Brain functional connectivity based on phase lag index of electroencephalography for automated diagnosis of schizophrenia using residual neural networks.

The complexity of symptoms of schizophrenia (SZ) complicate traditional and effective diagnoses based on clinical signs. Moreover, clinical diagnosis of SZ is manual, time-consuming, and error-prone. Thus, there is a requirement to develop automated systems for timely and accurate diagnosis of SZ. This paper proposes an automated SZ diagnosis pipeline based on residual neural networks (ResNet). To exploit the superior image processing capabilities of the ResNet models, multi-channel electroencephalogram (EEG) signals were converted into functional connectivity representations (FCRs). The functional connectivity of multiple regions in the cerebral cortex is critical for a better understanding of the mechanisms of SZ. In creating the FCR input images, the phase lag index (PLI) was calculated based on 16-channel EEG signals from 45 SZ patients and 39 healthy control (HC) subjects to reduce and avoid the volume conduction effect. The experimental results showed that satisfactory classification performance (accuracy = 96.02%, specificity = 94.85%, sensitivity = 97.03%, precision = 95.70%, and F1-score = 96.33%) was achieved by combining FCR inputs of beta oscillatory and the ResNet-50 model. The statistical analyses also confirmed that there is a significant difference between SZ patients and HC subjects (p < 0.001, one-way ANOVA). More specifically, the average connectivity strengths between nodes in the parietal cortex and those in the central, occipital, and temporal regions were significantly reduced in SZ patients compared to HC subjects. Overall results demonstrated that this paper not only provided an automated diagnostic model whose classification performance is superior to most previous studies but also valuable biomarkers for clinical use.

A study for gamma-ray attenuation performances of barite filled polymer composites.

In this study, radiation protection efficiency (RPE) for the coded as UP-Ba0, UP-Ba25, UP-Ba50, UP-Ba75 and UP-Ba100 at different sample thicknesses, total mass attenuation coefficient (µ/ρ), linear attenuation coefficients (µ), half value layers (HVL), tenth value layers (TVL), mean free paths (MFP), effective atomic numbers (Zeff) and effective electron densities (NE) were determined at various gamma energies between 59.5 and 1408.0 keV. With the help of the geometric progression (G-P) fitting method, the energy absorption build-up factor (EABF) and exposure build-up factor (EBF) values were calculated in the energy range from 0.015 MeV to 15 MeV for the produced composites. HPGe detector and eight radioactive sources (241Am, 152Eu, 137Cs, 133Ba, 60Co, 57Co, 54Mn and 22Na) were utilized in the experiment. Experimental results were compared with theoretical calculations and it has been observed that there is a good agreement between theoretical and experimental results. It was observed that RPE, µ/ρ, µ, Zeff and NE parameters increased with increasing barite amount and decreased with increasing energy, while the opposite situation was observed in HVL, TVL and MFP parameters. EABF and EBF values increase with increasing penetration depth. As a result, UP-Ba100 is a good radiation absorber according to the other studied barite filled polymer composites.

Effects of Pharmacological Treatments in Alzheimer's Disease: Permutation Entropy-Based EEG Complexity Study.

Alzheimer's disease (AD) is a neurodegenerative brain disease affecting cognitive and physical functioning. The currently available pharmacological treatments for AD mainly contain cholinesterase inhibitors (AChE-I) and N-methyl-D-aspartic acid (NMDA) receptor antagonists (i.e., memantine). Because brain signals have complex nonlinear dynamics, there has been an increase in interest in researching complexity changes in the time series of brain signals in individuals with AD. In this study, we explore the electroencephalographic (EEG) complexity for making better observation of pharmacological therapy-based treatment effects on AD patients using the permutation entropy (PE) method. We examined EEG sub-band (delta, theta, alpha, beta, and gamma) complexity in de-novo, monotherapy (AChE-I), dual therapy (AChE-I and memantine) receiving AD participants compared with healthy elderly controls. We showed that each frequency band depicts its own complexity profile, which is regionally altered between groups. These alterations were also found to be associated with global cognitive scores. Overall, our findings indicate that entropy measures could be useful to show medication effects in AD.

A modified DeepLabV3+ based semantic segmentation of chest computed tomography images for COVID-19 lung infections.

