Trends and Challenges of Wearable Multimodal Technologies for Stroke Risk Prediction.

We review in this paper the wearable-based technologies intended for real-time monitoring of stroke-related physiological parameters. These measurements are undertaken to prevent death and disability due to stroke. We compare the various characteristics, such as weight, accessibility, frequency of use, data continuity, and response time of these wearables. It was found that the most user-friendly wearables can have limitations in reporting high-precision prediction outcomes. Therefore, we report also the trend of integrating these wearables into the internet of things (IoT) and combining electronic health records (EHRs) and machine learning (ML) algorithms to establish a stroke risk prediction system. Due to different characteristics, such as accessibility, time, and spatial resolution of various wearable-based technologies, strategies of applying different types of wearables to maximize the efficacy of stroke risk prediction are also reported. In addition, based on the various applications of multimodal electroencephalography-functional near-infrared spectroscopy (EEG-fNIRS) on stroke patients, the perspective of using this technique to improve the prediction performance is elaborated. Expected prediction has to be dynamically delivered with high-precision outcomes. There is a need for stroke risk stratification and management to reduce the resulting social and economic burden.

Soft, comfortable polymer dry electrodes for high quality ECG and EEG recording.

Conventional gel electrodes are widely used for biopotential measurements, despite important drawbacks such as skin irritation, long set-up time and uncomfortable removal. Recently introduced dry electrodes with rigid metal pins overcome most of these problems; however, their rigidity causes discomfort and pain. This paper presents dry electrodes offering high user comfort, since they are fabricated from EPDM rubber containing various additives for optimum conductivity, flexibility and ease of fabrication. The electrode impedance is measured on phantoms and human skin. After optimization of the polymer composition, the skin-electrode impedance is only ~10 times larger than that of gel electrodes. Therefore, these electrodes are directly capable of recording strong biopotential signals such as ECG while for low-amplitude signals such as EEG, the electrodes need to be coupled with an active circuit. EEG recordings using active polymer electrodes connected to a clinical EEG system show very promising results: alpha waves can be clearly observed when subjects close their eyes, and correlation and coherence analyses reveal high similarity between dry and gel electrode signals. Moreover, all subjects reported that our polymer electrodes did not cause discomfort. Hence, the polymer-based dry electrodes are promising alternatives to either rigid dry electrodes or conventional gel electrodes.

Optical Transmission in Single-Layer Brain Tissues under Different Optical Source Types: Modelling and Simulation.

The human brain is a complex organ controlling daily activity. Present technique models have mostly focused on multi-layer brain tissues, which lack understanding of the propagation characteristics of various single brain tissues. To better understand the influence of different optical source types on individual brain tissues, we constructed single-layer brain models and simulated optical propagation using the Monte Carlo method. Based on the optical simulation results, sixteen optical source types had different optical energy distributions, and the distribution in cerebrospinal fluid had obvious characteristics. Five brain tissues (scalp, skull, cerebrospinal fluid, gray matter, and blood vessel) had the same set of the first three optical source types with maximum depth, while white matter had a different set of the first three optical source types with maximum depth. Each brain tissue had different optical source types with the maximum and minimum full width at half maximum. The study on single-layer brain tissues under different optical source types lays the foundation for constructing complex brain models with multiple tissue layers. It provides a theoretical reference for optimizing the selection of optical source devices for brain imaging.

Alzheimer's Disease Diagnosis in the Preclinical Stage: Normal Aging or Dementia.

