Anti-manic effect of deep brain stimulation of the ventral tegmental area in an animal model of mania induced by methamphetamine.

BACKGROUND: Treatment of refractory bipolar disorder (BD) is extremely challenging. Deep brain stimulation (DBS) holds promise as an effective treatment intervention. However, we still understand very little about the mechanisms of DBS and its application on BD. AIM: The present study aimed to investigate the behavioural and neurochemical effects of ventral tegmental area (VTA) DBS in an animal model of mania induced by methamphetamine (m-amph). METHODS: Wistar rats were given 14 days of m-amph injections, and on the last day, animals were submitted to 20 min of VTA DBS in two different patterns: intermittent low-frequency stimulation (LFS) or continuous high-frequency stimulation (HFS). Immediately after DBS, manic-like behaviour and nucleus accumbens (NAc) phasic dopamine (DA) release were evaluated in different groups of animals through open-field tests and fast-scan cyclic voltammetry. Levels of NAc dopaminergic markers were evaluated by immunohistochemistry. RESULTS: M-amph induced hyperlocomotion in the animals and both DBS parameters reversed this alteration. M-amph increased DA reuptake time post-sham compared to baseline levels, and both LFS and HFS were able to block this alteration. LFS was also able to reduce phasic DA release when compared to baseline. LFS was able to increase dopamine transporter (DAT) expression in the NAc. CONCLUSION: These results demonstrate that both VTA LFS and HFS DBS exert anti-manic effects and modulation of DA dynamics in the NAc. More specifically the increase in DA reuptake driven by increased DAT expression may serve as a potential mechanism by which VTA DBS exerts its anti-manic effects.

SmartFSCV: An Artificial Intelligence Enabled Miniaturised FSCV Device Targeting Serotonin.

Goal: Dynamically monitoring serotonin in real-time within target brain regions would significantly improve the diagnostic and therapeutic approaches to a variety of neurological and psychiatric disorders. Current systems for measuring serotonin lack immediacy and portability and are bulky and expensive. Methods: We present a new miniaturised device, named SmartFSCV, designed to monitor dynamic changes of serotonin using fast-scan cyclic voltammetry (FSCV). This device outputs a precision voltage potential between -3 to +3 V, and measures current between -1.5 to +1.5 µA with nano-ampere accuracy. The device can output modifiable arbitrary waveforms for various measurements and uses an N-shaped waveform at a scan-rate of 1000 V/s for sensing serotonin. Results: Four experiments were conducted to validate SmartFSCV: static bench test, dynamic serotonin test and two artificial intelligence (AI) algorithm tests. Conclusions: These tests confirmed the ability of SmartFSCV to accurately sense and make informed decisions about the presence of serotonin using AI.

A self-stiffening compliant intracortical microprobe.

Utilising a flexible intracortical microprobe to record/stimulate neurons minimises the incompatibility between the implanted microprobe and the brain, reducing tissue damage due to the brain micromotion. Applying bio-dissolvable coating materials temporarily makes a flexible microprobe stiff to tolerate the penetration force during insertion. However, the inability to adjust the dissolving time after the microprobe contact with the cerebrospinal fluid may lead to inaccuracy in the microprobe positioning. Furthermore, since the dissolving process is irreversible, any subsequent positioning error cannot be corrected by re-stiffening the microprobe. The purpose of this study is to propose an intracortical microprobe that incorporates two compressible structures to make the microprobe both adaptive to the brain during operation and stiff during insertion. Applying a compressive force by an inserter compresses the two compressible structures completely, resulting in increasing the equivalent elastic modulus. Thus, instant switching between stiff and soft modes can be accomplished as many times as necessary to ensure high-accuracy positioning while causing minimal tissue damage. The equivalent elastic modulus of the microprobe during operation is ≈ 23 kPa, which is ≈ 42% less than the existing counterpart, resulting in ≈ 46% less maximum strain generated on the surrounding tissue under brain longitudinal motion. The self-stiffening microprobe and surrounding neural tissue are simulated during insertion and operation to confirm the efficiency of the design. Two-photon polymerisation technology is utilised to 3D print the proposed microprobe, which is experimentally validated and inserted into a lamb's brain without buckling.

