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Monitoring level of hypnosis is a major ongoing challenge for anesthetists to reduce anesthetic drug consumption, avoiding intraoperative awareness and prolonged recovery. This paper proposes a novel automated method for accurate assessing of the level of hypnosis with sevoflurane in 17 patients using the electroencephalogram signal. In this method, a set of distinctive features and a hierarchical classification structure based on support vector machine (SVM) methods, is proposed to discriminate the four levels of anesthesia (awake, light, general and deep states). The first stage of the hierarchical SVM structure identifies the awake state by extracting Shannon Permutation Entropy, Detrended Fluctuation Analysis and frequency features. Then deep state is identified by extracting the sample entropy feature; and finally light and general states are identified by extracting the three mentioned features of the first step. The accuracy of the proposed method of analyzing the brain activity during anesthesia is 94.11%; which was better than previous studies and also a commercial monitoring system (Response Entropy Index).

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.

Iron-borosilicate magnetic composites could be applied as a soft magnetic material in high temperature and high frequency applications. In this research, the magnetic properties of soft magnetic composites with different iron particle sizes made by spark plasma sintering have been investigated. Different magnetic properties such as permeability, loss factor, and quality factor were examined up to frequencies in the order of kilohertz. The microstructural observations indicated the distribution of borosilicate on the iron grain boundaries. The results revealed that the loss factor is smaller for composites with fine particles at high frequencies. In addition, the magnetic impedance for smaller particles was greater. It was also found that the permeability and quality factor of composites with coarse particles are larger than those of fine particles. Indeed, when the particles become coarse, the density of porosities and consequently, the demagnetizing fields decrease which result in the increase of permeability. Furthermore, when the size of particles reduces, the density of grain boundaries enhances which is the main reason of lower loss factor achieved in the composites with fine particles.

One of the prevalent challenges in heart imaging via some 3D medical image modalities like cine-MRI is the large voxel anisotropy in acquired short-axis slices. In fact, due to some of the inherent limitation in the capacity of these imaging systems, segmented images suffer from significant staircase artifact in the direction of the heart long-axis. Therefore reconstructed 3D surfaces from these labeled images are not ideal for other accurate 3D analyses and inter-slice interpolation is needed to generate new slices between existing ones. The purpose of this paper is to generate 3D Spherical Harmonic (SH) surface of the heart left ventricle (LV) using anisotropic resolution images and assessment this claim that these SH surfaces are included of interpolated slices with good approximations. So the main contribution of this paper is to demonstrate the SPHARM robustness and ability to generate a smooth surface of the left ventricle without abrupt changes in edges nevertheless keeps the anatomical details and without production of over smooth interpolated shape.

Several studies have already assessed brain network variations in multiple sclerosis (MS) patients and healthy controls (HCs). The underlying neural system's functioning is apparently too complicated, however. Therefore, the neural time series' analysis through new methods is the aim of any recent research. Functional magnetic resonance imaging (fMRI) is a prominent modality for investigating the human brain's neural substrate, especially when cognitive impairment occurs. The present study was an attempt to investigate the brain network's differences between MS patients and HCs using graph-theoretic measures constructed by an effective connectivity measure through statistical tests. The results of the significant measures were then evaluated through machine learning methods. To this end, we gathered blood-oxygen level dependent (BOLD) fMRI data of the participants during the execution of paced auditory serial addition test (PASAT). Granger causality analysis (GCA) was then employed between brain regions' time series on each subject in order to construct a brain network. Afterward, the Wilcoxon rank-sum test was implemented to find the alteration of brain networks between the mentioned groups. According to the results, Global flow coefficient was significantly different between HCs and patients. Moreover, MS disease impacted several areas of the brain including Hippocampus, Para Hippocampal, Thalamus, Cuneus, Superior temporal gyrus, Heschl, Caudate, Medial Frontal Superior Gyrus, Fusiform, Pallidum, and several parts of Cerebellum in centrality measures and local flow coefficient. Most of the obtained regions were related to the cognitive impacts of the disease. We also found the best subset of graph features by means of Fisher score, and classified them to evaluate the features strength for the discrimination of MS patients from HCs via several machine learning methods. Having used the combination of Wilcoxon rank-sum test and Fisher score, we were able to classify MS patients from HCs using linear support vector machine (SVM) with an accuracy of 95%. With regard to the few existing studies on brain network of MS patients, especially during a cognitive task execution, our findings showed that the selected graph measures by Wilcoxon rank-sum test and Fisher score from the GCA-based brain networks resulted in a promising classification accuracy.

