Direct vagus nerve stimulation: A new tool to control allergic airway inflammation through α7 nicotinic acetylcholine receptor.

BACKGROUND AND PURPOSE: Asthma is characterized by airway inflammation, mucus hypersecretion, and airway hyperresponsiveness. The use of nicotinic agents to mimic the cholinergic anti-inflammatory pathway (CAP) controls experimental asthma. Yet, the effects of vagus nerve stimulation (VNS)-induced CAP on allergic inflammation remain unknown. EXPERIMENTAL APPROACH: BALB/c mice were sensitized and challenged with house dust mite (HDM) extract and treated with active VNS (5 Hz, 0.5 ms, 0.05-1 mA). Bronchoalveolar lavage (BAL) fluid was assessed for total and differential cell counts and cytokine levels. Lungs were examined by histopathology and electron microscopy. KEY RESULTS: In the HDM mouse asthma model, VNS at intensities equal to or above 0.1 mA (VNS 0.1) but not sham VNS reduced BAL fluid differential cell counts and alveolar macrophages expressing α7 nicotinic receptors (α7nAChR), goblet cell hyperplasia, and collagen deposition. Besides, VNS 0.1 also abated HDM-induced elevation of type 2 cytokines IL-4 and IL-5 and was found to block the phosphorylation of transcription factor STAT6 and expression level of IRF4 in total lung lysates. Finally, VNS 0.1 abrogated methacholine-induced hyperresponsiveness in asthma mice. Prior administration of α-bungarotoxin, a specific inhibitor of α7nAChR, but not propranolol, a specific inhibitor of ß2-adrenoceptors, abolished the therapeutic effects of VNS 0.1. CONCLUSION AND IMPLICATIONS: Our data revealed the protective effects of VNS on various clinical features in allergic airway inflammation model. VNS, a clinically approved therapy for depression and epilepsy, appears to be a promising new strategy for controlling allergic asthma.

A universal interface for plug-and-play assembly of stretchable devices.

Stretchable hybrid devices have enabled high-fidelity implantable1-3 and on-skin4-6 monitoring of physiological signals. These devices typically contain soft modules that match the mechanical requirements in humans7,8 and soft robots9,10, rigid modules containing Si-based microelectronics11,12 and protective encapsulation modules13,14. To make such a system mechanically compliant, the interconnects between the modules need to tolerate stress concentration that may limit their stretching and ultimately cause debonding failure15-17. Here, we report a universal interface that can reliably connect soft, rigid and encapsulation modules together to form robust and highly stretchable devices in a plug-and-play manner. The interface, consisting of interpenetrating polymer and metal nanostructures, connects modules by simply pressing without using pastes. Its formation is depicted by a biphasic network growth model. Soft-soft modules joined by this interface achieved 600% and 180% mechanical and electrical stretchability, respectively. Soft and rigid modules can also be electrically connected using the above interface. Encapsulation on soft modules with this interface is strongly adhesive with an interfacial toughness of 0.24 N mm-1. As a proof of concept, we use this interface to assemble stretchable devices for in vivo neuromodulation and on-skin electromyography, with high signal quality and mechanical resistance. We expect such a plug-and-play interface to simplify and accelerate the development of on-skin and implantable stretchable devices.

Assessing Elevated Blood Glucose Levels Through Blood Glucose Evaluation and Monitoring Using Machine Learning and Wearable Photoplethysmography Sensors: Algorithm Development and Validation.