A Bluetooth-Enabled Device for Real-Time Detection of Sitting, Standing, and Walking: Cross-Sectional Validation Study.

BACKGROUND: This study assesses the accuracy of a Bluetooth-enabled prototype activity tracker called the Sedentary behaviOR Detector (SORD) device in identifying sedentary, standing, and walking behaviors in a group of adult participants. OBJECTIVE: The primary objective of this study was to determine the criterion and convergent validity of SORD against direct observation and activPAL. METHODS: A total of 15 healthy adults wore SORD and activPAL devices on their thighs while engaging in activities (lying, reclining, sitting, standing, and walking). Direct observation was facilitated with cameras. Algorithms were developed using the Python programming language. The Bland-Altman method was used to assess the level of agreement. RESULTS: Overall, 1 model generated a low level of bias and high precision for SORD. In this model, accuracy, sensitivity, and specificity were all above 0.95 for detecting sitting, reclining, standing, and walking. Bland-Altman results showed that mean biases between SORD and direct observation were 0.3% for sitting and reclining (limits of agreement [LoA]=-0.3% to 0.9%), 1.19% for standing (LoA=-1.5% to 3.42%), and -4.71% for walking (LoA=-9.26% to -0.16%). The mean biases between SORD and activPAL were -3.45% for sitting and reclining (LoA=-11.59% to 4.68%), 7.45% for standing (LoA=-5.04% to 19.95%), and -5.40% for walking (LoA=-11.44% to 0.64%). CONCLUSIONS: Results suggest that SORD is a valid device for detecting sitting, standing, and walking, which was demonstrated by excellent accuracy compared to direct observation. SORD offers promise for future inclusion in theory-based, real-time, and adaptive interventions to encourage physical activity and reduce sedentary behavior.

A numerical study on the flow of water-based ternary hybrid nanomaterials on a stretchable curved sheet.

Nanomaterials are quite promising in electronic cooling systems, heat exchangers, engine lubricants, brake liquids, shock absorbers, radiators, etc. Therefore, the study of heat transfer characteristics on the flow of trihybrid nanofluids on an exponentially stretched curved surface is developed. Purpose: In this study, trihybrid nanofluid is taken into consideration, which is composed of Fe3O4, Ag and Cu as nanoparticles and water as the basefluid. Heat generation and magnetic field impacts are addressed. Based on these assumptions, the governing partial differential equations were reduced to a favorable set of ordinary differential equations using adequate transformations. Formulation: The highly nonlinear coupled system of equations was numerically solved using the shooting method with the Runge-Kutta-Fehlberg technique. Findings: Trihybrid nanofluids improve the thermal performance of fluid when compared with other fluids such as hybrid nanofluids, nanofluids, and basefluids. The trihybrid nanofluid is efficient in heat transfer phenomenon and has a significant impact on the overall performance of a system, including cooling systems, heat exchangers, electronics, and many industrial processes. Graphical representation for the physical variables of the fluid velocity and temperature is discussed. The local Nusselt number and skin friction coefficient are computed and analyzed. A magnetic field decreases the velocity but escalates the temperature. The Nusselt number decreases for larger solid volume fractions. Novelty: The Tiwari and Das model for hybrid nanofluid extended for trihybrid nanoparticles has not been investigated previously. Heat transfer examination on the flow of trihybrid nanomaterials on exponentially curved stretching sheets considering magnetism force and heat generation consequence has not yet been studied.

ViTab Transformer Framework for Predicting Induced Electric Field and Focality in Transcranial Magnetic Stimulation.