Coronavirus disease (COVID-19) affects the lives of billions of people worldwide and has destructive impacts on daily life routines, the global economy, and public health. Early diagnosis and quantification of COVID-19 infection have a vital role in improving treatment outcomes and interrupting transmission. For this purpose, advances in medical imaging techniques like computed tomography (CT) scans offer great potential as an alternative to RT-PCR assay. CT scans enable a better understanding of infection morphology and tracking of lesion boundaries. Since manual analysis of CT can be extremely tedious and time-consuming, robust automated image segmentation is necessary for clinical diagnosis and decision support. This paper proposes an efficient segmentation framework based on the modified DeepLabV3+ using lower atrous rates in the Atrous Spatial Pyramid Pooling (ASPP) module. The lower atrous rates make receptive small to capture intricate morphological details. The encoder part of the framework utilizes a pre-trained residual network based on dilated convolutions for optimum resolution of feature maps. In order to evaluate the robustness of the modified model, a comprehensive comparison with other state-of-the-art segmentation methods was also performed. The experiments were carried out using a fivefold cross-validation technique on a publicly available database containing 100 single-slice CT scans from >40 patients with COVID-19. The modified DeepLabV3+ achieved good segmentation performance using around 43.9 M parameters. The lower atrous rates in the ASPP module improved segmentation performance. After fivefold cross-validation, the framework achieved an overall Dice similarity coefficient score of 0.881. The results demonstrate that several minor modifications to the DeepLabV3+ pipeline can provide robust solutions for improving segmentation performance and hardware implementation.

Multi-task semantic segmentation of CT images for COVID-19 infections using DeepLabV3+ based on dilated residual network.

COVID-19 is a deadly outbreak that has been declared a public health emergency of international concern. The massive damage of the disease to public health, social life, and the global economy increases the importance of alternative rapid diagnosis and follow-up methods. RT-PCR assay, which is considered the gold standard in diagnosing the disease, is complicated, expensive, time-consuming, prone to contamination, and may give false-negative results. These drawbacks reinforce the trend toward medical imaging techniques such as computed tomography (CT). Typical visual signs such as ground-glass opacity (GGO) and consolidation of CT images allow for quantitative assessment of the disease. In this context, it is aimed at the segmentation of the infected lung CT images with the residual network-based DeepLabV3+, which is a redesigned convolutional neural network (CNN) model. In order to evaluate the robustness of the proposed model, three different segmentation tasks as Task-1, Task-2, and Task-3 were applied. Task-1 represents binary segmentation as lung (infected and non-infected tissues) and background. Task-2 represents multi-class segmentation as lung (non-infected tissue), COVID (GGO, consolidation, and pleural effusion irregularities are gathered under a single roof), and background. Finally, the segmentation in which each lesion type is considered as a separate class is defined as Task-3. COVID-19 imaging data for each segmentation task consists of 100 CT single-slice scans from over 40 diagnosed patients. The performance of the model was evaluated using Dice similarity coefficient (DSC), intersection over union (IoU), sensitivity, specificity, and accuracy by performing five-fold cross-validation. The average DSC performance for three different segmentation tasks was obtained as 0.98, 0.858, and 0.616, respectively. The experimental results demonstrate that the proposed method has robust performance and great potential in evaluating COVID-19 infection.

Novel Cu/Zn Reinforced Polymer Composites: Experimental Characterization for Radiation Protection Efficiency (RPE) and Shielding Properties for Alpha, Proton, Neutron, and Gamma Radiations.

In this study, brass (Cu/Zn) reinforced polymer composites with different proportions of brass powders were fabricated. Different types of nuclear shielding parameters such as mass and linear attenuation coefficients, radiation protection efficiency, half and tenth value layers, and effective atomic number values were determined experimentally and theoretically in the energy range of 0.060-1.408 MeV in terms of gamma-ray shielding capabilities of fabricated polymer composites. A high Purity Germanium detector (HPGe) in conjunction with a Multi-Channel Analyzer (MCA) and twenty-two characteristic gamma-ray energies have been used in the experimental phase. In addition, the exposure and energy absorption buildup factors of reinforced Cu/Zn composites were calculated, and relative dose distribution values were computed to verify them. Proton mass stopping power (ΨP), proton projected range (ΦP), alpha mass stopping power (ΨA), and alpha projected range (ΦA) parameters, which indicate the interactions of the produced composites with charged particle radiation, were investigated. Fast neutron removal cross-section (ΣR) results were determined to give an idea in terms of neutron shielding. According to the obtained results, it is reported that the CuZn20 coded sample's ability to attenuate gamma-ray and charged particle radiation is more efficient than that of other prepared composites. A CuZn05 coded sample was found to be more suitable for neutron shielding capability.

