Search

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.

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.

Abstract 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.

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.

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.

Abstract 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.

Platelets have unique properties that can be used in a variety of clinical settings. Their ability to target and accumulate in the lung cancer microenvironment can be exploited as a therapeutic targeted delivery method. In this study, we developed a drug-free biomimetic platelet-polyethyleneimine polypyrrole (PLT-PEI-PPY) conjugate that expresses epidermal growth factor (EGF) as a novel receptor-mediated material for efficient photothermal near-infrared (NIR) irradiation-hyperthermia and lung tumor ablation therapy. A neutral charge, photothermal response, and serum stability were discovered to be unique physiochemical features of this material. The PLT-PEI-PPY nanoparticles selectively aggregated in lung cancer cells in vitro and in vivo and had an anticancer impact via EGF receptor-mediated cancer cell endocytosis. In vivo, systemic injection of the platelet-PPY nanomaterial into mice was found to be safe and could fool the innate macrophage recognition system allowing to extend the residence time, and to improve the distribution to lung tumor tissues and the photothermal therapeutic efficacy under NIR illumination. By attaching photothermally responsive PEI-PPY nanoparticles to platelet membranes and exposing them to NIR irradiation, targeted ablation of lung malignancies was made possible. This nanoformulation, free of hazardous anticancer drugs, demonstrated a safer and more-effective bio-inspired photothermal-driven ablation therapy for lung cancer.

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.

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.

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.

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.

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.

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.

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.

Diatoms, efficient carbon capture organisms, contribute to 20% of global carbon fixation and 40% of ocean primary productivity, garnering significant attention to their growth. Despite their significance, the synthesis mechanism of polyamines (PAs), especially spermidine (Spd), which are crucial for growth in various organisms, remains unexplored in diatoms. This study reveals the vital role of Spd, synthesized through the spermidine synthase (SDS)-based pathway, in the growth of the diatom Phaeodactylum tricornutum. PtSDS1 and PtSDS2 in the P. tricornutum genome were confirmed as SDS enzymes through enzyme-substrate selectivity assays. Their distinct activities are governed primarily by the Y79 active site. Overexpression of a singular gene revealed that PtSDS1, PtSDS2, and PtSAMDC from the SDS-based synthesis pathway are all situated in the cytoplasm, with no significant impact on PA content or diatom growth. Co-overexpression of PtSDS1 and PtSAMDC proved essential for elevating Spd levels, indicating multifactorial regulation. Elevated Spd content promotes diatom growth, providing a foundation for exploring PA functions and regulation in diatoms.