Impact of negative links on the structural balance of brain functional network during emotion processing.

Activation of specific brain areas and synchrony between them has a major role in process of emotions. Nevertheless, impact of anti-synchrony (negative links) in this process still requires to be understood. In this study, we hypothesized that quantity and topology of negative links could influence a network stability by changing of quality of its triadic associations. Therefore, a group of healthy participants were exposed to pleasant and unpleasant images while their brain responses were recorded. Subsequently, functional connectivity networks were estimated and quantity of negative links, balanced and imbalanced triads, tendency to make negative hubs, and balance energy levels of two conditions were compared. The findings indicated that perception of pleasant stimuli was associated with higher amount of negative links with a lower tendency to make a hub in theta band; while the opposite scenario was observed in beta band. It was accompanied with smaller number of imbalanced triads and more stable network in theta band, and smaller number of balanced triads and less stable network in beta band. The findings highlighted that inter regional communications require less changes to receive new information from unpleasant stimuli, although by decrement in beta band stability prepares the network for the upcoming events.

Lower body kinematics estimation during walking using an accelerometer.

Measuring and predicting accurate joint angles are important to developing analytical tools to gauge users' progress. Such measurement is usually performed in laboratory settings, which is difficult and expensive. So, the aim of this study was continuous estimation of lower limb joint angles during walking using an accelerometer and random forest (RF). Thus, 73 subjects (26 women and 47 men) voluntarily participated in this study. The subjects walked at the slow, moderate, and fast speeds on a walkway, which was covered with 10 Vicon camera. Acceleration was used as input for a RF to estimate ankle, knee, and hip angles (in transverse, frontal, and sagittal planes). Pearson correlation coefficient (r) and Mean Square Error (MSE) were computed between the experimental and estimated data. Paired statistical parametric mapping (SPM) t-test was used to compare the experimental and estimated data throughout gait cycle. The results of this study showed that the MSE of joint angles between the experimental and estimated data ranged from 0.04 to 24.29 and r > 0.91. Moreover, the findings of SPM indicated that there was no significant difference between the experimental and estimated data of ankle, knee, and hip angles in all three planes throughout gait cycle. The results of our research developed a more accessible, portable procedure to quantifying lower limb joint angles by an accelerometer and RF. So, such wearable-based joint angles have the potential to be used in outside-laboratory settings to measure walking kinematics.

Safety and Efficacy of Single Anastomosis Sleeve Ileal (SASI) Bypass Surgery on Obese Patients with Type II Diabetes Mellitus during a One-Year Follow-up Period: A Single Center Cohort Study.

BACKGROUND: We aimed to evaluate the safety and efficacy of single anastomosis sleeve ileal (SASI) bypass surgery on obese patients with type II diabetes mellitus during a one-year follow-up period. METHODS: We included patients with a body mass index (BMI) more than 35 kg/m2 and at least one-year history of type II diabetes mellitus. We excluded patients aged under 25 or above 66 years, those who were not candidates for surgery, needed another bariatric surgery, and those not willing to participate in the study. All the patients were visited in the outpatient office on the 10th and 45th days as well as the 3rd month of the post-operative period until the end of the first year. RESULTS: in this study, we investigated 14 male (23.0%) and 47 female (77.0%) morbidly obese patients with type II diabetes mellitus who underwent SASI bypass. The mean excess weight loss (%EWL) was 60.99±15.69 and the mean total weight loss (%TWL) was 30.39±6.52 at the end of the one-year follow up. Finally, 44 patients (72.1%) had a complete and 15 patients (24.6%) had partial remission of type II diabetes mellitus. Of note, severe complications were recorded in two patients (3.2%). Paired t test analysis demonstrated a significant decrease for fasting plasma sugar (FBS) after one-year follow-up in comparison with FBS before surgery (P<0.0001). Furthermore, this difference was observed in HbA1c (P<0.0001). CONCLUSION: SASI bypass is an effective method for weight loss and controlling type II diabetes mellitus.

GC-CNNnet: Diagnosis of Alzheimer's Disease with PET Images Using Genetic and Convolutional Neural Network.