Background: Depression is a major disabling factor around the world. Most of the mental disorders immediately affect the inner mood and cognitive aspect of individuals. Therefore, hiding the depression can delay the early diagnosis and, consequently, a proper treatment in the patients. Objectives: We designed the study to determine the association between rheumatoid diseases and depression. Methods: This is a descriptive - analytic study which was performed on 354 patients referred to Rheumatology Clinic of Ahvaz, a province in south west of Iran in 2016. Then demographic information, depression, quality of life and social support were completed based on standardized questionnaires filled by the patients. Results: The prevalence of depression in rheumatoid arthritis (RA), systemic lupus erythematous (SLE) and systemic sclerosis (SSc) patients was 61.63%, 73.52% and 60%, respectively. There is a meaningful relationship between depression, age and duration of disease in SSc patients. There is a significant relationship between depression and gender in SLE and RA patients. There is a significant relationship between depression and the degree of education of SLE patients. There is a meaningful relationship between depression and quality of life, social support and corticosteroid consumption. Conclusions: The prevalence of depression is very high in all three diseases, which is equal in terms of quality of life, social support and corticosteroid usage in all three diseases. Psychiatric disorders such as depression are common in rheumatic patients.

Curve of left ventricular (LV) volume changes throughout the cardiac cycle is a fundamental parameter for clinical evaluation of various cardiovascular diseases. Currently, this evaluation is often performed manually which is tedious and time consuming and suffers from significant interobserver and intraobserver variability. This paper introduces a new automatic method, based on nonlinear dimensionality reduction (NLDR) for extracting the curve of the LV volume changes over a cardiac cycle from two-dimensional (2-D) echocardiography images. Isometric feature mapping (Isomap) is one of the most popular NLDR algorithms. In this study, a modified version of Isomap algorithm, where image to image distance metric is computed using nonrigid registration, is applied on 2-D echocardiography images of one cycle of heart. Using this approach, the nonlinear information of these images is embedded in a 2-D manifold and each image is characterized by a symbol on the constructed manifold. This new representation visualizes the relationship between these images based on LV volume changes and allows extracting the curve of the LV volume changes automatically. Our method in comparison to the traditional segmentation algorithms does not need any LV myocardial segmentation and tracking, particularly difficult in the echocardiography images. Moreover, a large data set under various diseases for training is not required. The results obtained by our method are quantitatively evaluated to those obtained manually by the highly experienced echocardiographer on ten healthy volunteers and six patients which depict the usefulness of the presented method.

Marjan Mokhtare,Vida Bozorgi, Shahram Agah,Mehdi Nikkhah,Amirhossein Faghihi,Amirhossein Boghratian,Neda Shalbaf,Abbas Khanlari,Hamidreza Seifmanesh Colorectal Research Center, Rasoul Akram Hospital, Tehran, Iran Background: Various risk scoring systems have been recently developed to predict clinical outcomes in patients with upper gastrointestinal bleeding (UGIB). The two commonly used scoring systems include full Rockall score (RS) and the Glasgow-Blatchford score (GBS). Bleeding scores were assessed in terms of prediction of clinical outcomes in patients with UGIB. Patients and methods: Two hundred patients (age >18 years) with obvious symptoms of UGIB in the emergency department of Rasoul Akram Hospital were enrolled. Full RS and GBS were calculated. We followed the patients for records of rebleeding and 1-month mortality. Areceiver operating characteristic curve by using areas under the curve (AUCs) was used to statistically identify the best cutoff point. Results: Eighteen patients were excluded from the study due to failure to follow-up. Rebleeding and mortality rate were 9.34% (n=17) and 11.53% (n=21), respectively. Regarding 1-month mortality, full RS was better than GBS (AUC, 0.648 versus 0.582; P=0.021). GBS was more accurate in terms of detecting transfusion need (AUC, 0.757 versus 0.528; P=0.001), rebleeding rate (AUC, 0.722 versus 0.520; P=0.002), intensive care unit admission rate (AUC, 0.648 versus 0.582; P=0.021), and endoscopic intervention rate (AUC, 0.771 versus 0.650; P

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.