Transcranial magnetic stimulation is an electromagnetic induction-based non-invasive therapeutic technique for neurological diseases. For finding new clinical applications and enhancing the efficacy of TMS in existing neurological disorders, the current study focuses on a deep learning-based prediction model as an alternative to time-consuming electromagnetic (EM) simulation software. The main bottleneck of the existing prediction models is to consider very few input parameters of a standard coil such as coil type and coil position for predicting an output of electric field value. To overcome this limitation, a transformer-based prediction model titled as ViTab transformer is developed in this work to predict electric field (E-max), focality or area of stmulation (S-half), and volume of stimulation (V-half) by considering several input parameters such as sources of MRI images, types of coils, coil position, rate of change of current, brain tissues conductivity, and coil distance from the scalp. The proposed framework consists of a vision and a tab transformer to handle both image and tabular-type data. The prediction performance of the offered model is evaluated in terms of coefficient determination, R2 score, for E-max, V-half, and S-half in the testing phase. The obtained result in terms of R2 score for E-max, V-half, and S-half are found 0.97, 0.87, and 0.90 respectively. The results indicate that the suggested ViTab transformer model can predict electric field as well as focality more accurately than the current state-of-the-art methods. The reduced computational time, as well as efficient prediction accuracy, resembles that ViTab transformer can assist the neuroscientist and neurosurgeon prior to providing superior TMS treatment in near future.

Dual core photonic crystal fiber based plasmonic refractive index sensor with ultra-wide detection range.

In this study, an ultra-wide range plasmonic refractive index sensor based on dual core photonic crystal fiber is suggested and analyzed numerically. The proposed design achieves fabrication feasibility by employing external sensing mechanism in which silver is deposited onto the flat outer surface of the fiber as plasmonic material. A thin layer of titanium oxide (TiO2) is considered on top of the silver layer for preventing its oxidation problem. The sensor attains identification of a vast array of analytes consisting a wide range of refractive indices of 1.10 - 1.45. It achieves a maximum spectral sensitivity of 24300 nm/RIU along with its corresponding resolution of 4.12 × 10-6 RIU. The maximum figure of merit of the sensor is 120 RIU-1. The sensor also supports amplitude interrogation approach and exhibits a maximum amplitude sensitivity of 172 RIU-1. The impact of the design parameters such as radius of air holes, polishing distance, thickness of silver and titanium oxide layers are investigated thoroughly. An ultra-wide detection range with high sensitivity, fabrication feasibility, and easy application make the sensor a potential candidate for detection of a wide array of bio-originated materials, chemicals, and other analytes.

Oxycodone-induced dopaminergic and respiratory effects are modulated by deep brain stimulation.

Introduction: Opioids are the leading cause of overdose death in the United States, accounting for almost 70,000 deaths in 2020. Deep brain stimulation (DBS) is a promising new treatment for substance use disorders. Here, we hypothesized that VTA DBS would modulate both the dopaminergic and respiratory effect of oxycodone. Methods: Multiple-cyclic square wave voltammetry (M-CSWV) was used to investigate how deep brain stimulation (130 Hz, 0.2 ms, and 0.2 mA) of the rodent ventral segmental area (VTA), which contains abundant dopaminergic neurons, modulates the acute effects of oxycodone administration (2.5 mg/kg, i.v.) on nucleus accumbens core (NAcc) tonic extracellular dopamine levels and respiratory rate in urethane-anesthetized rats (1.5 g/kg, i.p.). Results: I.V. administration of oxycodone resulted in an increase in NAcc tonic dopamine levels (296.9 ± 37.0 nM) compared to baseline (150.7 ± 15.5 nM) and saline administration (152.0 ± 16.1 nM) (296.9 ± 37.0 vs. 150.7 ± 15.5 vs. 152.0 ± 16.1, respectively, p = 0.022, n = 5). This robust oxycodone-induced increase in NAcc dopamine concentration was associated with a sharp reduction in respiratory rate (111.7 ± 2.6 min-1 vs. 67.9 ± 8.3 min-1; pre- vs. post-oxycodone; p < 0.001). Continuous DBS targeted at the VTA (n = 5) reduced baseline dopamine levels, attenuated the oxycodone-induced increase in dopamine levels to (+39.0% vs. +95%), and respiratory depression (121.5 ± 6.7 min-1 vs. 105.2 ± 4.1 min-1; pre- vs. post-oxycodone; p = 0.072). Discussion: Here we demonstrated VTA DBS alleviates oxycodone-induced increases in NAcc dopamine levels and reverses respiratory suppression. These results support the possibility of using neuromodulation technology for treatment of drug addiction.