There is a wide variety of effects of Alzheimer's disease (AD), a neurodegenerative disease that can lead to cognitive decline, deterioration of daily life, and behavioral and psychological changes. A polymorphism of the ApoE gene ε 4 is considered a genetic risk factor for Alzheimer's disease. The purpose of this paper is to demonstrate that single-nucleotide polymorphic markers (SNPs) have a causal relationship with quantitative PET imaging traits. Additionally, the classification of AD is based on the frequency of brain tissue variations in PET images using a combination of k-nearest-neighbor (KNN), support vector machine (SVM), linear discrimination analysis (LDA), and convolutional neural network (CNN) techniques. According to the results, the suggested SNPs appear to be associated with quantitative traits more strongly than the SNPs in the ApoE genes. Regarding the classification result, the highest accuracy is obtained by the CNN with 91.1%. These results indicate that the KNN and CNN methods are beneficial in diagnosing AD. Nevertheless, the LDA and SVM are demonstrated with a lower level of accuracy.

EEG Feature Extraction and Data Augmentation in Emotion Recognition.

Emotion recognition is a challenging problem in Brain-Computer Interaction (BCI). Electroencephalogram (EEG) gives unique information about brain activities that are created due to emotional stimuli. This is one of the most substantial advantages of brain signals in comparison to facial expression, tone of voice, or speech in emotion recognition tasks. However, the lack of EEG data and high dimensional EEG recordings lead to difficulties in building effective classifiers with high accuracy. In this study, data augmentation and feature extraction techniques are proposed to solve the lack of data problem and high dimensionality of data, respectively. In this study, the proposed method is based on deep generative models and a data augmentation strategy called Conditional Wasserstein GAN (CWGAN), which is applied to the extracted features to regenerate additional EEG features. DEAP dataset is used to evaluate the effectiveness of the proposed method. Finally, a standard support vector machine and a deep neural network with different tunes were implemented to build effective models. Experimental results show that using the additional augmented data enhances the performance of EEG-based emotion recognition models. Furthermore, the mean accuracy of classification after data augmentation is increased 6.5% for valence and 3.0% for arousal, respectively.

Single and Combined Neuroimaging Techniques for Alzheimer's Disease Detection.

Alzheimer's disease (AD) consists of the gradual process of decreasing volume and quality of neuron connection in the brain, which consists of gradual synaptic integrity and loss of cognitive functions. In recent years, there has been significant attention in AD classification and early detection with machine learning algorithms. There are different neuroimaging techniques for capturing data and using it for the classification task. Input data as images will help machine learning models to detect different biomarkers for AD classification. This marker has a more critical role for AD detection than other diseases because beta-amyloid can extract complex structures with some metal ions. Most researchers have focused on using 3D and 4D convolutional neural networks for AD classification due to reasonable amounts of data. Also, combination neuroimaging techniques like functional magnetic resonance imaging and positron emission tomography for AD detection have recently gathered much attention. However, gathering a combination of data can be expensive, complex, and tedious. For time consumption reasons, most patients prefer to throw one of the neuroimaging techniques. So, in this review article, we have surveyed different research studies with various neuroimaging techniques and ML methods to see the effect of using combined data as input. The result has shown that the use of the combination method would increase the accuracy of AD detection. Also, according to the sensitivity metrics from different machine learning methods, MRI and fMRI showed promising results.

The Effect of Arbutin on The Expression of Tumor Suppressor P53, BAX/BCL-2 Ratio and Oxidative Stress Induced by Tert-Butyl Hydroperoxide in Fibroblast and LNcap Cell Lines.

OBJECTIVE: Arbutin (p-hydroxyphenyl-ß-D-glucopyranoside) possesses beneficial functions including antioxidant, antiinflammatory, and anti-tumoral activities. Due to the important role of oxidative stress and apoptosis in the successful treatment of cancer, understanding mechanisms that lead to apoptosis in cancer cells, is essential. The purpose of the current study was to evaluate the effect of arbutin on tert-butyl hydroperoxide (t-BHP)-induced oxidative stress and the related mechanisms in fibroblast and Lymph Node Carcinoma of the Prostate (LNCaP) cells. MATERIALS AND METHODS: In this experimental study, the LNCaP and fibroblast cell lines were pre-treated with arbutin (50, 250 and 1000 µM). After 24 hours, t-BHP (30 and 35 µM) was added to the cells. Viability was measured (at 24 and 48 hours) using MTT assay. The antioxidant effect of arbutin was measured by FRAP assay. The mRNA expression of P53 and BAX/BCL-2 ratio were measured using quantitative polymerase chain reaction (PCR). The percentage of apoptotic or necrotic cells was determined using a double staining annexin V fluorescein isothiocyanate (FITC) apoptosis detection kit. RESULTS: Arbutin pre-treatment increased the total antioxidative power and cell viability in the MTT assay and reduced BAX/BCL-2 ratio, P53 mRNA expression and necrosis in fibroblasts exposed to the oxidative agent (P<0.001). In addition, our results showed that arbutin can decrease cell viability, induce apoptosis and increase BAX/BCL-2 ratio in LNCaP cells at some specific concentrations (P<0.001). CONCLUSION: Arbutin as a potential functional ß-D-glucopyranoside has strong ability to selectively protect fibroblasts against t-BHP-induced cell damage and induce apoptosis in LNCaP cells.