Background: Biomarkers that predict clinical outcomes in depression are essential for increasing the precision of treatments and clinical outcomes. The electroencephalogram (EEG) is a non-invasive neurophysiological test that has promise as a biomarker sensitive to treatment effects. The aim of our study was to investigate a novel non-linear index of resting state EEG activity as a predictor of clinical outcome, and compare its predictive capacity to traditional frequency-based indices. Methods: EEG was recorded from 62 patients with treatment resistant depression (TRD) and 25 healthy comparison (HC) subjects. TRD patients were treated with excitatory repetitive transcranial magnetic stimulation (rTMS) to the dorsolateral prefrontal cortex (DLPFC) for 4 to 6 weeks. EEG signals were first decomposed using the empirical mode decomposition (EMD) method into band-limited intrinsic mode functions (IMFs). Subsequently, Permutation Entropy (PE) was computed from the obtained second IMF to yield an index named PEIMF2. Receiver Operator Characteristic (ROC) curve analysis and ANOVA test were used to evaluate the efficiency of this index (PEIMF2) and were compared to frequency-band based methods. Results: Responders (RP) to rTMS exhibited an increase in the PEIMF2 index compared to non-responders (NR) at F3, FCz and FC3 sites (p < 0.01). The area under the curve (AUC) for ROC analysis was 0.8 for PEIMF2 index for the FC3 electrode. The PEIMF2 index was superior to ordinary frequency band measures. Conclusion: Our data show that the PEIMF2 index, yields superior outcome prediction performance compared to traditional frequency band indices. Our findings warrant further investigation of EEG-based biomarkers in depression; specifically entropy indices applied in band-limited EEG components. Registration in ClinicalTrials.Gov; identifiers NCT02800226 and NCT01887782.

Despite the role of agency in schools, few researchers have addressed the issue. The present study aims to analyze the relationship between agentic engagement, basic psychological needs, and test anxiety by using structural equation modeling. For this purpose, 289 female students in math-physics and basic sciences were selected as the samples by using multistage cluster sampling. Reeve and Tseng’s aspects of students’ engagement during learning activity, La Guardia’s et al. basic psychological needs, and Ahvaz test anxiety scale were used as data collection tools. The results of structural equation modeling indicated that agentic engagement positively influenced the basic psychological needs such as autonomy, competence, and relatedness while it could negatively affect test anxiety with the mediatory role of basic psychological needs. In conclusion, agentic engagement can be regarded as a critical variable in affecting the basic psychological needs and reducing test anxiety.

Photothermal therapy with various nanoparticles, as photothermal transducers, is a widely researched technique. A continuous wave (CW) laser is employed during this procedure. The therapeutic setup is slightly modified to measure the optical absorption cross-section of the graphene oxide (GO), by mitigating the effects of heat diffusion and light scattering. With an 808-nm CW laser setup modulated by a waveform modulation setup, the effect of nanoparticle size and composition of GO in water on optical absorption cross section is characterized.

Characterizing brain dynamics during anesthesia is a main current challenge in anesthesia study. Several single channel electroencephalogram (EEG)-based commercial monitors like the Bispectral index (BIS) have suggested to examine EEG signal. But, the BIS index has obtained numerous critiques. In this study, we evaluate the concentration-dependent effect of the propofol on long-range frontal-temporal synchronization of EEG signals collected from eight subjects during a controlled induction and recovery design. We used order patterns cross recurrence plot and provide an index named order pattern laminarity (OPL) to assess changes in neuronal synchronization as the mechanism forming the foundation of conscious perception. The prediction probability of 0.9 and 0.84 for OPL and BIS specified that the OPL index correlated more strongly with effect-site propofol concentration. Also, our new index makes faster reaction to transients in EEG recordings based on pharmacokinetic and pharmacodynamic model parameters and demonstrates less variability at the point of loss of consciousness (standard deviation of 0.04 for OPL compared with 0.09 for BIS index). The result show that the OPL index can estimate anesthetic state of patient more efficiently than the BIS index in lightly sedated state with more tolerant of artifacts.

Retinal prosthesis has been proposed to restore vision for those suffering from the retinal pathologies that mainly affect the photoreceptors layer but keep the inner retina intact. Prior to costly risky experimental studies computational modelling of the retina will help to optimize the device parameters and enhance the outcomes. Here, we developed an anatomically detailed computational model of the retina based on OCT data sets. The consecutive OCT images of individual were subsequently segmented to provide a 3D representation of retina in the form of finite elements. Thereafter, the electrical properties of the retina were modelled by implementing partial differential equation on the 3D mesh. Different electrode configurations, that is bipolar and hexapolar configurations, were implemented and the results were compared with the previous computational and experimental studies. Furthermore, the possible effects of the curvature of retinal layers on the current steering through the retina were proposed an...

