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Major Depressive Disorder (MDD) is one of the leading causes of disability worldwide. Prediction of response to Selective Serotonin Reuptake Inhibitors (SSRIs) antidepressants in patients with MDD is necessary for preventing side effects of mistreatment. In this study, a deep Transfer Learning (TL) strategy based on powerful pre-trained convolutional neural networks (CNNs) in the big data datasets is developed for classification of Responders and Non-Responders (R/NR) to SSRI antidepressants, using 19-channel Electro-encephalography (EEG) signal acquired from 30 MDD patients in the resting state. Multiple time–frequency images are obtained from each EEG channel using Continuous Wavelet Transform (CWT) for feeding into pre-trained CNN models that are VGG16, Xception, DenseNet121, MobileNetV2 and InceptionResNetV2. Our plan is to adapt and fine-tune the weights of networks to the target task with the small-sized dataset. Finally, to improve the recognition performance, an ensemble method based on majority voting of outputs of five mentioned deep TL architectures has been developed. Results indicate that the best performance among basic models achieved by DenseNet121 with accuracy, sensitivity and specificity of 95.74%, 95.56% and 95.64%, respectively. An Ensemble of these basic models created to surpass the accuracy obtained by each individual basic model. Our experiments show that ensemble model can gain accuracy, sensitivity and specificity of 96.55%, 96.01% and 96.95%, respectively. Therefore, proposed ensemble of TL strategy of pre-trained CNN models based on WT images obtained from EEG signal can be used for antidepressants treatment outcome prediction with a high accuracy.

The Trachyspermum copticum essential oil (TCEO) has been incorporated into niosomes (NIO-TCEO). The chemical composition of the TCEO has been evaluated using Gas chromatography mass spectrometry (GC-MS) and the antioxidant activity of the TCEO has been characterized by radical scavenging capacity (RSC). The niosomes have been characterized with respect to their size, zeta potential, morphology, encapsulation efficiency, in vitro release, and cell toxicity against hepatocellular carcinoma cell line (HepG2). The interaction of the TCEO with niosomes has been studied using Fourier transform infrared spectroscopy (FTIR) and differential scanning calorimetry (DSC). Our study has shown an excellent antioxidant activity of the essential oil with IC50 of 18.32 ± 3.6 μg/ml. The size of the niosomes has been around 151.06 ± 5.3 nm and the TCEO entrapment efficiency has been 87.41 ± 5.97%. Our results have demonstrated that niosomes loaded with TCEO have a potential application for cancer therapy.

Detection of mental disorders such as schizophrenia (SZ) through investigating brain activities recorded via Electroencephalogram (EEG) signals is a promising field in neuroscience. This study presents a hybrid brain effective connectivity and deep learning framework for SZ detection on multichannel EEG signals. First, the effective connectivity matrix is measured based on the Transfer Entropy (TE) method that estimates directed causalities in terms of brain information flow from 19 EEG channels for each subject. Then, TE effective connectivity elements were represented by colors and formed a 19 × 19 connectivity image which, simultaneously, represents the time and spatial information of EEG signals. Created images are used to be fed into the five pre-trained Convolutional Neural Networks (CNN) models named VGG-16, ResNet50V2, InceptionV3, EfficientNetB0, and DenseNet121 as Transfer Learning (TL) models. Finally, deep features from these TL models equipped with the Long Short-Term Memory (LSTM) model for the extraction of most discriminative spatiotemporal features are used to classify 14 SZ patients from 14 healthy controls. Results show that the hybrid framework of pre-trained CNN-LSTM models achieved higher accuracy than pre-trained CNN models. The highest average accuracy and F1-score were achieved using the EfficientNetB0-LSTM model through the 10-fold cross-validation method equal to 99.90% and 99.93%, respectively. Therefore, the superior performance of the hybrid framework of brain effective connectivity images from EEG signals and pre-trained CNN-LSTM models show that the proposed method is highly capable of detecting SZ patients from healthy controls.