BACKGROUND: Diabetes mellitus is the most challenging and fastest-growing global public health concern. Approximately 10.5% of the global adult population is affected by diabetes, and almost half of them are undiagnosed. The growing at-risk population exacerbates the shortage of health resources, with an estimated 10.6% and 6.2% of adults worldwide having impaired glucose tolerance and impaired fasting glycemia, respectively. All current diabetes screening methods are invasive and opportunistic and must be conducted in a hospital or laboratory by trained professionals. At-risk participants might remain undetected for years and miss the precious time window for early intervention to prevent or delay the onset of diabetes and its complications. OBJECTIVE: We aimed to develop an artificial intelligence solution to recognize elevated blood glucose levels (≥7.8 mmol/L) noninvasively and evaluate diabetic risk based on repeated measurements. METHODS: This study was conducted at KK Women's and Children's Hospital in Singapore, and 500 participants were recruited (mean age 38.73, SD 10.61 years; mean BMI 24.4, SD 5.1 kg/m2). The blood glucose levels for most participants were measured before and after consuming 75 g of sugary drinks using both a conventional glucometer (Accu-Chek Performa) and a wrist-worn wearable. The results obtained from the glucometer were used as ground-truth measurements. We performed extensive feature engineering on photoplethysmography (PPG) sensor data and identified features that were sensitive to glucose changes. These selected features were further analyzed using an explainable artificial intelligence approach to understand their contribution to our predictions. RESULTS: Multiple machine learning models were trained and assessed with 10-fold cross-validation, using participant demographic data and critical features extracted from PPG measurements as predictors. A support vector machine with a radial basis function kernel had the best detection performance, with an average accuracy of 84.7%, a sensitivity of 81.05%, a specificity of 88.3%, a precision of 87.51%, a geometric mean of 84.54%, and F score of 84.03%. CONCLUSIONS: Our findings suggest that PPG measurements can be used to identify participants with elevated blood glucose measurements and assist in the screening of participants for diabetes risk.

Artemisia argyi exhibits anti-aging effects through decreasing the senescence in aging stem cells.

Aging is accompanied by functional loss of many cellular pathways, creating an increased risk of many age-related complications (ARC). Aging causes stem cell exhaustion with a concomitant increase in cellular dysfunction. Recently, interest in senotherapeutics has been growing rapidly to promote healthy aging and as an intervention for ARCs. This research focused on screening the senomorphic properties of Artemisia argyi, as an emerging strategy for longevity, and prevention or treatment of ARCs. In this study, we aimed to find the clinical efficacy of daily consumption of Artemisia argyi water extract (AAW) on aging. In vitro 0.1µM Doxorubicin induced senescent human adipose derived mesenchymal stem cells was treated with different concentrations of AAW to show its anti-aging effect. 15 months old SHR rats (n=6) were treated with 7.9 mg/ml AAW for 4 weeks and anti-aging effect was evaluated. In vitro study showed the protective effect of AAW in telomere shortening and helps in maintaining a balance in the expression of anti-aging protein Klotho and TERT. AAW effectively reduced mitochondrial superoxide and also provided a protective shield against senescence markers like over-expression of p21 and formation of double strand breaks, which is known to cause premature aging. Moreover, animal studies indicated that AAW promoted the expression of Klotho in naturally aging rats. In addition, AAW successfully restored the decline cardiac function and improved the grip strength and memory of aging rat. These findings showed that therapeutic targeting of senescent stem cells by AAW restored stem cell homeostasis and improves overall health.

Tutorial: a guide to techniques for analysing recordings from the peripheral nervous system.

The nervous system, through a combination of conscious and automatic processes, enables the regulation of the body and its interactions with the environment. The peripheral nervous system is an excellent target for technologies that seek to modulate, restore or enhance these abilities as it carries sensory and motor information that most directly relates to a target organ or function. However, many applications require a combination of both an effective peripheral nerve interface (PNI) and effective signal processing techniques to provide selective and stable recordings. While there are many reviews on the design of PNIs, reviews of data analysis techniques and translational considerations are limited. Thus, this tutorial aims to support new and existing researchers in the understanding of the general guiding principles, and introduces a taxonomy for electrode configurations, techniques and translational models to consider.

A nonlinear hidden layer enables actor-critic agents to learn multiple paired association navigation.

Navigation to multiple cued reward locations has been increasingly used to study rodent learning. Though deep reinforcement learning agents have been shown to be able to learn the task, they are not biologically plausible. Biologically plausible classic actor-critic agents have been shown to learn to navigate to single reward locations, but which biologically plausible agents are able to learn multiple cue-reward location tasks has remained unclear. In this computational study, we show versions of classic agents that learn to navigate to a single reward location, and adapt to reward location displacement, but are not able to learn multiple paired association navigation. The limitation is overcome by an agent in which place cell and cue information are first processed by a feedforward nonlinear hidden layer with synapses to the actor and critic subject to temporal difference error-modulated plasticity. Faster learning is obtained when the feedforward layer is replaced by a recurrent reservoir network.

