A Multi-DoF Prosthetic Hand Finger Joint Controller for Wearable sEMG Sensors by Nonlinear Autoregressive Exogenous Model.

The loss of mobility function and sensory information from the arm, hand, and fingertips hampers the activities of daily living (ADL) of patients. A modern bionic prosthetic hand can compensate for the lost functions and realize multiple degree of freedom (DoF) movements. However, the commercially available prosthetic hands usually have limited DoFs due to limited sensors and lack of stable classification algorithms. This study aimed to propose a controller for finger joint angle estimation by surface electromyography (sEMG). The sEMG data used for training were gathered with the Myo armband, which is a commercial EMG sensor. Two features in the time domain were extracted and fed into a nonlinear autoregressive model with exogenous inputs (NARX). The NARX model was trained with pre-selected parameters using the Levenberg-Marquardt algorithm. Comparing with the targets, the regression correlation coefficient (R) of the model outputs was more than 0.982 over all test subjects, and the mean square error was less than 10.02 for a signal range in arbitrary units equal to [0, 255]. The study also demonstrated that the proposed model could be used in daily life movements with good accuracy and generalization abilities.

Multichannel Synchronous Hydrodynamic Gating Coupling with Concentration Gradient Generator for High-Throughput Probing Dynamic Signaling of Single Cells.

Cell signaling greatly affected by complicated and temporally dynamic extracellular microenvironments controls most of the physiological functions in vivo. To reconstruct or simulate such microenvironments in vitro represents a fundamental approach for revealing the underlying mechanisms of those sophisticated processes. Recent advances in microfluidics have added a new dimension to cell signaling analysis, for example, concentration gradient generators (amplitude aspect) or hydrodynamic gating strategy (frequency aspect), but it is still challengeable to capture single-cell dynamic signaling in response to a mimicked extracellular microenvironment with varied stimuli waveforms of different amplitude and frequency in a high-throughput manner. In this article, we proposed a novel microfluidic strategy coupling multichannel synchronous hydrodynamic gating with microfluidic concentration gradient generators (µMHG-CGG) to probe dynamic signaling of single cells with high throughput. The µMHG-CGG allows rapid delivery of dynamic chemical signals in both high frequency (as high as 670 mHz) and multiple amplitude domains at the same time and simultaneously high-throughput probing cell dynamics at single-cell resolution in real time. By applying the proposed system, the mechanisms for encoding/decoding systems (termed "frequency coding" or "amplitude coding") via GPCRs-mediated signaling pathways responding to histamine (HA) and adenosine triphosphate (ATP) in single HeLa cells were investigated. The optimal drug concentrations of single cells responses to HA and ATP individually or in combination were also successfully discussed, allowing us to obtain both single-cell heterogeneity and statistics from the cell population.

An Ultra-Sensitive Modular Hybrid EMG-FMG Sensor with Floating Electrodes.

To improve the reliability and safety of myoelectric prosthetic control, many researchers tend to use multi-modal signals. The combination of electromyography (EMG) and forcemyography (FMG) has been proved to be a practical choice. However, an integrative and compact design of this hybrid sensor is lacking. This paper presents a novel modular EMG-FMG sensor; the sensing module has a novel design that consists of floating electrodes, which act as the sensing probe of both the EMG and FMG. This design improves the integration of the sensor. The whole system contains one data acquisition unit and eight identical sensor modules. Experiments were conducted to evaluate the performance of the sensor system. The results show that the EMG and FMG signals have good consistency under standard conditions; the FMG signal shows a better and more robust performance than the EMG. The average accuracy is 99.07% while using both the EMG and FMG signals for recognition of six hand gestures under standard conditions. Even with two layers of gauze isolated between the sensor and the skin, the average accuracy reaches 90.9% while using only the EMG signal; if we use both the EMG and FMG signals for classification, the average accuracy is 99.42%.

High-throughput and proteome-wide discovery of endogenous biomolecular condensates.

Phase separation inside mammalian cells regulates the formation of the biomolecular condensates that are related to gene expression, signalling, development and disease. However, a large population of endogenous condensates and their candidate phase-separating proteins have yet to be discovered in a quantitative and high-throughput manner. Here we demonstrate that endogenously expressed biomolecular condensates can be identified across a cell's proteome by sorting proteins across varying oligomeric states. We employ volumetric compression to modulate the concentrations of intracellular proteins and the degree of crowdedness, which are physical regulators of cellular biomolecular condensates. The changes in degree of the partition of proteins into condensates or phase separation led to varying oligomeric states of the proteins, which can be detected by coupling density gradient ultracentrifugation and quantitative mass spectrometry. In total, we identified 1,518 endogenous condensate proteins, of which 538 have not been reported before. Furthermore, we demonstrate that our strategy can identify condensate proteins that respond to specific biological processes.