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.

ObjectiveWhen faced with clinical symptoms of scarring alopecia—the standard diagnostic pathway involves a scalp biopsy which is an invasive and expensive procedure. This project aimed to assess if plucked hair follicles (HFs) containing living epithelial cells can offer a non-invasive approach to diagnosing inflammatory scalp lesions.MethodsLesional and non-lesional HFs were extracted from the scalp of patients with chronic discoid lupus erythematosus (CDLE), psoriasis and healthy controls. RNA was isolated from plucked anagen HFs and microarray, as well as quantitative real-time PCR was performed.ResultsHere, we report that gene expression analysis of only a small number of HF plucked from lesional areas of the scalp is sufficient to differentiate CDLE from psoriasis lesions or healthy HF. The expression profile from CDLE HFs coincides with published profiles of CDLE from skin biopsy. Genes that were highly expressed in lesional CDLE corresponded to well-known histopathological diagnostic features of CDLE and included those related to apoptotic cell death, the interferon signature, complement components and CD8+ T-cell immune responses.ConclusionsWe therefore propose that information obtained from this non-invasive approach are sufficient to diagnose scalp lupus erythematosus. Once validated in routine clinical settings and compared with other scarring alopecias, this rapid and non-invasive approach will have great potential for paving the way for future diagnosis of inflammatory scalp lesions.

Identification and classification of left ventricular (LV) regional wall motion (RWM) abnormalities on echocardiograms has fundamental clinical importance for various cardiovascular disease assessments especially in ischemia. In clinical practice, this evaluation is still performed visually which is highly dependent on training and experience of the echocardiographers and therefore suffers from significant interobserver and intraobserver variability. This paper presents a new automatic technique, based on nonrigid image registration for classifying the RWM of LV in a three-point scale. In this algorithm, we register all images of one cycle of heart to a reference image (end-diastolic image) using a hierarchical parametric model. This model is based on an affine transformation for modeling the global LV motion and a B-spline free-form deformation transformation for modeling the local LV deformation. We consider image registration as a multiresolution optimization problem. Finally, a new regional quantitative index based on resultant parameters of the hierarchical transformation model is proposed for classifying RWM in a three-point scale. The results obtained by our method are quantitatively evaluated to those obtained by two experienced echocardiographers visually as gold standard on ten healthy volunteers and 14 patients (two apical views) and resulted in an absolute agreement of 83 % and a relative agreement of 99 %. Therefore, this diagnostic system can be used as a useful tool as well as reference visual assessment to classify RWM abnormalities in clinical evaluation.

The onset of thermal natural convection in a horizontal multilayer system consisting of a homogeneous porous layer sandwiched between two fluid layers has been simulated by an accurate numerical method. The porous and fluid layers include uniform heat sources. Flow in the porous medium has been governed by Darcy–Brinkman’s law. On the other hand, the Navier–Stokes equations with the Boussinesq approximation have ruled over the clear fluid layers. The lower and upper rigid surfaces are assumed to be fixed at the equal temperatures TL and TU. The eigenvalues and eigenfunctions of the linear stability analysis have been solved by utilizing the compound matrix method (CMM). The CMM reaches accurate results in a very efficient manner. Moreover, the method removes the stiffness from the equations of the stability system. The results indicate that the onset of convection and the nature of convection cells depend on the relative depths of layers. It has been observed that the thickness of the lower fluid layer increases the critical Rayleigh number of the upper fluid layer and stabilizes it.

A continuum bidomain model of sub-retinal electrical stimulation on an anatomically detailed mesh of retina is presented. The underlying geometry is made up of 256 B-scans of optical coherence tomography (OCT) images of a healthy human retina, covering approximately 6×2 mm(2) centered on the macula. The OCT images are initially segmented and digitized into five major retinal layers comprising passive and active retinal cell types. This computational mesh is then used to model a subretinal hexapolar biphasic electrical stimulation. Our results indicate that the ultra-structure of the retina results in an asymmetric spatial extracellular potential distribution, leading to an irregular pattern of retinal ganglion cell activation. This finding is in contrast to focal circular activation previously reported in retinal electrical stimulation modeling with a uniform mesh.