Histomorphometric Analysis of Newly-formed Bone Using Octacalcium Phosphate and Bone Matrix Gelatin in Rat Tibial Defects.

BACKGROUND: Repair of bone defects is challenging for reconstructive and orthopedic surgeons. In this study, we aimed to histomorphometrically assess new bone formation in tibial bone defects filled with octacalcium phosphate (OCP), bone matrix gelatin (BMG), and a combination of both. METHODS: A total of 96 male Sprague Dawley rats aged 6-8 weeks weighing 120-150 g were randomly allocated into three experimental (OCP, BMG, and OCP/BMG) and one control group (n=24 in each group). The defects in experimental groups were filled with OCP (6 mg), BMG (6 mg), or a combination of OCP and BMG (6 mg, 2:1 ratio). No material was used to fill the defects in the control group and the defect was only covered with Surgicel. Samples were taken on days 7, 14, 21, and 56 after the surgery. The sections were stained with hematoxylin-eosin (H&E) and assessed using light microscopy. RESULTS: In our experimental groups, bone formation was started from the margins of the defect towards the center with an increasing rate during the study period. Moreover, the formed bone was more mature. Bone formation in our control group was only limited to the margins of the defect. The newly formed bone mass was significantly higher in the experimental groups (P=0.001). CONCLUSION: OCP, BMG, and OCP/BMG compound enhanced osteoinduction in long bones.

Diagnosis of Autism Spectrum Disorders in Young Children Based on Resting-State Functional Magnetic Resonance Imaging Data Using Convolutional Neural Networks.

Statistics show that the risk of autism spectrum disorder (ASD) is increasing in the world. Early diagnosis is most important factor in treatment of ASD. Thus far, the childhood diagnosis of ASD has been done based on clinical interviews and behavioral observations. There is a significant need to reduce the use of traditional diagnostic techniques and to diagnose this disorder in the right time and before the manifestation of behavioral symptoms. The purpose of this study is to present the intelligent model to diagnose ASD in young children based on resting-state functional magnetic resonance imaging (rs-fMRI) data using convolutional neural networks (CNNs). CNNs, which are by far one of the most powerful deep learning algorithms, are mainly trained using datasets with large numbers of samples. However, obtaining comprehensive datasets such as ImageNet and achieving acceptable results in medical imaging domain have become challenges. In order to overcome these two challenges, the two methods of "combining classifiers," both dynamic (mixture of experts) and static (simple |Bayes) approaches, and "transfer learning" were used in this analysis. In addition, since diagnosis of ASD will be much more effective at an early age, samples ranging in age from 5 to 10 years from global Autism Brain Imaging Data Exchange I and II (ABIDE I and ABIDE II) datasets were used in this research. The accuracy, sensitivity, and specificity of presented model outperform the results of previous studies conducted on ABIDE I dataset (the best results obtained from Adamax optimization technique: accuracy = 0.7273, sensitivity = 0.712, specificity = 0.7348). Furthermore, acceptable classification results were obtained from ABIDE II dataset (the best results obtained from Adamax optimization technique: accuracy = 0.7, sensitivity = 0.582, specificity = 0.804) and the combination of ABIDE I and ABIDE II datasets (the best results obtained from Adam optimization technique: accuracy = 0.7045, sensitivity = 0.679, specificity = 0.7421). We can conclude that the proposed architecture can be considered as an efficient tool for diagnosis of ASD in young children. From another perspective, this proposed method can be applied to analyzing rs-fMRI data related to brain dysfunctions.