Automatic detection and localization of COVID-19 pneumonia using axial computed tomography images and deep convolutional neural networks.

COVID-19 was first reported as an unknown group of pneumonia in Wuhan City, Hubei province of China in late December of 2019. The rapid increase in the number of cases diagnosed with COVID-19 and the lack of experienced radiologists can cause diagnostic errors in the interpretation of the images along with the exceptional workload occurring in this process. Therefore, the urgent development of automated diagnostic systems that can scan radiological images quickly and accurately is important in combating the pandemic. With this motivation, a deep convolutional neural network (CNN)-based model that can automatically detect patterns related to lesions caused by COVID-19 from chest computed tomography (CT) images is proposed in this study. In this context, the image ground-truth regarding the COVID-19 lesions scanned by the radiologist was evaluated as the main criteria of the segmentation process. A total of 16 040 CT image segments were obtained by applying segmentation to the raw 102 CT images. Then, 10 420 CT image segments related to healthy lung regions were labeled as COVID-negative, and 5620 CT image segments, in which the findings related to the lesions were detected in various forms, were labeled as COVID-positive. With the proposed CNN architecture, 93.26% diagnostic accuracy performance was achieved. The sensitivity and specificity performance metrics for the proposed automatic diagnosis model were 93.27% and 93.24%, respectively. Additionally, it has been shown that by scanning the small regions of the lungs, COVID-19 pneumonia can be localized automatically with high resolution and the lesion densities can be successfully evaluated quantitatively.

Evaluation of potential auras in generalized epilepsy from EEG signals using deep convolutional neural networks and time-frequency representation.

The general uncertainty of epilepsy and its unpredictable seizures often affect badly the quality of life of people exposed to this disease. There are patients who can be considered fortunate in terms of prediction of any seizures. These are patients with epileptic auras. In this study, it was aimed to evaluate pre-seizure warning symptoms of the electroencephalography (EEG) signals by a convolutional neural network (CNN) inspired by the epileptic auras defined in the medical field. In this context, one-dimensional EEG signals were transformed into a spectrogram display form in the frequency-time domain by applying a short-time Fourier transform (STFT). Systemic changes in pre-epileptic seizure have been described by applying the CNN approach to the EEG signals represented in the image form, and the subjective EEG-Aura process has been tried to be determined for each patient. Considering all patients included in the evaluation, it was determined that the 1-min interval covering the time from the second minute to the third minute before the seizure had the highest mean and the lowest variance to determine the systematic changes before the seizure. Thus, the highest performing process is described as EEG-Aura. The average success for the EEG-Aura process was 90.38 ± 6.28%, 89.78 ± 8.34% and 90.47 ± 5.95% for accuracy, specificity and sensitivity, respectively. Through the proposed model, epilepsy patients who do not respond to medical treatment methods are expected to maintain their lives in a more comfortable and integrated way.

Emotion recognition based on EEG features in movie clips with channel selection.

Emotion plays an important role in human interaction. People can explain their emotions in terms of word, voice intonation, facial expression, and body language. However, brain-computer interface (BCI) systems have not reached the desired level to interpret emotions. Automatic emotion recognition based on BCI systems has been a topic of great research in the last few decades. Electroencephalogram (EEG) signals are one of the most crucial resources for these systems. The main advantage of using EEG signals is that it reflects real emotion and can easily be processed by computer systems. In this study, EEG signals related to positive and negative emotions have been classified with preprocessing of channel selection. Self-Assessment Manikins was used to determine emotional states. We have employed discrete wavelet transform and machine learning techniques such as multilayer perceptron neural network (MLPNN) and k-nearest neighborhood (kNN) algorithm to classify EEG signals. The classifier algorithms were initially used for channel selection. EEG channels for each participant were evaluated separately, and five EEG channels that offered the best classification performance were determined. Thus, final feature vectors were obtained by combining the features of EEG segments belonging to these channels. The final feature vectors with related positive and negative emotions were classified separately using MLPNN and kNN algorithms. The classification performance obtained with both the algorithms are computed and compared. The average overall accuracies were obtained as 77.14 and 72.92% by using MLPNN and kNN, respectively.