A self-healing nanocomposite double network bacterial nanocellulose/gelatin hydrogel for three dimensional printing.

Extrusion-based three-dimensional (3D) printing of gelatin is important for additive manufactured tissue engineering scaffolds, but gelatin's thermal instability has remained an ongoing challenge. The gelatin tends to suddenly collapse at mild temperatures, which is a significant limitation for using it at physiological temperature of 37 °C. Hence, fabrication of a thermo-processable gelatin hydrogel adapted for extrusion-based additive manufacturing is still a challenge. To achieve this, a self-healing nanocomposite double-network (ncDN) gelatin hydrogel was fabricated with high thermo-processability, shear-thinning, mechanical strength, self-healing, self-recovery, and biocompatibility. To do this, amino group-rich gelatin was first created by combining gelatin with carboxyl methyl chitosan. Afterwards, a self-healing ncDN gelatin hydrogel was formed via an in-situ formation of imine bonds between the blend of gelatin/carboxyl methyl chitosan (Gel/CMCh) and dialdehyde-functionalized bacterial nanocellulose (dBNC). dBNC plays as nanofiber cross-linkers capable of simultaneously crosslinking and reinforcing the double networks of Gel/CMCh through formation of dynamic 3D imine bonds. Based on our findings, our self-healing ncDA gelatin hydrogel displayed great potential as a promising ink for additive manufactured tissue engineering scaffolds.

Evaluation of machine learning-based models for prediction of clinical deterioration: A systematic literature review.

BACKGROUND AND OBJECTIVE: Early identification of patients at risk of deterioration can prevent life-threatening adverse events and shorten length of stay. Although there are numerous models applied to predict patient clinical deterioration, most are based on vital signs and have methodological shortcomings that are not able to provide accurate estimates of deterioration risk. The aim of this systematic review is to examine the effectiveness, challenges, and limitations of using machine learning (ML) techniques to predict patient clinical deterioration in hospital settings. METHODS: A systematic review was performed in accordance with the Preferred Reporting Items for Systematic Reviews and meta-Analyses (PRISMA) guidelines using EMBASE, MEDLINE Complete, CINAHL Complete, and IEEExplore databases. Citation searching was carried out for studies that met inclusion criteria. Two reviewers used the inclusion/exclusion criteria to independently screen studies and extract data. To address any discrepancies in the screening process, the two reviewers discussed their findings and a third reviewer was consulted as needed to reach a consensus. Studies focusing on use of ML in predicting patient clinical deterioration that were published from inception to July 2022 were included. RESULTS: A total of 29 primary studies that evaluated ML models to predict patient clinical deterioration were identified. After reviewing these studies, we found that 15 types of ML techniques have been employed to predict patient clinical deterioration. While six studies used a single technique exclusively, several others utilised a combination of classical techniques, unsupervised and supervised learning, as well as other novel techniques. Depending on which ML model was applied and the type of input features, ML models predicted outcomes with an area under the curve from 0.55 to 0.99. CONCLUSIONS: Numerous ML methods have been employed to automate the identification of patient deterioration. Despite these advancements, there is still a need for further investigation to examine the application and effectiveness of these methods in real-world situations.

Comparative study and multi-parameter analysis to optimize device structure of triboelectric nanogenerators.

Triboelectric nanogenerator is becoming one of the most efficient energy harvesting device among all mechanical energy harvesters. This device consists of dielectric friction layers and metal electrode which generates electrical charges using electrostatic induction effect. There are several factors influencing the performance of this generator which needs to be evaluated prior to experiment. The absence of a universal technique for TENG simulation makes the device design and optimization hard before practical fabrication, which also lengthens the exploration and advancement cycle and hinders the arrival of practical applications. In order to deepen the understanding the core physic behind the working process of this device, this work will provide comparative analysis on different modes of TENG. Systematic investigation on different material combination, effect of material thickness, dielectric constant and impact of surface patterning is evaluated to shortlist the best material combination. COMSOL Multiphysics simulating environment is used to design, model and analyze factor affecting the overall output performance of TENG. The stationary study in this simulator is performed using 2D geometry structure with higher mesh density. During this study short circuit and open circuit condition were applied to observe the behavior of charge and electric potential produced. This observation is analyzed by plotting charge transfer/electric potential against various displacement distances of dielectric friction layers. The ouput is then provided to load ciruitary to measure the maximum output power of the models. Overall, this study provides an excellent understanding and multi-parameter analysis on basic theoretical and simulation modeling of TENG device.

