Cerebellar infarction in a 9 year old child presenting with fever and ataxia: A case report

Cerebellar acute ischemic stroke (CAIS) can be a complication of minor head trauma, vertebral artery dissection, vasospasm or systemic hypoperfusion. CT scan usually is negative few hours after acute infarction. Magnetic resonance imaging (MRI) is superior to CT scan for posterior fossa lesions and also in acute phase of cerebellar stroke especially in children. Here we report a 9-year-old girl referred to the Pediatric Emergency Room, Moosavi Hospital, Zanjan, Iran in January 2017 presenting with sudden onset of headache and recurrent vomiting, ataxia, and history of 3 consecutive days of fever and malaise. In the report of MRI, there were abnormal low T1 and high T2 signal intensity in left cerebellar hemisphere involving superior and middle cerebellar peduncles. After 4 days of admission, the patient became drowsy, symptoms progressed and transferred to the pediatric intensive care unit (PICU). The patient underwent hemispherectomy surgery of the left cerebellar hemisphere because of acute obstructive hydrocephaly. After 5 months of occupational therapy, the force of her extremities was normal and the ataxia completely disappeared. Childhood acute ischemic stroke although rare can happen with cerebellar involvement. Because in our patient the first brain CT scan was nearly normal and a false negative rate for initial computed tomography (CT) scanning of 60-80 also contributes to missed and delayed diagnosis of childhood AIS, for every child presenting with acute ataxia without identified cause in addition to CT scan, MRI also being ordered and from the beginning besides other causes, stroke be contemplated as a cause of ataxia. © 2019, Iranian Child Neurology Society. All rights reserved

Plucked hair follicles from patients with chronic discoid lupus erythematosus show a disease-specific molecular signature

Objective: When 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. Methods: Lesional 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. Results: Here, 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. Conclusions: We 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

Left ventricle wall motion quantification from echocardiographic images by non-rigid image registration

Purpose: 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. Methods: 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. Results: 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. Conclusions: 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. © 2012 CARS

Epidemiological aspects and clinical outcome of patients with rhinocerebral zygomycosis: A survey in a referral hospital in Iran

Introduction: No comprehensive reports have been published on epidemiological status of Rhinocerebral zygomycosis infections and its outcome in our population, Hence, the current study came to address epidemiological characteristics as well as clinical outcome of patients with Rhinocerebral zygomycosis infection referred to a referral hospital in Iran. Methods: This retrospective study was performed at the Rasoul-e-Akram hospital, an 800-bed tertiary care teaching hospital in Tehran, Iran. The pathology recorded charts were reviewed to identify all cases of Rhinocerebral zygomycosis from patients admitted between April 2007 and March 2014. A diagnosis of Rhinocerebral zygomycosis was based on histopathological assessments. Results: Sixty four patients with Rhinocerebral zygomycosis were assessed. The mean age of the patients was 46.07 ± 22.59 years and 51.6 were female. Among those, 67.2 were diabetic, 26.6 were hypertensive and 29.7 had history of cancer. Different sinuses were infected in 73.4 of the patients. Out of all the patients 26.6 underwent surgical procedures and 17.2 were controlled medically. Extensive debridement was carried out in 40.6. Neutropenia ( 14 days) was found in 60.9. According to the Multivariable logistic regression analysis, the main predictors of in-hospital mortality included female gender, advanced age, the presence of sinus infection, and neutropenia, while higher dosages of amphotericin administered had a protective role in preventing early mortality. In a similar Multivariate model, history of cancer could predict prolonged hospital stay, whereas using higher dose of amphotericin could lead to shortening length of hospital stay. Conclusion: There is no difference in demographic characteristics between our patients with Rhinocerebral zygomycosis and other nations. The presence of diabetes mellitus is closely associated with the presence of this infection. Sinus involvement is very common in those with Rhinocerebral zygomycosis leading to high mortality and morbidity. Besides female gender, advanced age, and presence of neutropenia was a major risk factor for increasing early mortality. The use of higher doses of antifungal treatment such as amphotericin can prevent both mortality and prolonged hospital stay. The cancer patients may need longer hospital stay because of needing comprehensive in-hospital treatment. © Vida Bozorgiet al

Prediction of autoimmune connective tissue disease in an at-risk cohort: prognostic value of a novel two-score system for interferon status

Objective: To evaluate clinical, interferon and imaging predictors of progression from ‘At Risk’ to autoimmune connective tissue diseases (AI-CTDs). Methods: A prospective observational study was conducted in At-Risk of AI-CTD (defined as antinuclear antibody (ANA) positive; ≤1 clinical systemic lupus erythematosus (SLE) criterion; symptom duration <12 months and treatment-naïve). Bloods and skin biopsy (non-lesional) were analysed for two interferon-stimulated gene expression scores previously described (IFN-Score-A and IFN-Score-B). Forty-nine healthy controls (HCs) and 114 SLE were used as negative and positive controls. Musculoskeletal ultrasound was performed. Progression was defined by meeting classification criteria for AI-CTDs at 12 months. Results: 118 individuals with 12-month follow-up were included. Of these, 19/118 (16%) progressed to AI-CTD (SLE=14, primary Sjogren’s=5). At baseline, both IFN scores differed among At-Risk, HCs and SLE groups (p<0.001) and both were elevated in At-Risk who progressed to AI-CTD at 12 months versus non-progressors, to a greater extent for IFN-Score-B (fold difference (95% CI) 3.22 (1.74 to 5.95), p<0.001) than IFN-Score-A (2.94 (1.14 to 7.54); p=0.018). Progressors did not have significantly greater baseline clinical characteristics or ultrasound findings. Fold difference between At-Risk and HCs for IFN-Score-A was markedly greater in skin than blood. In multivariable logistic regression, only family history of autoimmune rheumatic disease, OR 8.2 (95% CI 1.58 to 42.53) and IFN-Score-B, 3.79 (1.50–9.58) increased the odds of progression. Conclusion: A two-factor interferon score and family history predict progression from ANA positivity to AI-CTD. These interferon scores may allow stratification of individuals At-Risk of AI-CTD permitting early intervention for disease prevention and avoid irreversible organ damage