[The effect of ligustrazin in intestinal ischemia reperfusion injury generated on rats]. / Siçanlarda olusturulan ince bagirsak iskemi reperfüzyon hasarinda ligustrazin'in etkisi.

BACKGROUND: Intestinal ischemia is a serious and common clinical status. It develops as result of superior mesenteric artery (SMA) obstruction caused by many etiologic factors. Sepsis and multiple organ failure could develop following intestinal ischemia. The present study aimed to investigate the effects of ligustrazin, which has a vasodilator impact on intestinal ischemia. METHODS: Forty male Wistar rats were divided into three groups randomly. Sham operation was performed on Group S (n=7); mesenteric ischemia and then 60 minutes reperfusion of the intestine process was performed on Group MI (n=7); mesenteric ischemia and then 60 minutes reperfusion of the intestine process was performed and 80 mg/kg ligustrazin was administrated intraperitoneally on Group MI+L (n=7). Intestinal tissue samples were taken for tissue MDA, SDO and nitric oxide (NO) levels, and ileum and jejunum samples were taken for histopathologic examination. RESULTS: Tissue MDA levels and tissue NO levels of Group MI-L was determined to have significantly decreased. Tissue SOD levels were found similar to Group S. Chiu classification score of the jejunum and ileum was determined to have decreased in Group MI-L compared to Group MI. DISCUSSION: As a result of this study, Ligustrazin was found to adjust lipid peroxidation in biochemical parameters during mesenteric I-R and decrease the severity of damage of I-R on the histopathological scores of the jejunum and ileum.

Pollen aero allergens and the climate in Mediterranean region and allergen sensitivity in allergic rhinoconjunctivitis and allergic asthma patients.

BACKGROUND: We evaluated the profiles of allergic rhino-conjunctivitis and asthma patients annually in Antalya, a Mediterranean coastal city in Turkey. MATERIAL AND METHODS: We evaluated patients' allergic clinical status, and recorded the climate and pollens in the city center air, investigating any correlation between pollination, climatic conditions and allergic disorders. The meteorological conditions and the pollen count/cm2 during every month of the year and the concordance of this with the patient's clinical status were evaluated. RESULTS: SPT positivity for plantago lanceolata, aspergillus fumigatus and d. pteronyssinus was significant in patients younger than 40 years old. Pollination levels are consistent from March 2010 to February 2011. In Antalya, high levels occur mostly from April to June, thus we performed skin prick tests mostly in May/June (~30%). During these months meteorological conditions of the city were windy with low humidity, without rain, and lukewarm temperatures, all of which contribute to high-risk conditions for seasonal allergies. CONCLUSIONS: The major allergen between April and June was derived from Graminea; between February and March was Cupressus spp; and between March and June was Pinus spp. These results suggest that the pollination is correlated with allergic conditions and thus SPT might be best performed according to the pollen count.

Status of influenza vaccination in patients presenting to two neighborhood primary health care clinics in Antalya.

The objective of our study was to determine the status of influenza vaccination in patients presenting to two neighborhood primary health care clinics at the provincial centre of Antalya. This type of descriptive research was conducted between March 15 and April 15, 2006, at Primary Health Care Clinics Number 9 and 16 in Antalya. A prepared questionnaire was completed by Akdeniz University Medical Faculty intern physicians during face-to-face interviews with 1494 patients. Information about infants and children were obtained from their parents. Data that were obtained were evaluated using the SPSS program. It was determined that 7.4% (111 individuals) of the research group had been vaccinated against influenza. Although there were no infants between 6 - 23 months who had been vaccinated, the percentage of individuals in the older than 60 yrs group who had been vaccinated was 27.9%. The vaccination status was significantly higher in the 60+ age group (p <0.001), in university graduates (p<0.001), in civil servants, in those with health insurance (p<0.025) and in those who had any kind of risk (p<0.001). It was determined that 24.6% of the research group had had influenza in the last month. In those at risk of catching influenza the vaccination rate was 14.9%. In the research group 27.3% of the chronic obstructive pulmonary disease (COPD) patients, 21.3% of the chronic cardiopulmonary disease patients, 18.0% of those with asthma, and 13.4% of the individuals with diabetes mellitus had received the influenza vaccination. It is recommended that all health care personnel working in primary health care clinics must educate the public about this issue.