A convolutional attention mapping deep neural network for classification and localization of cardiomegaly on chest X-rays.

Building a reliable and precise model for disease classification and identifying abnormal sites can provide physicians assistance in their decision-making process. Deep learning based image analysis is a promising technique for enriching the decision making process, and accordingly strengthening patient care. This work presents a convolutional attention mapping deep learning model, Cardio-XAttentionNet, to classify and localize cardiomegaly effectively. We revisit the global average pooling (GAP) system and add a weighting term to develop a light and effective Attention Mapping Mechanism (AMM). The model enables the classification of cardiomegaly from chest X-rays through image-level classification and pixel-level localization only from image-level labels. We leverage some of the advanced ConvNet architectures as a backbone-model of the proposed attention mapping network to build Cardio-XAttentionNet. The proposed model is trained on ChestX-Ray14, which is a publicly accessible chest X-ray dataset. The best single model achieves an overall precision, recall, F-1 measure and area under curve (AUC) scores of 0.87, 0.85, 0.86 and 0.89, respectively, for the classification of the cardiomegaly. The results also demonstrate that the Cardio-XAttentionNet model well captures the cardiomegaly class information at image-level as well as localization at pixel-level on chest x-rays. A comparative analysis between the proposed AMM and existing GAP based models shows that the proposed model achieves a state-of-the-art performance on this dataset for cardiomegaly detection using a single model.

Starch-g-Acrylic Acid/Magnetic Nanochitin Self-Healing Ferrogels as Flexible Soft Strain Sensors.

Mechanically robust ferrogels with high self-healing ability might change the design of soft materials used in strain sensing. Herein, a robust, stretchable, magneto-responsive, notch insensitive, ionic conductive nanochitin ferrogel was fabricated with both autonomous self-healing and needed resilience for strain sensing application without the need for additional irreversible static chemical crosslinks. For this purpose, ferric (III) chloride hexahydrate and ferrous (II) chloride as the iron source were initially co-precipitated to create magnetic nanochitin and the co-precipitation was confirmed by FTIR and microscopic images. After that, the ferrogels were fabricated by graft copolymerisation of acrylic acid-g-starch with a monomer/starch weight ratio of 1.5. Ammonium persulfate and magnetic nanochitin were employed as the initiator and crosslinking/nano-reinforcing agents, respectively. The ensuing magnetic nanochitin ferrogel provided not only the ability to measure strain in real-time under external magnetic actuation but also the ability to heal itself without any external stimulus. The ferrogel may also be used as a stylus for a touch-screen device. Based on our findings, our research has promising implications for the rational design of multifunctional hydrogels, which might be used in applications such as flexible and soft strain sensors, health monitoring, and soft robotics.

A self-healing magneto-responsive nanocellulose ferrogel and flexible soft strain sensor.

Crosslinks are the building blocks of hydrogels and play an important role in their overall properties. They may either be reversible and dynamic allowing for autonomous self-healing properties, or permanent and static resulting in robustness and mechanical strength. Hence, a combination of crosslinks is often required to engineer the 3D network of hydrogels with both autonomous self-healing and required robustness for strain sensing application; however, this complicates the fabrication of such hydrogels. The facile, yet versatile, approach used in this study is to forgo the use of extra crosslinks and instead rely solely on the properties of magnetic nanocellulose to fabricate a tough, stretchy, yet magneto-responsive, ionic conductive ferrogel for strain sensing. The ferrogel also gives stimuli-free and autonomous self-healing capacity, as well as the ability to monitor real-time strain under external magnetic actuation. The ferrogel also functions as a touch-screen pen. Based on our findings, this study has the potential to advance the rational design of multifunctional hydrogels, with applications in soft and flexible strain sensors, health monitoring and soft robotics.