Detection of Freezing of Gait Using Convolutional Neural Networks and Data From Lower Limb Motion Sensors.

Parkinson's disease (PD) is a chronic, non-reversible neurodegenerative disorder, and freezing of gait (FOG) is one of the most disabling symptoms in PD as it is often the leading cause of falls and injuries that drastically reduces patients' quality of life. In order to monitor continuously and objectively PD patients who suffer from FOG and enable the possibility of on-demand cueing assistance, a sensor-based FOG detection solution can help clinicians manage the disease and help patients overcome freezing episodes. Many recent studies have leveraged deep learning models to detect FOG using signals extracted from inertial measurement unit (IMU) devices. Usually, the latent features and patterns of FOG are discovered from either the time or frequency domain. In this study, we investigated the use of the time-frequency domain by applying the Continuous Wavelet Transform to signals from IMUs placed on the lower limbs of 63 PD patients who suffered from FOG. We built convolutional neural networks to detect the FOG occurrences, and employed the Bayesian Optimisation approach to obtain the hyper-parameters. The results showed that the proposed subject-independent model was able to achieve a geometric mean of 90.7% and a F1 score of 91.5%.

STEER: 3D Printed Guide for Nerve Regrowth Control and Neural Interface in Non-Human Primate Model.

OBJECTIVE: Peripheral neural interface (PNI) with a stable integration of synthetic elements with neural tissue is key for successfulneuro-prosthetic applications. An inevitable phenomenon of reactive fibrosis is a primary hurdle for long term functionality of PNIs. This proof-of-concept study aimed to fabricate and test a novel, stable PNI that harnesses fibro-axonal outgrowth at the nerve end and includes fibrosis in the design. METHODS: Two non-human primates were implanted with Substrate-guided, Tissue-Electrode Encapsulation and Integration (STEER) PNIs. The implant included a 3D printed guide that strove to steer the regrowing nerve towards encapsulation of the electrodes into a fibro-axonal tissue. After four months from implantation, we performed electrophysiological measurements to test STEER's functionality and examined the macro and micro- morphology of the outgrowth tissue. RESULTS: We observed a highly structured fibro-axonal composite within the STEER PNI. A conduction of intracranially generated action potentials was successfully recorded across the neural interface. Immunohistology demonstrated uniquely configured laminae of myelinated axons encasing the implant. CONCLUSION: STEER PNI reconfigured the structure of the fibro-axonal tissue and facilitated long-term functionality and stability of the neural interface. SIGNIFICANCE: The results point to the feasibility of our concept for creating a stable PNI with long-term electrophysiologic functionality by using simple design and materials.

Convolutional Neural Network for Freezing of Gait Detection Leveraging the Continuous Wavelet Transform on Lower Extremities Wearable Sensors Data.

Freezing of Gait is the most disabling gait disturbance in Parkinson's disease. For the past decade, there has been a growing interest in applying machine learning and deep learning models to wearable sensor data to detect Freezing of Gait episodes. In our study, we recruited sixty-seven Parkinson's disease patients who have been suffering from Freezing of Gait, and conducted two clinical assessments while the patients wore two wireless Inertial Measurement Units on their ankles. We converted the recorded time-series sensor data into continuous wavelet transform scalograms and trained a Convolutional Neural Network to detect the freezing episodes. The proposed model achieved a generalisation accuracy of 89.2% and a geometric mean of 88.8%.

Minimally dependent activity subspaces for working memory and motor preparation in the lateral prefrontal cortex.

The lateral prefrontal cortex is involved in the integration of multiple types of information, including working memory and motor preparation. However, it is not known how downstream regions can extract one type of information without interference from the others present in the network. Here, we show that the lateral prefrontal cortex of non-human primates contains two minimally dependent low-dimensional subspaces: one that encodes working memory information, and another that encodes motor preparation information. These subspaces capture all the information about the target in the delay periods, and the information in both subspaces is reduced in error trials. A single population of neurons with mixed selectivity forms both subspaces, but the information is kept largely independent from each other. A bump attractor model with divisive normalization replicates the properties of the neural data. These results provide new insights into neural processing in prefrontal regions.

