used to automatically assess action quality. The proposed 1D-CNN model is first pretrained on the UCI-HAR dataset, and it achieves a performance of 91.96%. Then, five typical actions were selected from the Fugl-Meyer Assessment Scale for the experiment, wearable sensors were used to collect the participants\' exercise data, and experienced therapists were employed to assess participants\' exercise at the same time. Following the above process, a dataset was built based on the Fugl-Meyer scale. Based on the 1D-CNN model, a multichannel 1D-CNN model was built, and the model using the Naive Bayes fusion had the best performance (precision: 97.26%, recall: 97.22%, F1-score: 97.23%) on the dataset. This shows that the HREA system provides accurate and timely assessment, which can provide real-time feedback for stroke survivors\' home rehabilitation.

In the pursuit of advancing neural tissue regeneration, biomaterial scaffolds have emerged as promising candidates, offering potential solutions for nerve disruptions. Among these scaffolds, multichannel hydrogels, characterized by meticulously designed micrometer-scale channels, stand out as instrumental tools for guiding axonal growth and facilitating cellular interactions. This study explores the innovative application of human amniotic membranes modified with methacryloyl domains (AMMA) in neural stem cell (NSC) culture. AMMA hydrogels, possessing a tailored softness resembling the physiological environment, are prepared in the format of multichannel scaffolds to simulate native-like microarchitecture of nerve tracts. Preliminary experiments on AMMA hydrogel films showcase their potential for neural applications, demonstrating robust adhesion, proliferation, and differentiation of NSCs without the need for additional coatings. Transitioning into the 3D realm, the multichannel architecture fosters intricate neuronal networks guiding neurite extension longitudinally. Furthermore, the presence of synaptic vesicles within the cellular arrays suggests the establishment of functional synaptic connections, underscoring the physiological relevance of the developed neuronal networks. This work contributes to the ongoing efforts to find ethical, clinically translatable, and functionally relevant approaches for regenerative neuroscience.

Respiratory sensing provides a simple, non-invasive, and efficient way for medical diagnosis and health monitoring, but it relies on sensors that are conformal, accurate, durable, and sustainable working. Here, a stretchable, multichannel respiratory sensor inspired by the structure of shark gill cleft is reported. The bionic shark gill structure can convert transverse elastic deformation into longitudinal elastic deformation during stretching. Combining the optimized bionic shark gill structure with the piezoelectric and the triboelectric effect, the bionic shark gill respiratory sensor (BSG-RS) can produce a graded electrical response to different tensile strains. Based on this feature, BSG-RS can simultaneously monitor the breathing rate and breathing depth of the human body accurately, and realize the effective recognition of the different human body\'s breathing state under the supporting software. With good stretchability, wearability, accuracy, and long-term stability (50,000 cycles), BSG-RS is expected to be applied as self-powered smart wearables for mobile medical diagnostic analysis in the future.

Deep learning presents a generalizable solution for motion correction requiring no pulse sequence modifications or additional hardware, but previous networks have all been applied to coil-combined data. Multichannel MRI data provide a degree of spatial encoding that may be useful for motion correction. We hypothesize that incorporating deep learning for motion correction prior to coil combination will improve results. A conditional generative adversarial network was trained using simulated rigid motion artifacts in brain images acquired at multiple sites with multiple contrasts (not limited to healthy subjects). We compared the performance of deep-learning-based motion correction on individual channel images (single-channel model) with that performed after coil combination (channel-combined model). We also investigate simultaneous motion correction of all channel data from an image volume (multichannel model). The single-channel model significantly (p < 0.0001) improved mean absolute error, with an average 50.9% improvement compared with the uncorrected images. This was significantly (p < 0.0001) better than the 36.3% improvement achieved by the channel-combined model (conventional approach). The multichannel model provided no significant improvement in quantitative measures of image quality compared with the uncorrected images. Results were independent of the presence of pathology, and generalizable to a new center unseen during training. Performing motion correction on single-channel images prior to coil combination provided an improvement in performance compared with conventional deep-learning-based motion correction. Improved deep learning methods for retrospective correction of motion-affected MR images could reduce the need for repeat scans if applied in a clinical setting.

Despite the advances in tissue engineering approaches, reconstruction of long segmental peripheral nerve defects remains unsatisfactory. Although autologous grafts with proper fascicular complementation have shown meaningful functional recovery according to the Medical Research Council Classification (MRCC), the lack of donor nerve for such larger defect sizes (>30 mm) has been a serious clinical issue. Further clinical use of hollow nerve conduits is limited to bridging smaller segmental defects of denuded nerve ends (<30 mm). Recently, bioinspired multichannel nerve guidance conduits (NGCs) gained attention as autograft substitutes as they mimic the fascicular connective tissue microarchitecture in promoting aligned axonal outgrowth with desirable innervation for complete sensory and motor function restoration. This review outlines the hierarchical organization of nerve bundles and their significance in the sensory and motor functions of peripheral nerves. This review also emphasizes the major challenges in addressing the longer nerve defects with the role of fascicular arrangement in the multichannel nerve guidance conduits and the need for fascicular matching to accomplish complete functional restoration, especially in treating long segmental nerve defects. Further, currently available fabrication strategies in developing multichannel nerve conduits and their inconsistency in existing preclinical outcomes captured in this review would seed a new process in designing an ideal larger nerve conduit for peripheral nerve repair.