EEG artifacts reduction by multivariate empirical mode decomposition and multiscale entropy for monitoring depth of anaesthesia during surgery

Electroencephalography (EEG) has been widely utilized to measure the depth of anaesthesia (DOA) during operation. However, the EEG signals are usually contaminated by artifacts which have a consequence on the measured DOA accuracy. In this study, an effective and useful filtering algorithm based on multivariate empirical mode decomposition and multiscale entropy (MSE) is proposed to measure DOA. Mean entropy of MSE is used as an index to find artifacts-free intrinsic mode functions. The effect of different levels of artifacts on the performances of the proposed filtering is analysed using simulated data. Furthermore, 21 patients' EEG signals are collected and analysed using sample entropy to calculate the complexity for monitoring DOA. The correlation coefficients of entropy and bispectral index (BIS) results show 0.14 ± 0.30 and 0.63 ± 0.09 before and after filtering, respectively. Artificial neural network (ANN) model is used for range mapping in order to correlate the measurements with BIS. The ANN method results show strong correlation coefficient (0.75 ± 0.08). The results in this paper verify that entropy values and BIS have a strong correlation for the purpose of DOA monitoring and the proposed filtering method can effectively filter artifacts from EEG signals. The proposed method performs better than the commonly used wavelet denoising method. This study provides a fully adaptive and automated filter for EEG to measure DOA more accuracy and thus reduce risk related to maintenance of anaesthetic agents.This research was financially supported by the Center for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan, which is sponsored by Ministry of Science and Technology (Grant Number: NSC102-2911-I-008-001). Also, it was supported by Chung-Shan Institute of Science and Technology in Taiwan (Grant Numbers: CSIST-095-V301 and CSIST-095-V302) and National Natural Science Foundation of China (Grant Number: 51475342)

Automatic assessment of regional and global wall motion abnormalities in echocardiography images by nonlinear dimensionality reduction

Purpose: Identification and assessment of left ventricular (LV) global and regional wall motion (RWM) abnormalities are essential for clinical evaluation of various cardiovascular diseases. Currently, this evaluation is performed visually which is highly dependent on the training and experience of echocardiographers and thus is prone to considerable interobserver and intraobserver variability. This paper presents a new automatic method, based on nonlinear dimensionality reduction (NLDR) for global wall motion evaluation and also detection and classification of RWM abnormalities of LV wall in a three-point scale as follows: (1) normokinesia, (2) hypokinesia, and (3) akinesia. Methods: Isometric feature mapping (Isomap) is one of the most popular NLDR algorithms. In this paper, a modified version of Isomap algorithm, where image to image distance metric is computed using nonrigid registration, is applied on two-dimensional (2D) echocardiography images of one cycle of heart. By this approach, nonlinear information in these images is embedded in a 2D manifold and each image is characterized by a point on the constructed manifold. This new representation visualizes the relationship between these images based on LV volume changes. Then, a new global and regional quantitative index from the resultant manifold is proposed for global wall motion estimation and also classification of RWM of LV wall in a three-point scale. Obtained results by our method are quantitatively evaluated to those obtained visually by two experienced echocardiographers as the reference (gold standard) on 10 healthy volunteers and 14 patients. Results: Linear regression analysis between the proposed global quantitative index and the global wall motion score index and also with LV ejection fraction obtained by reference experienced echocardiographers resulted in the correlation coefficients of 0.85 and 0.90, respectively. Comparison between the proposed automatic RWM scoring and the reference visual scoring resulted in an absolute agreement of 82 and a relative agreement of 97. Conclusions: The proposed diagnostic method can be used as a useful tool as well as a reference visual assessment by experienced echocardiographers for global wall motion estimation and also classification of RWM abnormalities of LV wall in a three-point scale in clinical evaluations. © 2013 American Association of Physicists in Medicine

Automatic classification of left ventricular regional wall motion abnormalities in echocardiography images using nonrigid image registration

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. © 2013 Society for Imaging Informatics in Medicine

Factor Analysis of Student’s Self-Leadership Strategies and their Relationship with Academic Performance (Case: Birjand University Students' of Science and Literature)

This study was aimed at modeling a factor analysis of student’s self-leadership straregies and their relationship with academic performance in Faculty of Science and Literature at University of Birjand. The research method was descriptive and correlational using Cochran's sampling formula and Sample size was determined 351 Students by stratified random sampling with proportional allocation. Self- leadership questionnaire with the reliability (α =0/80) was used to collect data. Exploratory factor analysis using principal component (PC) showed students used six strategy - involves a focus on natural rewards and individual goal setting)α=0/66), visualizing successful performance)α=0/59), self-punishment)α=0/56), self-dialogue)α=0/73), self- help)α=  0/69)  and self - reward)α= 0/73)  are used. Correlation analysis also showed there are positive correlation between applying self-leadership sterategies and academic performance (r=0/25), (