Correction to: The evolutionary genetics of lactase persistence in seven ethnic groups across the Iranian plateau.

AbstractIn the original publication of this article [1], the colors of the Fig. 1 are wrong, and are revised in the updated figure below.

The evolutionary genetics of lactase persistence in seven ethnic groups across the Iranian plateau.

BACKGROUND: The ability to digest dietary lactose is associated with lactase persistence (LP) in the intestinal lumen in human. The genetic basis of LP has been investigated in many populations in the world. Iran has a long history of pastoralism and the daily consumption of dairy products; thus, we aim to assess how LP has evolved in the Iranian population. We recruited 400 adult individuals from seven Iranian ethnic groups, from whom we investigated their lactose tolerance and screened the genetic variants in their lactase gene locus. RESULTS: The LP frequency distribution ranged from 0 to 29.9% in the seven Iranian ethnic groups with an average value of 9.8%. The variants, - 13910*T and - 22018*A, were significantly associated with LP phenotype in Iranians. We found no evidence of hard selective sweep for - 13910*T and - 22018*A in Persians, the largest ethnic group of Iran. The extremely low frequency of - 13915*G in the Iranian population challenged the view that LP distribution in Iran resulted from the demic diffusion, especially mediated by the spread of Islam, from the Arabian Peninsula. CONCLUSIONS: Our results indicate the distribution of LP in seven ethnic groups across the Iranian plateau. Soft selective sweep rather than hard selective sweep played a substantial role in the evolution of LP in Iranian populations.

Informative combination of CLU rs11136000, serum HDL levels, diabetes, and age as a new piece of puzzle-picture of predictive medicine for cognitive disorders.

Clusterin (CLU) is the third most important associated risk gene in cognitive disorders. Regarding the controversy about the association of CLU rs11136000 with mild cognitive impairment (MCI), the aim of this study was to investigate a putative association of CLU rs11136000 with MCI as well as the serum biological factors with a special attention to the age as a main dimension of a multifactorial elderly disease in an Iranian elderly cohort in which the mentioned association was not previously investigated. The study also checked the association between diabetes and MCI in this population. A population of 418 individuals containing 236 MCI and 192 control subjects was recruited from the Amirkola health and aging population cohort. Serum biological indexes were assessed by biochemical and enzyme-linked immunosorbent assay, and rs11136000 genotyping was performed using polymerase chain reaction-restriction fragment length polymorphism. Bioinformatics analyses were used to identify the putative effect of rs11136000 on the secondary structure of RNA and chromatin location in different cell lines and tissues. Type 2 diabetes was present with a higher proportion in the MCI group in comparison with the control group (P = 0.041). The frequency of the C allele of CLU rs11136000 was significantly different between cases and controls and was associated with MCI risk (OR 1.79, P = 0.019). Under a dominant genetic model, the CC genotype showed a predisposing effect in individuals aged ≥ 75 years (OR 3.33, P = 0.0004). Interestingly, under an over-dominant model, the CT genotype had a protective effect in this population (OR 4.52, P = < 0.0001). We also found a significant association between the genotypes and high-density lipoprotein (HDL) levels in MCI patients (P = 0.0004). Bioinformatics analysis showed that rs11136000 is located in the transcribed region without any regulatory features such as being enhancer or insulator. Also, the T>C transition of CLU rs11136000 could not cause significant mRNA folding (P = 0.950). Contrary to other studies on Asian populations, this study demonstrated an association between rs11136000 and MCI in an elderly Iranian population. This study also suggests that an age-dependent approach to the previous studies may be performed in order to revise the previous belief in this geographical area. The rs11136000 genotypes in combination with HDL levels and knowledge about diabetes background may be used as a predictive medicine tool for cognitive disorders.

Combination of rs-fMRI and sMRI Data to Discriminate Autism Spectrum Disorders in Young Children Using Deep Belief Network.