Alzheimer's disease (AD) progressively impairs the memory and thinking skills of patients, resulting in a significant global economic and social burden each year. However, diagnosis of this neurodegenerative disorder can be challenging, particularly in the early stages of developing cognitive decline. Current clinical techniques are expensive, laborious, and invasive, which hinders comprehensive studies on Alzheimer's biomarkers and the development of efficient devices for Point-of-Care testing (POCT) applications. To address these limitations, researchers have been investigating various biosensing techniques. Unfortunately, these methods have not been commercialized due to several drawbacks, such as low efficiency, reproducibility, and the lack of accurate identification of AD markers. In this review, we present diverse promising hallmarks of Alzheimer's disease identified in various biofluids and body behaviors. Additionally, we thoroughly discuss different biosensing mechanisms and the associated challenges in disease diagnosis. In each context, we highlight the potential of realizing new biosensors to study various features of the disease, facilitating its early diagnosis in POCT. This comprehensive study, focusing on recent efforts for different aspects of the disease and representing promising opportunities, aims to conduct the future trend toward developing a new generation of compact multipurpose devices that can address the challenges in the early detection of AD.

Enhancing photoacoustic imaging for lung diagnostics and BCI communication: simulation of cavity structures artifact generation and evaluation of noise reduction techniques.

Pandemics like COVID-19 have highlighted the potential of Photoacoustic imaging (PAI) for Brain-Computer Interface (BCI) communication and lung diagnostics. However, PAI struggles with the clear imaging of blood vessels in areas like the lungs and brain due to their cavity structures. This paper presents a simulation model to analyze the generation and propagation mechanism within phantom tissues of PAI artifacts, focusing on the evaluation of both Anisotropic diffusion filtering (ADF) and Non-local mean (NLM) filtering, which significantly reduce noise and eliminate artifacts and signify a pivotal point for selecting artifact-removal algorithms under varying conditions of light distribution. Experimental validation demonstrated the efficacy of our technique, elucidating the effect of light source uniformity on artifact-removal performance. The NLM filtering simulation and ADF experimental validation increased the peak signal-to-noise ratio by 11.33% and 18.1%, respectively. The proposed technique adds a promising dimension for BCI and is an accurate imaging solution for diagnosing lung diseases.

Monte Carlo-Based Optical Simulation of Optical Distribution in Deep Brain Tissues Using Sixteen Optical Sources.

Optical-based imaging has improved from early single-location research to further sophisticated imaging in 2D topography and 3D tomography. These techniques have the benefit of high specificity and non-radiative safety for brain detection and therapy. However, their performance is limited by complex tissue structures. To overcome the difficulty in successful brain imaging applications, we conducted a simulation using 16 optical source types within a brain model that is based on the Monte Carlo method. In addition, we propose an evaluation method of the optical propagating depth and resolution, specifically one based on the optical distribution for brain applications. Based on the results, the best optical source types were determined in each layer. The maximum propagating depth and corresponding source were extracted. The optical source propagating field width was acquired in different depths. The maximum and minimum widths, as well as the corresponding source, were determined. This paper provides a reference for evaluating the optical propagating depth and resolution from an optical simulation aspect, and it has the potential to optimize the performance of optical-based techniques.

Green and Heavy-Duty Anticorrosion Coatings: Waterborne Epoxy Thermoset Composites Modified through Variation of Zinc Dust Loading and Incorporation of Amine-Capped Aniline Trimer and Graphene Oxide.

In this study, an array of environmentally friendly and heavy-duty anticorrosion composite coatings were prepared. The synthesis involved amine-capped aniline trimer (ACAT) produced by an oxidative coupling reaction and graphene oxide (GO) prepared based on Hummer's method, and later, the waterborne epoxy thermoset composite (WETC) coatings were prepared by thermal ring-opening polymerization of EP 147w, a commercial waterborne epoxy resin, in the presence of ACAT and/or GO with zinc dust (ZD). A synergistic effect was observed by replacing a significant amount of the ZD loading in the WETC by simultaneously incorporating a small amount of ACAT and GO. The electrochemical corrosion measurements of the as-prepared WETC coatings indicated that incorporating 5% w/w ACAT or 0.5% w/w GO separately replaced approximately 30% w/w or 15% w/w of the ZD, respectively. Moreover, the WETC coatings containing 5% w/w ACAT and 0.5% w/w GO simultaneously were found to replace 45% w/w of the ZD. A salt spray test based on ASTM B-117 also showed a consistent trend with the electrochemical results. Incorporating small amounts of ACAT and GO in WETC coatings instead of ZD not only maintains the anticorrosion performance but also enhances adhesion and abrasion resistance, as demonstrated by the adhesion and abrasion tests.