A 3-Mbps, 802.11g-Based EMG Recording System With Fully Implantable 5-Electrode EMG Acquisition Device.

We have developed a 5-electrode recording system that combines an implantable electromyography (EMG) device package with transcutaneous inductive power transmission, near-infrared (NIR) transcutaneous data telemetry and 3 Mbps Wi-Fi data acquisition for chronic EMG recording in vivo. This system comprises a hermetically-sealed single-chip, 5-electrode Implantable EMG Acquisition Device (IEAD), a custom external powering and Implant Telemetry Module (ITM), and a custom Wi-Fi-based Raspberry Pi-based Data Acquisition (RaspDAQ) and relay device. The external unit (ITM and RaspDAQ) is powered entirely by a single battery to achieve the objective of untethered EMG recording, for the convenience of clinicians and animal researchers. The IEAD acquires intramuscular EMG signals at 17.85 ksps/electrode while being powered transcutaneously by the ITM using 22 MHz near-field inductive coupling. The acquired EMG data is transmitted transcutaneously via NIR telemetry to the ITM, which in turn, transfers the data to the RaspDAQ for relaying to a laptop computer for display and storage. We have also validated the complete system by acquiring EMG signals from rodents for up to two months. Following the explantation of the devices, we have also conducted failure and histological analysis on the devices and the surrounding tissue, respectively.

Nanotunnels within Poly(3,4-ethylenedioxythiophene)-Carbon Nanotube Composite for Highly Sensitive Neural Interfacing.

Neural electrodes are developed for direct communication with neural tissues for theranostics. Although various strategies have been employed to improve performance, creating an intimate electrode-tissue interface with high electrical fidelity remains a great challenge. Here, we report the rational design of a tunnel-like electrode coating comprising poly(3,4-ethylenedioxythiophene) (PEDOT) and carbon nanotubes (CNTs) for highly sensitive neural recording. The coated electrode shows a 50-fold reduction in electrochemical impedance at the biologically relevant frequency of 1 kHz, compared to the bare gold electrode. The incorporation of CNT significantly reinforces the nanotunnel structure and improves coating adhesion by ∼1.5 fold. In vitro primary neuron culture confirms an intimate contact between neurons and the PEDOT-CNT nanotunnel. During acute in vivo nerve recording, the coated electrode enables the capture of high-fidelity neural signals with low susceptibility to electrical noise and reveals the potential for precisely decoding sensory information through mechanical and thermal stimulation. These findings indicate that the PEDOT-CNT nanotunnel composite serves as an active interfacing material for neural electrodes, contributing to neural prosthesis and brain-machine interface.

Time-invariant working memory representations in the presence of code-morphing in the lateral prefrontal cortex.

Maintenance of working memory is thought to involve the activity of prefrontal neuronal populations with strong recurrent connections. However, it was recently shown that distractors evoke a morphing of the prefrontal population code, even when memories are maintained throughout the delay. How can a morphing code maintain time-invariant memory information? We hypothesized that dynamic prefrontal activity contains time-invariant memory information within a subspace of neural activity. Using an optimization algorithm, we found a low-dimensional subspace that contains time-invariant memory information. This information was reduced in trials where the animals made errors in the task, and was also found in periods of the trial not used to find the subspace. A bump attractor model replicated these properties, and provided predictions that were confirmed in the neural data. Our results suggest that the high-dimensional responses of prefrontal cortex contain subspaces where different types of information can be simultaneously encoded with minimal interference.

A Wearable, Patient-Adaptive Freezing of Gait Detection System for Biofeedback Cueing in Parkinson's Disease.