In recent years, the use of advanced magnetic resonance (MR) imaging methods such as functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) has recorded a great increase in neuropsychiatric disorders. Deep learning is a branch of machine learning that is increasingly being used for applications of medical image analysis such as computer-aided diagnosis. In a bid to classify and represent learning tasks, this study utilized one of the most powerful deep learning algorithms (deep belief network (DBN)) for the combination of data from Autism Brain Imaging Data Exchange I and II (ABIDE I and ABIDE II) datasets. The DBN was employed so as to focus on the combination of resting-state fMRI (rs-fMRI), gray matter (GM), and white matter (WM) data. This was done based on the brain regions that were defined using the automated anatomical labeling (AAL), in order to classify autism spectrum disorders (ASDs) from typical controls (TCs). Since the diagnosis of ASD is much more effective at an early age, only 185 individuals (116 ASD and 69 TC) ranging in age from 5 to 10 years were included in this analysis. In contrast, the proposed method is used to exploit the latent or abstract high-level features inside rs-fMRI and sMRI data while the old methods consider only the simple low-level features extracted from neuroimages. Moreover, combining multiple data types and increasing the depth of DBN can improve classification accuracy. In this study, the best combination comprised rs-fMRI, GM, and WM for DBN of depth 3 with 65.56% accuracy (sensitivity = 84%, specificity = 32.96%, F1 score = 74.76%) obtained via 10-fold cross-validation. This result outperforms previously presented methods on ABIDE I dataset.

Genetic variants and expression study of FOXP3 gene in acute coronary syndrome in Iranian patients.

Acute coronary syndrome (ACS) is the most serious form of coronary artery disease. Inflammatory processes participate in different stages of this disorder. FOXP3 gene plays an important role for the development and function of regulatory T cells. Consequently, the expression level and polymorphisms of this gene have been studied in many immune related diseases. In the present study, we analysed the expression of FOXP3 as well as the association between two variants in this gene (rs3761548A/C and rs5902434del/ATT) and occurrence of ACS in Iranian patients. FOXP3 expression analysis showed a significant decrease in patients with ACS compared with controls (P = 0.029). In addition, a significant decrease has been detected in female patients compared with normal female subjects (P = 0.020). No significant change has been observed in FOXP3 expression in male patients compared with normal male subjects. In addition, no difference has been detected between ACS and normal subjects in combined genotype frequencies of both polymorphisms and the allele frequencies of rs5902434. However, rs3761548 C allele was more prevalent in controls compared with patients with ACS (P = 0.024). Consequently, our data demonstrated that FOXP3 expression is markedly decreased in female patients with ACS, which highlight the role of immune responses in plaque destabilization in such patients. Copyright © 2016 John Wiley & Sons, Ltd. SIGNIFICANCE PARAGRAPH: Considering the role of immune system in different stages of acute coronary syndrome (ACS), we evaluated the expression of FOXP3 gene as a master regulator of immune response in these patients compared with normal subjects. We detected a significant down-regulation of this gene in patients with ACS. Such decreased expression was more prominent in female patients, which implies the role of immune responses in plaque destabilization in such patients.

The first report of pyrrolizidine alkaloid poisoning in a gazelle (Gazella Subgutturosa) - histopathologic diagnosis.

Pyrrolizidine alkaloids (PAs) are natural phytotoxins found in thousands of plant species around the world. They are probably the most common poisonous plants affecting livestock, wildlife and humans. The disease occurs almost entirely as a consequence of chronic poisoning and in general ends fatally. In the present study, PAs poisoning was investigated in a gazelle with hepatic encephalopathy associated with severe neurologic signs. The main clinical signs included head pressing, progressive depression and weakness, ataxia and reluctance to move, turn the head to the left and to paddle, hyperesthesia and decreased food intake. Histopathological examination revealed major lesions in the liver consisting of severe hepatocyte megalocytosis and hypertrophy with nuclei enlargement, mild bile duct hyperplasia, centriacinar fatty change and hepatocellular necrosis. Moreover, pulmonary congestion and edema with endothelium necrosis and alveolar septa thickening, severe congestion in vessels of the brain and meninges, and myocardial necrosis were observed.

The Effects of Balance Training on Static and Dynamic Postural Stability Indices After Acute ACL Reconstruction.