Exploring the Role of Visual Guidance in Motor Imagery-Based Brain-Computer Interface: An EEG Microstate-Specific Functional Connectivity Study.

Motor imagery-based brain-computer interfaces (BCI) have been widely recognized as beneficial tools for rehabilitation applications. Moreover, visually guided motor imagery was introduced to improve the rehabilitation impact. However, the reported results to support these techniques remain unsatisfactory. Electroencephalography (EEG) signals can be represented by a sequence of a limited number of topographies (microstates). To explore the dynamic brain activation patterns, we conducted EEG microstate and microstate-specific functional connectivity analyses on EEG data under motor imagery (MI), motor execution (ME), and guided MI (GMI) conditions. By comparing sixteen microstate parameters, the brain activation patterns induced by GMI show more similarities to ME than MI from a microstate perspective. The mean duration and duration of microstate four are proposed as biomarkers to evaluate motor condition. A support vector machine (SVM) classifier trained with microstate parameters achieved average accuracies of 80.27% and 66.30% for ME versus MI and GMI classification, respectively. Further, functional connectivity patterns showed a strong relationship with microstates. Key node analysis shows clear switching of key node distribution between brain areas among different microstates. The neural mechanism of the switching pattern is discussed. While microstate analysis indicates similar brain dynamics between GMI and ME, graph theory-based microstate-specific functional connectivity analysis implies that visual guidance may reduce the functional integration of the brain network during MI. Thus, we proposed that combined MI and GMI for BCI can improve neurorehabilitation effects. The present findings provide insights for understanding the neural mechanism of microstates, the role of visual guidance in MI tasks, and the experimental basis for developing new BCI-aided rehabilitation systems.

Challenges and future trends in wearable closed-loop neuromodulation to efficiently treat methamphetamine addiction.

Achieving abstinence from drugs is a long journey and can be particularly challenging in the case of methamphetamine, which has a higher relapse rate than other drugs. Therefore, real-time monitoring of patients' physiological conditions before and when cravings arise to reduce the chance of relapse might help to improve clinical outcomes. Conventional treatments, such as behavior therapy and peer support, often cannot provide timely intervention, reducing the efficiency of these therapies. To more effectively treat methamphetamine addiction in real-time, we propose an intelligent closed-loop transcranial magnetic stimulation (TMS) neuromodulation system based on multimodal electroencephalogram-functional near-infrared spectroscopy (EEG-fNIRS) measurements. This review summarizes the essential modules required for a wearable system to treat addiction efficiently. First, the advantages of neuroimaging over conventional techniques such as analysis of sweat, saliva, or urine for addiction detection are discussed. The knowledge to implement wearable, compact, and user-friendly closed-loop systems with EEG and fNIRS are reviewed. The features of EEG and fNIRS signals in patients with methamphetamine use disorder are summarized. EEG biomarkers are categorized into frequency and time domain and topography-related parameters, whereas for fNIRS, hemoglobin concentration variation and functional connectivity of cortices are described. Following this, the applications of two commonly used neuromodulation technologies, transcranial direct current stimulation and TMS, in patients with methamphetamine use disorder are introduced. The challenges of implementing intelligent closed-loop TMS modulation based on multimodal EEG-fNIRS are summarized, followed by a discussion of potential research directions and the promising future of this approach, including potential applications to other substance use disorders.

Longitudinal Data to Enhance Dynamic Stroke Risk Prediction.

