Multiple Dipole Source Position and Orientation Estimation Using Non-Invasive EEG-like Signals

Salient Arithmetic Data Extraction from Brain Activity via an Improved Deep Network

Interpretation of neural activity in response to stimulations received from the surrounding environment is necessary to realize automatic brain decoding. Analyzing the brain recordings corresponding to visual stimulation helps to infer the effects of perception occurring by vision on brain activity. In this paper, the impact of arithmetic concepts on vision-related brain records has been considered and an efficient convolutional neural network-based generative adversarial network (CNN-GAN) is proposed to map the electroencephalogram (EEG) to salient parts of the image stimuli. The first part of the proposed network consists of depth-wise one-dimensional convolution layers to classify the brain signals into 10 different categories according to Modified National Institute of Standards and Technology (MNIST) image digits. The output of the CNN part is fed forward to a fine-tuned GAN in the proposed model. The performance of the proposed CNN part is evaluated via the visually provoked 14-channel MindBigData recorded by David Vivancos, corresponding to images of 10 digits. An average accuracy of 95.4% is obtained for the CNN part for classification. The performance of the proposed CNN-GAN is evaluated based on saliency metrics of SSIM and CC equal to 92.9% and 97.28%, respectively. Furthermore, the EEG-based reconstruction of MNIST digits is accomplished by transferring and tuning the improved CNN-GAN's trained weights.

Deep Learning for Detecting Multi-Level Driver Fatigue Using Physiological Signals: A Comprehensive Approach

A large share of traffic accidents is related to driver fatigue. In recent years, many studies have been organized in order to diagnose and warn drivers. In this research, a new approach was presented in order to detect multi-level driver fatigue. A multi-level driver tiredness diagnostic database based on physiological signals including ECG, EEG, EMG, and respiratory effort was developed for this aim. The EEG signal was used for processing and other recorded signals were used to confirm the driver's fatigue so that fatigue was not confirmed based on self-report questionnaires. A customized architecture based on adversarial generative networks and convolutional neural networks (end-to-end) was utilized to select/extract features and classify different levels of fatigue. In the customized architecture, with the objective of eliminating uncertainty, type 2 fuzzy sets were used instead of activation functions such as Relu and Leaky Relu, and the performance of each was investigated. The final accuracy obtained in the three scenarios considered, two-level, three-level, and five-level, were 96.8%, 95.1%, and 89.1%, respectively. Given the suggested model's optimal performance, which can identify five various levels of driver fatigue with high accuracy, it can be employed in practical applications of driver fatigue to warn drivers.

Polyvinyl Alcohol (PVA)-Based Nanoniosome for Enhanced in vitro Delivery and Anticancer Activity of Thymol

Introduction

There is an unmet need to develop potent therapeutics against cancer with minimal side effects and systemic toxicity. Thymol (TH) is an herbal medicine with anti-cancer properties that has been investigated scientifically. This study shows that TH induces apoptosis in cancerous cell lines such as MCF-7, AGS, and HepG2. Furthermore, this study reveals that TH can be encapsulated in a Polyvinyl alcohol (PVA)-coated niosome (Nio-TH/PVA) to enhance its stability and enable its controlled release as a model drug in the cancerous region.

Materials and Methods

TH-loaded niosome (Nio-TH) was fabricated and optimized using Box-Behnken method and the size, polydispersity index (PDI) and entrapment efficiency (EE) were characterized by employing DLS, TEM and SEM, respectively. Additionally, in vitro drug release and kinetic studies were performed. Cytotoxicity, antiproliferative activity, and the mechanism were assessed by MTT assay, quantitative real-time PCR, flow cytometry, cell cycle, caspase activity evaluation, reactive oxygen species investigation, and cell migration assays.

Results

This study demonstrated the exceptional stability of Nio-TH/PVA at 4 °C for two months and its pH-dependent release profile. It also showed its high toxicity on cancerous cell lines and high compatibility with HFF cells. It revealed the modulation of Caspase-3/Caspase-9, MMP-2/MMP-9 and Cyclin D/ Cyclin E genes by Nio-TH/PVA on the studied cell lines. It confirmed the induction of apoptosis by Nio-TH/PVA in flow cytometry, caspase activity, ROS level, and DAPI staining assays. It also verified the inhibition of metastasis by Nio-TH/PVA in migration assays.

Conclusion

Overall, the results of this study revealed that Nio-TH/PVA may effectively transport hydrophobic drugs to cancer cells with a controlled-release profile to induce apoptosis while exhibiting no detectable side effects due to their biocompatibility with normal cells.

3D-printing-assisted synthesis of paclitaxel-loaded niosomes functionalized by cross-linked gelatin/alginate composite: Large-scale synthesis and in-vitro anti-cancer evaluation

Breast cancer is one of the most lethal cancers, especially in women. Despite many efforts, side effects of anti-cancer drugs and metastasis are still the main challenges in breast cancer treatment. Recently, advanced technologies such as 3D-printing and nanotechnology have created new horizons in cancer treatment. In this work, we report an advanced drug delivery system based on 3D-printed gelatin-alginate scaffolds containing paclitaxel-loaded niosomes (Nio-PTX@GT-AL). The morphology, drug release, degradation, cellular uptake, flow cytometry, cell cytotoxicity, migration, gene expression, and caspase activity of scaffolds, and control samples (Nio-PTX, and Free-PTX) were investigated. Results demonstrated that synthesized niosomes had spherical-like, in the range of 60-80 nm with desirable cellular uptake. Nio-PTX@GT-AL and Nio-PTX had a sustained drug release and were biodegradable. Cytotoxicity studies revealed that the designed Nio-PTX@GT-AL scaffold had <5 % cytotoxicity against non-tumorigenic breast cell line (MCF-10A) but showed 80 % cytotoxicity against breast cancer cells (MCF-7), which was considerably more than the anti-cancer effects of control samples. In migration evaluation (scratch-assay), approximately 70 % reduction of covered surface area was observed. The anticancer effect of the designed nanocarrier could be attributed to gene expression regulation, where a significant increase in the expression and activity of genes promoting apoptosis (CASP-3, CASP-8, and CASP-9) and inhibiting metastasis (Bax, and p53) and a remarkable decrease in metastasis-enhancing genes (Bcl2, MMP-2, and MMP-9) were observed. Also, flow cytometry results declared that Nio-PTX@GT-AL reduced necrosis and increased apoptosis considerably. The results of this study prove that employing 3D-printing and niosomal formulation is an effective approach in designing nanocarriers for efficient drug delivery applications.