Enhancing single-cell biology through advanced AI-powered microfluidics.

Microfluidic technology has largely benefited both fundamental biological research and translational clinical diagnosis with its advantages in high-throughput, single-cell resolution, high integrity, and wide-accessibility. Despite the merits we obtained from microfluidics in the last two decades, the current requirement of intelligence in biomedicine urges the microfluidic technology to process biological big data more efficiently and intelligently. Thus, the current readout technology based on the direct detection of the signals in either optics or electrics was not able to meet the requirement. The implementation of artificial intelligence (AI) in microfluidic technology matches up with the large-scale data usually obtained in the high-throughput assays of microfluidics. At the same time, AI is able to process the multimodal datasets obtained from versatile microfluidic devices, including images, videos, electric signals, and sequences. Moreover, AI provides the microfluidic technology with the capability to understand and decipher the obtained datasets rather than simply obtaining, which eventually facilitates fundamental and translational research in many areas, including cell type discovery, cell signaling, single-cell genetics, and diagnosis. In this Perspective, we will highlight the recent advances in employing AI for single-cell biology and present an outlook on the future direction with more advanced AI algorithms.

Smartphone-based platforms implementing microfluidic detection with image-based artificial intelligence.

The frequent outbreak of global infectious diseases has prompted the development of rapid and effective diagnostic tools for the early screening of potential patients in point-of-care testing scenarios. With advances in mobile computing power and microfluidic technology, the smartphone-based mobile health platform has drawn significant attention from researchers developing point-of-care testing devices that integrate microfluidic optical detection with artificial intelligence analysis. In this article, we summarize recent progress in these mobile health platforms, including the aspects of microfluidic chips, imaging modalities, supporting components, and the development of software algorithms. We document the application of mobile health platforms in terms of the detection objects, including molecules, viruses, cells, and parasites. Finally, we discuss the prospects for future development of mobile health platforms.

Activation of the primary motor cortex using fully-implanted electrical sciatic nerve stimulation.

Functional degradation of the motor cortex usually results from brain injury, stroke, limb amputation, aging or other diseases. Currently, there are no ideal means of treatment, other than medication and sports rehabilitation. The present study investigated whether electrical stimulation of the sciatic nerve can activate the motor-related area of the brain. The study is based on a self-developed fully implantable nerve electrical stimulator and a self-developed multi-channel electroencephalogram (EEG) electrode array. The sciatic nerves of Sprague-Dawley rats (sorted into old and young groups) were stimulated by the electrical stimulator under anesthesia, and the EEG signal was recorded simultaneously. The relationship between sciatic nerve stimulation and brain activity was analyzed. The results showed that when the sciatic nerve was stimulated by the implanted electrical stimulator, motor-related channels were activated, causing contraction of the left leg. It was found that at the frequency band of 8-16 Hz, the EEG signal in the right motor area was higher than at other frequency bands. This phenomenon was identical in both young and old rats. The results indicated that electrical stimulation of the sciatic nerve can activate the motor region of the rat brain, and provided evidence that stimulation of the sciatic nerve could be a method of preventing motor cortex degeneration.

Bioheat transfer model of transcutaneous spinal cord stimulation-induced temperature changes.

Transcutaneous spinal cord stimulation (tSCS) has been extensively studied due to its promising application in motor function restoration. Many previous studies have explored both the essential mechanism of action and the methods for determining optimal stimulation parameters. In contrast, the bioheat transfer analysis of tSCS therapy has not been investigated to the same extent, despite widely existing, and being of great significance in assuring a stable and thermally safe treatment. In this paper, we concentrated on the thermal effects of tSCS using a finite element-based method. By coupling the electric field and bioheat field, systematic finite element simulations were performed on a human spinal cord model to survey the influence of anatomical structures, blood perfusion, and stimulation parameters on temperature changes for the first time. The results show that tSCS-induced temperature rise mainly occurs in the skin and fat layers and varies due to individual differences. The current density distribution along with the interactions of multiple biothermal effects synthetically determines the thermal status of the whole spinal cord model. Smaller stimulation electrodes have a higher risk of thermal damage when compared with larger electrodes. Increasing the stimulation intensity will result in more joule heat accumulation, hence an increase in the temperature. Among all configurations in this study that simulated the clinical tSCS protocols, the temperature rise could reach up to 9.4 °C on the skin surface depending on the stimulation parameters and tissue blood perfusion.