Stroke risk prediction based on electronic health records is currently an important research topic. Previous research activities have generally used single-time physiological data to build static models and have focused on algorithms to improve prediction accuracy. Few studies have considered historical measurements from a data perspective to construct dynamic models. Since it is a chronic disease, the risk of having a stroke increases and the corresponding risk factors become abnormal when healthy people are diagnosed with a stroke. Therefore, in this paper, we applied longitudinal data, with the backward joint model, to the Chinese Longitudinal Healthy Longevity and Happy Family Study's dataset to monitor changes in individuals' health status precisely on time and to increase the prediction accuracy of the model. The three-year prediction accuracy of our model, considering three measurements of longitudinal parameters, is 0.926. This is higher than the traditional Cox proportional hazard model, which has a 0.833 prediction accuracy. The results obtained in this study verified that longitudinal data improves stroke risk prediction accuracy and is promising for dynamic stroke risk prediction and prevention. Our model also verified that the frequency of fruit consumption, erythrocyte hematocrit, and glucose are potential stroke-related factors.

Intelligent Classification Technique of Hand Motor Imagery Using EEG Beta Rebound Follow-Up Pattern.

To apply EEG-based brain-machine interfaces during rehabilitation, separating various tasks during motor imagery (MI) and assimilating MI into motor execution (ME) are needed. Previous studies were focusing on classifying different MI tasks based on complex algorithms. In this paper, we implement intelligent, straightforward, comprehensible, time-efficient, and channel-reduced methods to classify ME versus MI and left- versus right-hand MI. EEG of 30 healthy participants undertaking motional tasks is recorded to investigate two classification tasks. For the first task, we first propose a "follow-up" pattern based on the beta rebound. This method achieves an average classification accuracy of 59.77% ± 11.95% and can be up to 89.47% for finger-crossing. Aside from time-domain information, we map EEG signals to feature space using extraction methods including statistics, wavelet coefficients, average power, sample entropy, and common spatial patterns. To evaluate their practicability, we adopt a support vector machine as an intelligent classifier model and sparse logistic regression as a feature selection technique and achieve 79.51% accuracy. Similar approaches are taken for the second classification reaching 75.22% accuracy. The classifiers we propose show high accuracy and intelligence. The achieved results make our approach highly suitable to be applied to the rehabilitation of paralyzed limbs.

PDK1- and PDK2-mediated metabolic reprogramming contributes to the TGFß1-promoted stem-like properties in head and neck cancer.

BACKGROUND: Resistance to chemotherapeutic drugs is a key factor for cancer recurrence and metastases in head and neck cancer (HNC). Cancer stem cells (CSCs) in tumors have self-renewal, differentiation, and higher drug resistance capabilities, resulting in a poor prognosis for patients. In glucose metabolism, pyruvate dehydrogenase kinase (PDK) inhibits pyruvate dehydrogenase and impedes pyruvate from being metabolized into acetyl-CoA and entering the tricarboxylic acid cycle to generate energy. Studies have reported that PDK1 and PDK2 inhibition suppresses the growth, motility, and drug resistance of cancer cells. Furthermore, while TGFß1 levels are persistently elevated in HNC patients with poor prognosis, the role of PDK isoforms in the TGFß1-promoted progression and stem-like properties of HNC is unclear. METHODS: Levels of PDK1 and PDK2 were evaluated in HNC tissue microarrays by immunohistochemistry to explore potential clinical relevance. PDK1 and PDK2 were knocked down by the lentivirus shRNA system to investigate their role in TGFß1-promoted tumor progression in vitro. RESULTS: We found that PDK2 levels were increased in the later stage of HNC tissues compared to constant PDK1 expression. After PDK1 and PDK2 knockdown, we discovered increased ATP production and decreased lactate production in TGFß1-treated and untreated HNC cells. However, only PDK2 silencing significantly inhibited the clonogenic ability of HNC cells. We subsequently found that TGFß1-promoted migration and invasion capabilities were decreased in PDK1 and PDK2 knockdown cells. The tumor spheroid-forming capability, motility, CSC genes, and multidrug-resistant genes were downregulated in PDK1 and PDK2 silencing CSCs. PDK1 and PDK2 inhibition reversed cisplatin and gemcitabine resistance of CSCs, but not paclitaxel resistance. CONCLUSION: The results demonstrated that the PDK1- and PDK2-mediated Warburg effect contributes to the TGFß1-enhanced stemness properties of HNC. Therefore, PDK1 and PDK2 may serve as molecular targets for the combination therapy of HNC.