High frequency deep brain stimulation can mitigate the acute effects of cocaine administration on tonic dopamine levels in the rat nucleus accumbens.

Cocaine's addictive properties stem from its capacity to increase tonic extracellular dopamine levels in the nucleus accumbens (NAc). The ventral tegmental area (VTA) is a principal source of NAc dopamine. To investigate how high frequency stimulation (HFS) of the rodent VTA or nucleus accumbens core (NAcc) modulates the acute effects of cocaine administration on NAcc tonic dopamine levels multiple-cyclic square wave voltammetry (M-CSWV) was used. VTA HFS alone decreased NAcc tonic dopamine levels by 42%. NAcc HFS alone resulted in an initial decrease in tonic dopamine levels followed by a return to baseline. VTA or NAcc HFS following cocaine administration prevented the cocaine-induced increase in NAcc tonic dopamine. The present results suggest a possible underlying mechanism of NAc deep brain stimulation (DBS) in the treatment of substance use disorders (SUDs) and the possibility of treating SUD by abolishing dopamine release elicited by cocaine and other drugs of abuse by DBS in VTA, although further studies with chronic addiction models are required to confirm that. Furthermore, we demonstrated the use of M-CSWV can reliably measure tonic dopamine levels in vivo with both drug administration and DBS with minimal artifacts.

Attention-assisted hybrid 1D CNN-BiLSTM model for predicting electric field induced by transcranial magnetic stimulation coil.

Deep learning-based models such as deep neural network (DNN) and convolutional neural network (CNN) have recently been established as state-of-the-art for enumerating electric fields from transcranial magnetic stimulation coil. One of the main challenges related to this electric field enumeration is the prediction time and accuracy. Despite the low computational cost, the performance of the existing prediction models for electric field enumeration is quite inefficient. This study proposes a 1D CNN-based bi-directional long short-term memory (BiLSTM) model with an attention mechanism to predict electric field induced by a transcranial magnetic stimulation coil. The model employs three consecutive 1D CNN layers followed by the BiLSTM layer for extracting deep features. After that, the weights of the deep features are redistributed and integrated by the attention mechanism and a fully connected layer is utilized for the prediction. For the prediction purpose, six input features including coil turns of single wing, coil thickness, coil diameter, distance between two wings, distance between head and coil position, and angle between two wings of coil are mapped with the output of the electric field. The performance evaluation is conducted based on four verification metrics (e.g. R2, MSE, MAE, and RMSE) between the simulated data and predicted data. The results indicate that the proposed model outperforms existing DNN and CNN models in predicting the induced electrical field with R2 = 0.9992, MSE = 0.0005, MAE = 0.0188, and RMSE = 0.0228 in the testing stage.

Specifying a cascade water demand forecasting model using time-series analysis: a case of Jordan.

This study aims to develop a model for forecasting water demand for 2021-2030 to examine water availability for municipality uses in the Al-Balqa governorate of Jordan. The method was developed using a time series analysis of historical data from 1990-2010, which comprised yearly and monthly water consumption and socioeconomic factors, including population, income, and climate factors, such as average precipitation and temperatures. The analysis of historical data was conducted using the Statistical Package for the Social Sciences. The study found that the increase in population, reaching 740,790 inhabitants in 2030, the high level of social life, and the fluctuation of temperature and precipitation exceed the significant water demand, increasing to 69.88 million cubic meters in 2030 from 52.95 in 2020. The time series analysis employed historical data for 2011-2020 indicating monthly municipal water use to measure the model's validity. The results confirm the model's ability to forecast water demand. The study recommends intensifying managerial practices to avoid such difficulties that face the water sector to achieve water security at the country's level.

Electrophysiology-Based Closed Loop Optogenetic Brain Stimulation Devices: Recent Developments and Future Prospects.

