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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.

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.

Brain diseases need advanced detection methods to diagnosis abnormalities. Advanced imaging techniques have been developed and applied in different stages for several requirements related to anatomical, functional, and metabolic imaging for the human brain. Among them, photoacoustic imaging has the advantages of high resolution and deep penetration with rich optical contrast, which plays an important role in brain imaging. The characteristic of photoacoustic imaging brings the opportunity for different objects with multi-scale observation in the human brain. In this chapter, the authors introduce the principle and theory of photoacoustic imaging, review the modality of the present photoacoustic imaging systems and applications for brain diseases, and summarize the trends of this imaging technique.

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 TGFβ1 levels are persistently elevated in HNC patients with poor prognosis, the role of PDK isoforms in the TGFβ1-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 TGFβ1-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 TGFβ1-treated and untreated HNC cells. However, only PDK2 silencing significantly inhibited the clonogenic ability of HNC cells. We subsequently found that TGFβ1-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 TGFβ1-enhanced stemness properties of HNC. Therefore, PDK1 and PDK2 may serve as molecular targets for the combination therapy of HNC.

Functional near-infrared spectroscopy (fNIRS) is an emerging wearable neuroimaging technique based on monitoring the hemodynamics of brain activity. First, the operation principle of fNIRS is described. This includes introducing the absorption spectra of the targeted molecule: the oxygenated and deoxygenated hemoglobin. Then, the optical path formed by emitters and detectors and the concentration of the molecules is determined using Beer-Lambert law. In the second part, the advantages of applying fNIRS are compared with other neuroimaging techniques, such as computed tomography and magnetic resonance imaging. The compared parameters include time and spatial resolution, immobility, etc. Next, the evolution of the fNIRS devices is shown. It includes the commercially available systems and the others under construction in academia. In the last section, the applications of fNIRS to avoid stroke are presented. The challenges of achieving good signal quality and high user comfort monitoring on stroke patients are discussed. Due to the wearable, user-friendly, and accessibility characteristics of fNIRS, it has the potential to be a complementary technique for real-time bedside monitoring of stroke patients. A stroke risk prediction system can be implemented to avoid stroke by combining the recorded fNIRS signals, routinely monitored physiological parameters, electronic health records, and machine learning models.

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 simple and 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.

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.

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.

Cold atmospheric plasma (CAP) is a promising tool to overcome certain cancerous and precancerous conditions in dermatology. A scheme of transferred CAP was first developed to treat melanoma (B16F10) skin cancer cells as well as non-malignant (L929) cells in vitro. CAP was transferred using a silicone tube with a jet system that was developed and was assessed as to whether it could generate reactive oxygen and nitrogen species (RONS) at near-room temperature. The transferred CAP was characterized electrically and spectroscopically. Biological data showed that the transferred CAP killed cancer cells but not non-malignant (L929) cells. Plasma treatment was effective with a time duration of 30 s, whereas non-malignant (L929) cells were less damaged during plasma treatment. In addition, catalase (CAT) enzyme was applied to neutralize and detoxify the RONS generated by the transferred CAP. These findings suggest that transferred CAP can be considered a melanoma cancer therapy.

Internet addiction (IA), as a new and often unrecognized psychosocial disorder, endangers people’s health and their lives. However, the common biometric analysis based on the combination of EEG signals and results of questionnaires is not quantitative, and thus difficult to ensure a specific biomarker. This work aims to develop a deep learning algorithm (no need to identify biomarkers) used for diagnosing IA and evaluating therapy efficacy. Herein, a five-layer CNN model combined with a fast Fourier transform is proposed to diagnose IA quantitatively. This algorithm is validated in the Lemon dataset by using it to process raw data, full spectral power, and alpha-beta-gamma spectral power (related to IA). In contrast to alpha-beta-gamma spectral power, the results based on full spectral power show better performance (87.59% accuracy, 88.80% sensitivity, and 86.41% specificity), which confirms that the proposed algorithm can diagnose IA without biomarkers. In addition, this proposed CNN model presents obvious advantages in processing raw data, achieving 81.1% accuracy. Such results verify that this method can contribute to the reduction of diagnosis time and be potentially used in real-time health monitoring systems. This work provides a quantitative approach to diagnose IA and evaluate therapy efficacy, as a general strategy, and can be widely used in other disorder diagnoses that affect EEG signals, such as psychiatric disorders, substance dependence, and depression.

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.

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.

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.

Highlights • Summarizes photoacoustic imaging for monitoring of stroke diseases. • Focuses on the potential photoacoustic systems for human monitoring. • Reports the challenges of stroke monitoring in the human brain., 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.

Previous studies on determinants of flight delays have been limited to the attributes of departure and arrival airports and routes and experiences in North America and Europe. This research extends the existing discussions by considering a network perspective to explore East Asian airports. A total of 4611 routes among 318 airports in the second half of 2017 are analyzed using panel data regressions. Results show that the attributes of airports and routes linked with the departure and arrival airports of a flight are the essential determinants of flight delays in East Asia. However, the congestion internalization and hubness effect of departure and arrival airports suggested in previous research are not observed in this region. The associations of slot control levels and route attributes with delays are varying between airports inside and outside China and domestic and international routes. This research supplements a new perspective to understand the determinants of flight delays in existing literature and provides a rarely observed knowledge to encourage East Asian airport operators and carriers developing management strategies for minimizing flight delays.