EMG signals have played a pivotal role as a fundamental component of myriad modern prostheses to control prostheses’ movements as well as identifying individual and combined hand or finger gestures. Despite a great deal of interest in these signals, the non-stationary nature of biological EMG signals has led to complications in EMG applications. Stationary signals have been analyzed plainly by time domain approaches like Fourier Transform, while non-stationary signals analysis is not satisfactory to be carried out with such method as it is not capable to illustrate the incidence time of various frequency components, besides, extracting both time and frequency information is essential. The Fractional Fourier Transform (FrFT), which is a generalization of classical Fourier Transform, is able to demonstrate the variable frequency of non-stationary signals. In this paper, FrFT technique with different fractional orders is employed as a novel and sophisticated feature extraction method for EMG signals of 8 subjects including 6 men and 2 women recorded in 10 different finger movements which are 5 individual and 5 combined postures. Windowing method and the t-test approach are utilized to select the best FrFT extracted coefficients as features. Employing KNN method to classify 10 different classes and the average classification accuracy of 98.12% of proposed method is a significant indicator of its sufficient performance.

Improving bone regeneration is one of the most pressing problems facing bone tissue engineering (BTE) which can be tackled by incorporating different biomaterials into the fabrication of the scaffolds. The present study aims to apply the 3D-printing and freeze-drying methods to design an ideal scaffold for improving the osteogenic capacity of Dental pulp stem cells (DPSCs). To achieve this purpose, hybrid constructs consisted of 3D-printed Beta-tricalcium phosphate (β-TCP)-based scaffolds filled with freeze-dried gelatin/reduced graphene oxide-Magnesium-Arginine (GRMA) matrix were fabricated through a novel green method. The effect of different concentrations of Reduced graphene oxide-Magnesium-Arginine (RMA) (0, 0.25% and 0.75%wt) on the morphology, mechanical properties, and biological activity of the 3D scaffolds were completely evaluated. Our findings show that the incorporation of RMA hybrid into the scaffold can remarkably enhance its mechanical features and improve cell proliferation and differentiation simultaneously. Of all scaffolds, β-TCP/0.25GRMA showed not only the highest ALP activity and cell proliferation after 14 days but it up-regulated bone-related genes and proteins (COL-I, RUNX2, OCN). Hence, the fabricated 3D printed β-TCP/0.25GRMA porous scaffolds can be considered as a high-potential candidate for BTE.

This paper describes the design of stimuli-sensitive theranostic nanoparticles, composed of reduced graphene oxide (rGO) self-assembled on thermosensitive liposomes encapsulated doxorubicin (DOX) and carbon quantum dot (CQD) (CQD-DOX-rGO-Tlip). The rGO-Tlip particles have been observed to be flower-shaped objects. The thermoresponsive and theranostic potential of CQD-DOX-rGO-Tlips have been studied using differential scanning calorimetry (DSC), ultraviolet visible spectroscopy (UV-Vis), Raman spectroscopy and photoluminescent assays. The chemo-photothermal potential of rGO-Tlip on MD-MB-231 cells during NIR laser irradiation has been examined using MTT assay. Also, the ability of rGO-Tlip to be taken up by MD-MB-231 cells has been studied using confocal microscopy and flowcytometry. The results indicate that CQD-DOX-rGO-Tlips achieve a synergistic effect between photothermal therapy and chemotherapy for cancer treatment. Furthermore, online monitoring drug release is accomplished by studying the emission intensity of CQD while DOX released.

A highly effective and mild protocol for ring opening of epoxides with NH 4 SCN in the presence of catalytic amount of a multi-site phase-transfer catalyst, α,α′,α″-N-hexakis(triethylammoniummethylene chloride)-melamine, is developed. A variety of β-hydroxy thiocyanates as important intermediates in agricultural and pharmaceutical chemistry were obtained in high yields with excellent regioselectivity and in short reaction times.