Photoacoustic imaging for monitoring of stroke diseases: A review.

Stroke is the leading cause of death and disability after ischemic heart disease. However, there is lacking a non-invasive long-time monitoring technique for stroke diagnosis and therapy. The photoacoustic imaging approach reconstructs images of an object based on the energy excitation by optical absorption and its conversion to acoustic waves, due to corresponding thermoelastic expansion, which has optical resolution and acoustic propagation. This emerging functional imaging method is a non-invasive technique. Due to its precision, this method is particularly attractive for stroke monitoring purpose. In this paper, we review the achievements of this technology and its applications on stroke, as well as the development status in both animal and human applications. Also, various photoacoustic systems and multi-modality photoacoustic imaging are introduced as for potential clinical applications. Finally, the challenges of photoacoustic imaging for monitoring stroke are discussed.

Surface charge manipulation and electrostatic immobilization of synaptosomes for super-resolution imaging: a study on tau compartmentalization.

Synaptosomes are subcellular fractions prepared from brain tissues that are enriched in synaptic terminals, widely used for the study of neural transmission and synaptic dysfunction. Immunofluorescence imaging is increasingly applied to synaptosomes to investigate protein localization. However, conventional methods for imaging synaptosomes over glass coverslips suffer from formaldehyde-induced aggregation. Here, we developed a facile strategy to capture and image synaptosomes without aggregation artefacts. First, ethylene glycol bis(succinimidyl succinate) (EGS) is chosen as the chemical fixative to replace formaldehyde. EGS/glycine treatment makes the zeta potential of synaptosomes more negative. Second, we modified glass coverslips with 3-aminopropyltriethoxysilane (APTES) to impart positive charges. EGS-fixed synaptosomes spontaneously attach to modified glasses via electrostatic attraction while maintaining good dispersion. Individual synaptic terminals are imaged by conventional fluorescence microscopy or by super-resolution techniques such as direct stochastic optical reconstruction microscopy (dSTORM). We examined tau protein by two-color and three-color dSTORM to understand its spatial distribution within mouse cortical synapses, observing tau colocalization with synaptic vesicles as well postsynaptic densities.

Cold-atmospheric plasma augments functionalities of hybrid polymeric carriers regenerating chronic wounds: In vivo experiments.

The skin possesses an epithelial barrier. Delivering growth factors to deeper wounds is usually rather challenging, and these typically restrict the therapeutic efficacy for chronic wound healing. Efficient healing of chronic wounds also requires abundant blood flow. Therefore, addressing these concerns is crucial. Among presently accessible biomedical materials, tailored hydrogels are favorable for translational medicine. However, these hydrogels display insufficient mechanical properties, hampering their biomedical uses. Cold-atmospheric plasma (CAP) has potent cross-linking/polymerizing abilities. The CAP was characterized spectroscopically to identify excited radiation and species (hydroxyl and UV). CAP was used to polymerize pyrrole (creating Ppy) and crosslink hybrid polymers (Ppy, hyaluronic acid (HA), and gelatin (GEL)) as a multimodal dressing for chronic wounds (CAP-Ppy/GEL/HA), which were used to incorporate therapeutic platelet proteins (PPs). Herein, the physicochemical and biological features of the developed CAP-Ppy/GEL/HA/PP complex were assessed. CAP-Ppy/GEL/HA/PPs had positive impacts on wound healing in vitro. In addition, the CAP-Ppy/GEL/HA complex has improved mechanical aspects, therapeutics sustained-release/retention effect, and near-infrared (NIR)-driven photothermal-hyperthermic effects on lesions that drive the expression of heat-shock protein (HSP) with anti-inflammatory properties for boosted restoration of diabetic wounds in vivo. These in vitro and in vivo outcomes support the use of CAP-Ppy/GEL/HA/PPs for diabetic wound regeneration.