Osteomyelitis caused by orthopedic and cranial implants infection is the principal cause of implant failure, septicemia, and even death; it is an urgent issue with prevalence rates ranging from 4% to 10% in industrialized countries. Bioactive and multifunctional materials such as bioglass, biometal, and biopolymer are commonly used in biomedical fields; however, it is still a challenge to fabricate effective antibacterial coatings. The purpose of this study is to use layer-by-layer technology to develop an antibacterial coating with outer and inner portions for short/long-term release of antibacterial agents. Antibiotics would be released immediately after implant and antibacterial silver ions in the long term. In this study, polyelectrolyte multilayers (PEMs) comprised of collagen as the cationic layers and γ-poly-glutamic acid as the anionic layers were coated onto the 316L stainless steel substrate via a spin coating technique. A chitosan buffer layer was designed and placed in the middle of the coating. 1 mol% silver additive 58S bioactive glass (SiO2: CaO: P2O5 = 60: 36: 4 in mol%) was introduced into the inner γ-poly-glutamic acid layers, while gentamicin was added to the outer layer. Herein, it is demonstrated that bioactive glass was successfully incorporated into the PEM coatings which had a total thickness of about 61.4 ± 2.8 μm (via SEM) and good hydrophilicity with a contact angle of 34.91°. The PEMs had a hardness of 0.18 ± 0.02 GPa, Young's modulus of 3.85 ± 0.32 GPa, and roughness of 394.7 nm. In the short-term, the PEM coatings would release gentamicin and silver ions, while the long-term controlled release of silver ions served as an antibacterial. The PEM coatings were not only effective as an antibacterial, as seen in the zone of inhibition test, but also biocompatible with rat bone marrow mesenchymal stem cells (BMSCs), as seen in the MTT assay. Therefore, we believe that this technology will have great potential in the surface modification of orthopedic and cranial implants due to its antibacterial, tissue restoration, and osseointegration properties.

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.

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.

Wet gel electrodes are widely used for ECG/EEG monitoring, their low impedance results in high-quality signals. But they have important drawbacks too, such as time-consuming electrode set-up for EEG followed by a painful removal, skin irritation by the gel and signal degradation due to gel drying. Hence various dry electrode types are investigated, such as hard metal electrodes with low impedance but limited patient comfort/safety. We focus on flexible conductive polymer-based electrodes to combine low impedance, user comfort and safety. The composition of the conductive polymers is optimized to improve various properties such as conductivity, which directly affects signal quality and sensitivity to motion artifacts, and mechanical properties of the electrodes, important with respect to patient comfort. Electrode impedance and ECG/EEG signal recordings are evaluated using various polymer compositions and compared to wet gel electrode results. Additive optimization to improve processability of the conductive formulations is performed by dedicated flow studies, and will result in a high electrode fabrication yield. Very promising results are obtained regarding impedance, EEG/ECG signal quality and user comfort.

Current EEG research approaches are focusing on developing new dry electrode EEG (electroencephalogram) systems providing a high enough signal quality for a wide range of applications. This study proposes several parameters for evaluating signal quality of dry electrodes and relates the results to skin-electrode contact impedance magnitude values. The EEG recordings of a Ag/AgCl pinned electrode and a flexible polymer pinned electrode are evaluated through a comparison to conductive gel electrode recordings. The experimental setup was made up of two EEG acquisition systems connected in parallel. The protocol included open eyes, closed eyes and steady-state visually evoked potentials (SSVEP) sessions in both seated and walking in place conditions. The parameters used for evaluation were signal correlations, signal coherence and signal-to-noise ratios (SNRs). Results showed that the three proposed parameters provided equivalent outcome for signal quality estimation for the same recordings. There was no relation reported between the defined signal quality and the skin-electrode contact impedance in either dry or gel electrodes, although high impedance variations were present among subjects. However, larger impedance magnitude and impedance magnitude variations, and lower signal quality is observed for dry electrodes compared to gel ones.

Soft, comfortable polymer-based dry electrodes are fabricated. Impedance and biopotential measurements are carried out to compare the performance of conventional gel electrodes with our dry electrodes. The impedance of our dry electrodes is reduced by adding more conductive additives to the polymer material. To further lower the impedance, two skin pretreatment techniques are evaluated regarding their influence on skin impedance. However, these techniques are found to have only temporary beneficial effects. Finally biopotential measurements (both ECG and EEG) are performed using our soft polymer electrodes. The ECG signal acquired with both gel and our polymer electrodes demonstrates high degree of similarity. Therefore, heart beat detection is straightforward. To enable monitoring of EEG signals with smaller amplitudes, our dry electrodes need to be combined with pre-amplifiers. Initial EEG tests show that the alpha waves are clearly identifiable with the dry electrodes when subjects close their eyes. Based on the results, combining with sophisticated signal acquisition electronics, the dry electrodes provide a high user comfort solution for high quality biopotential measurements, even on very hairy skin.

Over the past years, there has been a lot of efforts on the development of wireless sensor networks. However, the major constraint on broad application of wireless sensor network is still the battery lifetime on the wireless sensor nodes. In this paper, a complete power efficient sensor network system will be presented, and energy harvesting from ambient vibrations to further extend battery lifetime will also be detailed. The consideration on the power efficient design will take all aspects from hardware to network protocols. A tunable resonant frequency power harvesting device in cantilever beam form which will shift its resonant frequency to match that of the external vibrations adapting to the random ambient vibrations will also be presented to gain high energy harvesting efficiency from random ambient vibrations. With all the power saving and the power harvesting features implemented in the wireless sensor network design, years of battery lifetime on the sensor node could be expected. The experiment verification of the system and also scenario analysis on battery lifetime will also be presented in this paper.