The internet of things (IoT) revolutionized human life, whereby a large number of interrelated devices are connected to exchange data in order to accomplish many tasks, leading to the rapid growth of connected devices, reaching the tens of billions. The Low Power Wide Area (LPWA) protocols paradigm has emerged to satisfy the IoT application requirements, especially in terms of long-range communication and low power consumption. However, LPWA technologies still do not completely meet the scalability requirement of IoT applications. The main critical issues are the restrictive duty cycle regulations of the sub-GHz band in which most LPWA technologies operate, as well as the random access to the medium. Ingenu Random Phase Multiple Access (RPMA) is an LPWA technology that uses the 2.4 GHz band that is not subject to the duty cycle constraint. Furthermore, RPMA uses Direct-Sequence Spread Spectrum (DSSS) as a modulation technique; hence, it is an excellent candidate technology for handling scalable LPWA networks. In this paper, we perform mathematical and simulation analysis to assess RPMA scalability and the factors that affect it, especially when all the available channels are used. The results indicate that RPMA has impressive scalability. Indeed, by taking advantage of the multichannel feature in RPMA, the network capacity can be increased by up to 38 times. Aditionally, randomly selecting the Spreading Factors (SF) degrades the network scalability, as working on higher SFs will increase the probability of collision. Thus, we proposed an SF distribution algorithm that ensures effective packet delivery with minimum collision.

Free-floating electrochemical sensors are promising for in situ bioprocess monitoring with the advantages of movability, a lowered risk of contamination, and a simplified structure of the bioreactor. Although floating sensors were developed for the measurement of physical and chemical indicators such as temperature, velocity of flow, pH, and dissolved oxygen, it is the lack of available electrochemical sensors for the determination of the inorganic ions in bioreactors that has a significant influence on cell culture. In this study, a capsule-shaped electrochemical system (iCapsuleEC) is developed to monitor ions including K+, NH4+, Na+, Ca2+, and Mg2+ based on solid-contact ion-selective electrodes (SC-ISEs). It consists of a disposable electrochemical sensor and signal-processing device with features including multichannel measurement, self-calibration, and wireless data transmission. The capacities of the iCapsuleEC were demonstrated not only for in situ measurement of ion concentrations but also for the optimization of the sensing electrodes. We also explored the possibility of the system for use in detection in simulated cell culture media.

An accurate rule for predicting conductance is the cornerstone of developing molecular circuits and provides a promising solution for miniaturizing electric circuits. The successful prediction of series molecular circuits has proven the possibility of establishing a rule for molecular circuits under quantum mechanics. However, the quantitatively accurate prediction has not been validated by experiments for parallel molecular circuits. Here we used 1,3-dihydrobenzothiophene (DBT) to build the parallel molecular circuits. The theoretical simulation and single-molecule conductance measurements demonstrated that the conductance of the molecule containing one DBT is the unprecedented linear combination of the conductance of the two individual channels with respective contribution weights of 0.37 and 0.63. With these weights, the conductance of the molecule containing two DBTs is predicted as 1.81 nS, matching perfectly with the measured conductance (1.82 nS). This feature offers a potential rule for quantitatively predicting the conductance of parallel molecular circuits.

Multichannel quantum emission is in high demand for advanced quantum photonic applications such as quantum communications, quantum computing, and quantum cryptography. However, to date, the most common way for shaping photon emission from quantum emitters (QEs) is to utilize free-standing (external) bulky optical components. Here, we develop the multichannel holography approach for flexibly designing on-chip QE-coupled metasurfaces that make use of nonradiatively QE-excited surface plasmon polaritons for generating far-field quantum emission, which propagates in designed directions carrying specific spin and orbital angular momenta (SAM and OAM, respectively). We further design, fabricate, and characterize on-chip quantum light sources of multichannel quantum emission encoded with different SAMs and OAMs. The holography-based inverse design approach developed and demonstrated on-chip quantum light sources with multiple degrees of freedoms, thereby enabling a powerful platform for quantum nanophotonics, especially relevant for advanced quantum photonic applications, e.g., high-dimensional quantum information processing.

Peripheral nerve regeneration after injury is still a clinical problem. The application of autologous nerve grafting, the gold standard treatment, is greatly restricted. Acellular nerve allografts (ANAs) are considered promising alternatives, but they are difficult to achieve satisfactory therapeutic outcomes, which may be attributed to their compact inherent ultrastructure and substantial loss of extracellular matrix (ECM) components. Regarding these deficiencies, this study developed an optimized multichannel ANA by a modified decellularization method. These innovative ANAs were demonstrated to retain more ECM bioactive molecules and regenerative factors, with effective elimination of cellular antigens. The presence of microchannels with larger pore size allowed ANAs to gain higher porosity and better swelling performance, which improves their internal ultrastructure. Their mechanical properties were more similar to those of native nerves. Moreover, the optimized ANAs exhibited good biocompatibility and possessed significant advantages in supporting the proliferation and migration of Schwann cells in vitro. The in vivo results further confirmed their superior capacity to promote axon regrowth and myelination as well as restore innervation of target muscles, leading to better functional recovery than the conventional ANAs. Overall, this study demonstrates that the optimized multichannel ANAs have great potential for clinical application and offer new insight into the further improvement of ANAs.