Freezing of Gait (FoG) is a common motor-related impairment among Parkinson's disease patients, which substantially reduces their quality of life and puts them at risk of falls. These patients benefit from wearable FoG detection systems that provide timely biofeedback cues and hence help them regain control over their gait. Unfortunately, the systems proposed thus far are bulky and obtrusive when worn. The objective of this paper is to demonstrate the first integration of an FoG detection system into a single sensor node. To achieve such an integration, features with low computational load are selected and dedicated hardware is designed that limits area and memory utilization. Classification is achieved with a neural network that is capable of learning in real time and thus allows the system to adapt to a patient during run-time. A small form factor FPGA implements the feature extraction and classification, whereas a custom PCB integrates the system into a single node. The system fits into a 4.5 × 3.5 × 1.5 cm 3 housing case, weighs 32 g, and achieves 95.6% sensitivity and 90.2% specificity when adapted to a patient. Biofeedback cues are provided either through auditory or somatosensory means and the system can remain operational for longer than 9 h while providing cues. The proposed system is highly competitive in terms of classification performance and excels with respect to wearability and real-time patient adaptivity.

Closed-loop stimulation of the pelvic nerve for optimal micturition.

OBJECTIVE: Neural stimulation to restore bladder function has traditionally relied on open-loop approaches that used pre-set parameters, which do not adapt to suboptimal outcomes. The goal of this study was to examine the effectiveness of a novel closed-loop stimulation paradigm for improving micturition or bladder voiding. APPROACH: We compared the voiding efficiency obtained with this closed-loop framework against open-loop stimulation paradigms in anesthetized rats. The bladder pressures that preceded voiding, and the minimum current amplitudes for stimulating the pelvic nerves to evoke bladder contractions, were first calibrated for each animal. An automated closed-loop system was used to initiate voiding upon bladder fullness, adapt the stimulation current by using real-time bladder pressure changes to classify voiding outcomes, and halt stimulation when the bladder had been emptied or when the safe stimulation limit was reached. MAIN RESULTS: In vivo testing demonstrated that the closed-loop system achieved high voiding efficiency or VE (75.7% ± 3.07%, mean ± standard error of the mean) and outperformed open-loop systems with either conserved number of stimulation epochs (63.2% ± 4.90% VE) or conserved charge injected (32.0% ± 1.70% VE). Post-hoc analyses suggest that the classification algorithm can be further improved with data from additional closed-loop experiments. SIGNIFICANCE: This novel approach may be applied to an implantable device for treating underactive bladder (<60% VE), especially in cases where under- or over-stimulation of the nerve is a concern.

Regression analysis of gait parameters and mobility measures in a healthy cohort for subject-specific normative values.

BACKGROUND: Deviation in gait performance from normative data of healthy cohorts is used to quantify gait ability. However, normative data is influenced by anthropometry and such differences among subjects impede accurate assessment. De-correlation of anthropometry from gait parameters and mobility measures is therefore desirable. METHODS: 87 (42 male) healthy subjects varying form 21 to 84 years of age were assessed on gait parameters (cadence, ankle velocity, stride time, stride length) and mobility measures (the 3-meter/7-meter Timed Up-and-Go, 10-meter Walk Test). Multiple linear regression models were derived for each gait parameter and mobility measure, with anthropometric measurements (age, height, body mass, gender) and self-selected walking speed as independent variables. The resulting models were used to normalize the gait parameters and mobility measures. The normalization's capability in de-correlating data and reducing data dispersion were evaluated. RESULTS: Gait parameters were predominantly influenced by height and walking speed, while mobility measures were affected by age and walking speed. Normalization de-correlated data from anthropometric measurements from |rs| < 0.74 to |rs| < 0.23, and reduced data dispersion by up to 69%. CONCLUSION: Normalization of gait parameters and mobility measures through linear regression models augment the capability to compare subjects with varying anthropometric measurements.

Toward Self-Control Systems for Neurogenic Underactive Bladder: A Triboelectric Nanogenerator Sensor Integrated with a Bistable Micro-Actuator.