With its potential of single cell specificity, optogenetics has made the investigation into the brain circuits more controllable. Closed loop optogenetic brain stimulation enhances the efficacy of the stimulation by adjusting the stimulation parameters based on direct feedback from the target area of the brain. It combines the principles of genetics, physiology, electrical engineering, optics, signal processing and control theory to create an efficient brain stimulation system. To read the underlying neuronal condition from the electrical activity of neurons, a sensor, sensor interface circuit, and signal conditioning are needed. Also, efficient feature extraction, classification, and control algorithms should be in place to interpret and use the sensed data for closing the feedback loop. Finally, a stimulation circuitry is required to effectively control a light source to deliver light based stimulation according to the feedback signal. Thus, the backbone to a functioning closed loop optogenetic brain stimulation device is a well-built electronic circuitry for sensing and processing of brain signals, running efficient signal processing and control algorithm, and delivering timed light stimulations. This paper presents a review of electronic and software concepts and components used in recent closed-loop optogenetic devices based on neuro-electrophysiological reading and an outlook on the future design possibilities with the aim of providing a compact and easy reference for developing closed loop optogenetic brain stimulation devices.

Development of an integrated microperfusion-EEG electrode for unbiased multimodal sampling of brain interstitial fluid and concurrent neural activity.

Objective. To modify off-the-shelf components to build a device for collecting electroencephalography (EEG) from macroelectrodes surrounded by large fluid access ports sampled by an integrated microperfusion system in order to establish a method for sampling brain interstitial fluid (ISF) at the site of stimulation or seizure activity with no bias for molecular size.Approach. Twenty-four 560µm diameter holes were ablated through the sheath surrounding one platinum-iridium macroelectrode of a standard Spencer depth electrode using a femtosecond UV laser. A syringe pump was converted to push-pull configuration and connected to the fluidics catheter of a commercially available microdialysis system. The fluidics were inserted into the lumen of the modified Spencer electrode with the microdialysis membrane removed, converting the system to open flow microperfusion. Electrical performance and analyte recovery were measured and parameters were systematically altered to improve performance. An optimized device was tested in the pig brain and unbiased quantitative mass spectrometry was used to characterize the perfusate collected from the peri-electrode brain in response to stimulation.Main results. Optimized parameters resulted in >70% recovery of 70 kDa dextran from a tissue analog. The optimized device was implanted in the cortex of a pig and perfusate was collected during four 60 min epochs. Following a baseline epoch, the macroelectrode surrounded by microperfusion ports was stimulated at 2 Hz (0.7 mA, 200µs pulse width). Following a post-stimulation epoch, the cortex near the electrode was stimulated with benzylpenicillin to induce epileptiform activity. Proteomic analysis of the perfusates revealed a unique inflammatory signature induced by electrical stimulation. This signature was not detected in bulk tissue ISF.Significance. A modified dual-sensing electrode that permits coincident detection of EEG and ISF at the site of epileptiform neural activity may reveal novel pathogenic mechanisms and therapeutic targets that are otherwise undetectable at the bulk tissue level.

AI-Based Soft Module for Safe Human-Robot Interaction towards 4D Printing.

Soft robotic modules have potential use for therapeutic and educational purposes. To do so, they need to be safe, soft, smart, and customizable to serve individuals' different preferences and personalities. A safe modular robotic product made of soft materials, particularly silicon, programmed by artificial intelligence algorithms and developed via additive manufacturing would be promising. This study focuses on the safe tactile interaction between humans and robots by means of soft material characteristics for translating physical communication to auditory. The embedded vibratory sensors used to stimulate touch senses transmitted through soft materials are presented. The soft module was developed and verified successfully to react to three different patterns of human-robot contact, particularly users' touches, and then communicate the type of contact with sound. The study develops and verifies a model that can classify different tactile gestures via machine learning algorithms for safe human-robot physical interaction. The system accurately recognizes the gestures and shapes of three-dimensional (3D) printed soft modules. The gestures used for the experiment are the three most common, including slapping, squeezing, and tickling. The model builds on the concept of how safe human-robot physical interactions could help with cognitive and behavioral communication. In this context, the ability to measure, classify, and reflect the behavior of soft materials in robotic modules represents a prerequisite for endowing robotic materials in additive manufacturing for safe interaction with humans.