BACKGROUND: Proprioception and postural stability play an important role in knee movements. However, there are controversies about the overall recovery time of proprioception following knee surgery and onset of balance and neuromuscular training after ACL reconstruction. Therefore, it is necessary to evaluate the effect of balance training in early stage of knee rehabilitation after anterior cruciate ligament (ACL) reconstruction. The purpose of this study was to evaluate the effect of balance exercises on postural stability indices in subjects with anterior cruciate ligament (ACL) reconstruction. METHODS: The study was a controlled randomized trial study. Twenty four patients who had ACL reconstructed (balance training group) and twenty four healthy adults without any knee injury (control group) were recruited in the study. The balance exercises group performed balance exercises for 2 weeks. Before and after the interventions, overall, anteroposterior, and mediolateral stability indices were measured with a Biodex Balance System in bilateral and unilateral stance positions with the eyes open and closed. T-tests were used for statistical analysis (p<0.05). RESULTS: Results showed that amount of static stability indices did not change after training and there were not significant differences in static stability indices before and after balance training (p>0.05). Although amount of dynamic stability indices decreased, there were not significant differences in dynamic stability indices before and after balance training (p>0.05). Amount of dynamic stability indices were decreased in balance training group, however, there were not significant differences between groups (p>0.05). CONCLUSION: These results support that balance exercise could partially improved dynamic stability indices in early stage of ACL reconstruction rehabilitation. The results of this study suggest that balance exercises should be part of the rehabilitation program following ACL reconstruction.

Prevalence of enterotoxigenic Staphylococcus aureus in organic milk and cheese in Tabriz, Iran.

BACKGROUND AND OBJECTIVES: Staphylococcal food poisoning is a gastrointestinal disease, which is caused by consumption of contaminated food with enterotoxins produced by Staphylococcus aureus (SEs). Milk and its products are known sources of food borne diseases. This study was carried out to evaluate the prevalence of enterotoxigenic S. aureus strains in organic milk and cheese in Tabriz - Iran. MATERIALS AND METHODS: A total of 200 samples (100 milk samples and 100 cheese samples) were collected from farms and milk collection points in Tabriz - Iran. The samples were cultured and identified by standard bacteriological methods, then PCR was performed to detect sea gene. RESULTS AND CONCLUSION: Staphylococcus aureus was found in 27% of all samples (milk and cheese). Results of PCR showed that 12.96% of S. aureus isolates possessed sea gene. It suggested the potential public health threat of S. aureus resulting from contamination of dairy products. So, efforts are required to improve safety standards for preventing staphylococcal food poisoning.

Assessment of sleep disturbance in renal transplant recipients and associated risk factors.

Sleep disturbances are highly prevalent in ESRD patients. In this study we sought to evaluate the associations of poor sleep with several genetic, laboratory, treatment and demographic factors in renal allograft recipients using a validated sleep quality questionnaire. A cross-sectional study was conducted on renal transplant patients over 18 years of age with stable current stable graft function. All patients completed PSQI and Ifudu questionnaires for assessment of sleep quality and morbidity measures. Kolmogorov-Smirnov test was used for evaluation of distributions besides Student's t-test, and Fisher's exact test for analyses. Mean total PSQI score for the whole patients was 6.5 +/- 2.6. Overall 26 (67%) of patients were diagnosed as "poor sleepers" (PSQI total score > or =5) and the reminding 13 (33%) were "good sleepers". Compared to "good sleepers", "poor sleepers" significantly had higher serum phosphate levels and ESRD duration (P = 0.05). Hematological disorders were more seen in "poor sleepers" and musculoskeletal disorders had a significant worsening impact on PSQI total score (beta = 0.28, P = 0.05). In conclusion our study showed that sleep disturbance is common in renal transplant patients is surprisingly common, and ESRD duration prior to transplant was significantly associate with sleep quality. Future studies with larger sample sizes are necessary for confirming our results.

Prognosis and predictors of convulsion among pediatric lupus nephritis patients.

In this study, we aimed to analyze features and outcome of convulsion in pediatric lupus nephritis patients. We retrospectively reviewed data of 14 Iranian children with lupus nephritis who developed seizures and compared them with a group of the same number of well matched pediatric lupus nephritis patients. Higher serum creatinine levels and higher frequencies of anemia and lymphopenia were observed in the convulsion group. Multivariable logistic regression analysis revealed that the only risk factor for development of convulsion in pediatric lupus patients with nephritis was lymphopenia. Survival analysis showed that convulsion had no impact on patient and renal function outcomes in our pediatric lupus nephritis subjects. In conclusion, we found that lymphopenia is a predictive factor for convulsion occurrence in our patients and special attention to neurological status assessment may be needed in this situation.