Aging, neurologic diseases, and diabetes are a few risk factors that may lead to underactive bladder (UAB) syndrome. Despite all of the serious consequences of UAB, current solutions, the most common being ureteric catheterization, are all accompanied by serious shortcomings. The necessity of multiple catheterizations per day for a physically able patient not only reduces the quality of life with constant discomfort and pain but also can end up causing serious complications. Here, we present a bistable actuator to empty the bladder by incorporating shape memory alloy components integrated on flexible polyvinyl chloride sheets. The introduction of two compression and restoration phases for the actuator allows for repeated actuation for a more complete voiding of the bladder. The proposed actuator exhibits one of the highest reported voiding percentages of up to 78% of the bladder volume in an anesthetized rat after only 20 s of actuation. This amount of voiding is comparable to the common catheterization method, and its one time implantation onto the bladder rectifies the drawbacks of multiple catheterizations per day. Furthermore, the scaling of the device for animal models larger than rats can be easily achieved by adjusting the number of nitinol springs. For neurogenic UAB patients with degraded nerve function as well as degenerated detrusor muscle, we integrate a flexible triboelectric nanogenerator sensor with the actuator to detect the fullness of the bladder. The sensitivity of this sensor to the filling status of the bladder shows its capability for defining a self-control system in the future that would allow autonomous micturition.

Novel Neurostimulation of Autonomic Pelvic Nerves Overcomes Bladder-Sphincter Dyssynergia.

The disruption of coordination between smooth muscle contraction in the bladder and the relaxation of the external urethral sphincter (EUS) striated muscle is a common issue in dysfunctional bladders. It is a significant challenge to overcome for neuromodulation approaches to restore bladder control. Bladder-sphincter dyssynergia leads to undesirably high bladder pressures, and poor voiding outcomes, which can pose life-threatening secondary complications. Mixed pelvic nerves are potential peripheral targets for stimulation to treat dysfunctional bladders, but typical electrical stimulation of pelvic nerves activates both the parasympathetic efferent pathway to excite the bladder, as well as the sensory afferent pathway that causes unwanted sphincter contractions. Thus, a novel pelvic nerve stimulation paradigm is required. In anesthetized female rats, we combined a low frequency (10 Hz) stimulation to evoke bladder contraction, and a more proximal 20 kHz stimulation of the pelvic nerve to block afferent activation, in order to produce micturition with reduced bladder-sphincter dyssynergia. Increasing the phase width of low frequency stimulation from 150 to 300 µs alone was able to improve voiding outcome significantly. However, low frequency stimulation of pelvic nerves alone evoked short latency (19.9-20.5 ms) dyssynergic EUS responses, which were abolished with a non-reversible proximal central pelvic nerve cut. We demonstrated that a proximal 20 kHz stimulation of pelvic nerves generated brief onset effects at lower current amplitudes, and was able to either partially or fully block the short latency EUS responses depending on the ratio of the blocking to stimulation current. Our results indicate that ratios >10 increased the efficacy of blocking EUS contractions. Importantly, we also demonstrated for the first time that this combined low and high frequency stimulation approach produced graded control of the bladder, while reversibly blocking afferent signals that elicited dyssynergic EUS contractions, thus improving voiding by 40.5 ± 12.3%. Our findings support advancing pelvic nerves as a suitable neuromodulation target for treating bladder dysfunction, and demonstrate the feasibility of an alternative method to non-reversible nerve transection and sub-optimal intermittent stimulation methods to reduce dyssynergia.

A Highly Selective 3D Spiked Ultraflexible Neural (SUN) Interface for Decoding Peripheral Nerve Sensory Information.

Artificial sensors on the skin are proposed as a way to capture information that can be used in intracortical microstimulation or peripheral intraneural stimulation to restore sensory feedback to persons with tetraplegia. However, the ability of these artificial sensors to replicate the density and complexity of the natural mechanoreceptors is limited. One relatively unexplored approach is to make use of the signals from surviving tactile and proprioceptive receptors in existing limbs by recording from their transmitting axons within the primary sensory nerves. Here, a novel spiked ultraflexible neural (SUN) interface that is implanted into the peripheral nervous system to capture sensory information from these mechanoreceptors in acute rat experiments is described. The novel 3D design, which integrates spiked structures for intrafascicular nerve recording with an ultraflexible substrate, enables a unique conformal interface to the target nerve. With the high-quality recording (average signal-to-noise-ratio of 1.4) provided by the electrode, tactile from proprioceptive stimuli can be differentiated in terms of the firing rate. In toe pinching experiments, high spatial resolution classification can be achieved with support vector machine classifier. Further work remains to be done to assess the chronic recording capability of the SUN interface.