Cold atmospheric plasma physically reinforced substances of platelets-laden photothermal-responsive methylcellulose complex restores burn wounds.

Patients with irregular, huge burn wounds require time-consuming healing. The skin has an epithelial barrier mechanism. Hence, the penetration and retention of therapeutics across the skin to deep lesion is generally quite difficult and these usually constrain the delivery/therapeutic efficacies for wound healing. Effective burn wound healing also necessitates proper circulation. Conventional polymeric dressing usually exhibits weak mechanical behaviors, obstructing their load-bearing applications. Cold atmospheric plasma (CAP) was used as an efficient, environmentally friendly, and biocompatible process to crosslink methylcellulose (MC) designed for topical administration such as therapeutic substances of platelets (SP) and polyethyleneimine-polypyrrole nanoparticle (PEI-PPy NP)-laden MC hydrogel carriers, and wound dressings. The roles of framework parameters for CAP-treated SP-PEI-PPy NP-MC polymeric complex system; chemical, physical, and photothermal effects; morphological, spectroscopical, mechanical, rheological, and surface properties; in vitro drug release; and hydrophobicity are discussed. Furthermore, CAP-treated SP-PEI-PPy NP-MC polymeric complex possessed augmented mechanical properties, biocompatibility, sustainable drug release, drug-retention effects, and near-infrared (NIR)-induced hyperthermia effects that drove heat-shock protein (HSP) expression with drug permeation to deep lesions. This work sheds light on the CAP crosslinking polymeric technology and the efficacy of combining sustained drug release with photothermal therapy in burn wound bioengineering carrier designs.

Photothermal-Irradiated Polyethyleneimine-Polypyrrole Nanopigment Film-Coated Polyethylene Fabrics for Infrared-Inspired with Pathogenic Evaluation.

Influenza, pneumonia, and pathogenic infection of the respiratory system are boosted in cold environments. Low temperatures also result in vasoconstriction, restraint of blood flow, and decreased oxygen to the heart, and the risk of a heart attack would increase accordingly. The present face mask fabric fails to preserve its air-filtering function as its electrostatic function vanishes once exposed to water. Therefore, its filtering efficacy would be decreased meaningfully, making it nearly impracticable to reuse the disposable face masks. The urgent requirement for photothermal fabrics is also rising. Nanobased polyethyleneimine-polypyrrole nanopigments (NPP NPs) have been developed and have strong near-infrared spectrum absorption and exceptional photothermal convertible performance. Herein, the mask fabric used PE-fiber-constructed membrane (PEFM) was coated by the binding affinity of the cationic polyethyleneimine component of NPP NPs forming NPP NPs-PEFM. To the best of our knowledge, no study has investigated NPP NP-coated mask fabric to perform infrared red (solar or body) photothermal conversion efficacy to provide biocompatible warming, remotely photothermally captured antipathogen, and antivasoconstriction in vivo. This pioneering study showed that the developed NPP NPs-PEFM could be washable, reusable, breathable, biocompatible, and photothermal conversable for active eradication of pathogenic bacteria. Further, it possesses warming preservation and antivasoconstriction.

Noncontact ECG recording system with real time capacitance measurement for motion artifact reduction.

A system for noncontact ECG recording is proposed that measures the real time electrode-body capacitance concurrently with the ECG as a reference signal for motion artifact reduction. Simultaneous recordings of these two signals from the human body in the presence of electrode motion artifacts are shown and an adaptive least-mean-squares (LMS) filtering algorithm run on these signals is demonstrated to be able to reduce the severity of certain types of electrode motion artifacts.