The aim of this study is to evaluate the efficiency of applying a new non-rigid image registration method on two-dimensional echocardiographic images for computing the left ventricle (LV) myocardial motion field over a cardiac cycle. The key feature of our method is to register all images in the sequence to a reference image (end-diastole image) using a hierarchical transformation model, which is a combination of an affine transformation for modeling the global LV motion and a free-form deformation (FFD) transformation based on B-splines for modeling the local LV deformation. Registration is done by minimizing a cost function associated with the image similarity based on a global pixel-based matching and the smoothness of transformation. The algorithm uses a fast and robust optimization strategy using a multiresolution approach for the estimation of parameters of the deformation model. The proposed algorithm is evaluated for calculating the displacement curves of two expert-identified anatomical landmarks in apical views of the LV for 10 healthy volunteers and 14 subjects with pathology. The proposed algorithm is also evaluated for classifying the regional LV wall motion abnormality using the calculation of the strain value at the end of systole in 288 segments as scored by two consensual experienced echocardiographers in a three-point scale: 1: normokinesia, 2: hypokinesia, and 3: akinesia. Moreover, we compared the results of the proposed registration algorithm to those previously obtained using the other image registration methods. Regarding to the reference two experienced echocardiographers, the results demonstrate the proposed algorithm more accurately estimates the displacement curve of the two anatomical landmarks in apical views than the other registration methods in all data set. Moreover, the p values of the t test for the strain value of each segment at the end of systole measured by the proposed algorithm show higher differences than the other registration method. These differences are between each pair of scores in all segments and in three segments of septum independently. The clinical results show that the proposed algorithm can improve both the calculation of the displacement curve of every point of LV during a cardiac cycle and the classification of regional LV wall motion abnormality. Therefore, this diagnostic system can be used as a useful tool for clinical evaluation of the regional LV function.

Background Monitoring the effect of anesthetic drugs on the neural system is a major ongoing challenge for anesthetists. During the past few years, several electroencephalogram (EEG)-based methods such as the response entropy (RE) as implemented in the Datex-Ohmeda M-Entropy Module have been proposed. In this paper, sample entropy is used to quantify the predictability of EEG series, which could provide an index to show the effect of sevoflurane anesthesia. The dose–response relation of sample entropy is compared with that of RE. Methods EEG data from 21 subjects is collected during the induction of general anesthesia with sevoflurane. The sample entropy is applied to the EEG recording. Pharmacokinetic-pharmacodynamic modeling and prediction probability statistic are used to evaluate the efficiency of sample entropy in comparison with RE. Results Both methods track the gross changes in EEG, especially the occurrence of burst-suppression pattern at high doses of anesthetics. However, our method produces faster reaction to transients in EEG during the induction of anesthesia as indicated from the pharmacokinetic and pharmacodynamic modeled parameters and analysis around the point of loss of consciousness. Also, sample entropy correlated more closely with effect-site sevoflurane concentration than the RE. In addition, our proposed method exhibits greater resistance to noise in the EEG signals. Conclusion The results demonstrate that sample entropy can estimate the sevoflurane drug effect on the EEG more effectively than the commercial RE index with a stronger noise resistance.

Monitoring the effect of anesthetic drugs on the central nervous system is a major ongoing challenge in anesthesia research. A number of electroencephalogram (EEG)-based monitors of the anesthetic drug effect such as the bispectral (BIS) index have been proposed to analyze the EEG signal during anesthesia. However, the BIS index has received some criticism. This paper offers a method based on the Hilbert-Huang transformation to calculate an index, called the Hilbert-Huang weighted regional frequency (HHWRF), to quantify the effect of propofol on brain activity. The HHWRF and BIS indices are applied to EEG signals collected from nine patients during a controlled propofol induction and emergence scheme. The results show that both the HHWRF and BIS track the gross changes in the EEG with increasing and decreasing anesthetic drug effect (the prediction probability P(k) of 0.85 and 0.83 for HHWRF and BIS, respectively). Our new index can reflect the transition from unconsciousness to consciousness faster than the BIS, as indicated from the pharmacokinetic and pharmacodynamic modeled parameters and also from the analysis around the point of reawakening. This method could be used to design a new EEG monitoring system to estimate the propofol anesthetic drug effect.