Analysis of Receptor’s Distribution in Entorhinal Cortex after Induction of Spreading Depression in Juvenile Rats

Spreading depression (SD), discovered by Leao in 1944, is a pathophysiological wave which propagates slowly in the brain (3 mm/min) and cause dramatic ionic and hemodynamic changes. SD appears to act through several mechanisms and receptors which have not completely understood. Here, we studied the effect of inhibitory system in animal model of SD using immunohistochemistry technique. After implanting recording electrodes and cannula over the brain, repetitive SD was induced by KCl injection (2 M) in juvenile rats for four consecutive weeks. Then all rats were decapitated and the brains removed. Mean number of dark neurons in entorhinal cortex were determined using Toluidine blue staining. To identify the prevalence and distribution of γ-aminobutyric acid A (GABA-A) subunit receptors and glutamic acid decarboxylase (GAD), immunohistochemistry technique was performed. The mean number of SD induced by KCl injection was statistically increased during four weeks of experiments (P=0.036). The mean number of dark neurons in entorhinal cortex was significantly increased in SD group compared to sham rats (P≤0.001). Also, expression of GAD 65 receptor in the Entorhinal cortex significantly increased in SD group compare to control group (P<0.05). GABA-Aα and GABA-Aβ receptors didn’t show significant change in that region. These data suggest that SD is able to damage neural cells and also it could lead to enhancement of GAD, the enzyme which is responsible for synthesizing an important inhibitory neurotransmitter, GABA receptor, in the central nervous system. Keywords: Cortical Spreading Depression, Entorhinal Cortex, Gamma-Aminobutyric Acid

Effect of nitric oxide modulation on the basic and rate-dependent electrophysiological properties of AV-node in the isolated heart of rabbit: The role of adrenergic and cholinergic receptors

Introduction: Recent studies showed that nitrergic system have specific modulatory effects on electrophysiological properties of atrioventricular (AV) node. The aim of this study was to determine the effects of nitric oxide (NO) on the electrophysiological properties of isolated rabbit AV node and to investigate the role of adrenergic and cholinergic receptors in the mechanism of its action. Methods: In our laboratory, an experimental model of isolated double-perfused AV-node of rabbits weighing 1.5-2 kg was used. Specific experimental protocols of recovery, Facilitation, Fatigue and Wenckbach were applied in both control and in the presence of the drug. A total number of 35 rabbits were divided randomly into the following groups (n=7): 1) L-Arg (NO donor) (250, 750 and 1000 μmol), 2) L- NAME, a NO synthesis inhibitor (25, 50 and 100 μmol), 3) L-Arg + L- NAME, 4) Nadolol (1 μmol), 5) Atropine (3 μmol). All data were shown as mean ± SE. The level of statistical significance was set at p<0.05. Results: Our results revealed the depressant effect of L-Arg on the basic and rate-dependent electrophysiological properties of AV-node. L- NAME did not deteriorate the effects of L-Arg on the basic and rate-dependent properties, nevertheless, at high concentration (100 μmol) it had a direct inhibitory effect on the AV-node. Nadolol and atropine could prevent the effects of NO on the basic nodal characteristics and the fatigue phenomenon, respectively. Conclusion: Nitergic system can affect basic and rate-dependent electrophysiological properties of the AV-node through adrenergic and cholinergic receptors

Geographic information system (GIS) as a tool in the epidemiological assessment of wetwood disease on elm trees in Tabriz City, Iran

Plants disease epidemiology provides us with some information about the spread of diseases in different regions with various climates and helps us conduct suitable managing operations and predictions about the spread of disease to other areas. Geographic Information System (GIS) has been widely used as an important tool in epidemiological studies. Wetwood disease is one of the most important bacterial diseases on elm trees found in the Northwest of Iran. The disease has spread in different regions of Tabriz (located in the Northwest of Iran), which has become terribly epidemic. Geographic Information System as an appropriate tool in epidemiological examination of plant disease is useful in various ways. In this study, the epidemiology of bacterial wetwood disease on elm trees in Tabriz was investigated using GIS databases. The results indicate that the disease has become epidemic in different areas of Tabriz. According to the results, although the disease was not found in some regions, its severity was very high in some other areas. Based on the distribution map, the wetwood disease most highly exists in the central regions and some parts of the northern regions of the city, but eastern regions are least affected

Correlation between results of head-up tilt test and clinical features in patients with syncope or presyncope

Background: Head-up tilt test (HUTT) is a well-established diagnostic tool in patients with suspected vasovagal syncope. Identification of factors that predict a positive HUTT result could simplify diagnostic steps. The aim of this study was to assess the correlation between clinical characteristics of patients with suspected neurocardiogenic syncope or presyncope and results of HUTT. Materials and Methods: The study group consisted of 90 patients (55 men, 35 women; mean age, 43.2 ± 17 years) with a history of syncope or presyncope. Cardiological and neurologic test findings were normal in every patient. The patients were tilted to a 70° position for 45 minutes. If the first phase produced a negative response, the patients received 400 μg of sublingual nitroglycerin for the second phase and continued to be tilted for an additional 15 minutes. Results: Sixty-four patients had a positive HUTT result, characterized by a vasodepressive response in 26 patients, mixed response in 24 patients, and cardioinhibitory response in 14 patients. In logistic regression analysis, the presence of prodromal symptoms was a predictor of a positive HUTT result (P = .002). Conclusion: We showed that the prognostic performance of clinical features, including the time interval between the last episode and HUTT, the number of syncope or presyncope episodes, age, and sex, was not ideal. The presence of prodromal symptoms might be more likely to predict a positive response during HUTT. © 2007 Elsevier Inc. All rights reserved

Expression profiles of adult T-cell leukemia-lymphoma and associations with clinical responses to zidovudine and interferon alpha

Adult T-cell leukemia-lymphoma (ATLL) is an HTLV-1-associated lymphoproliferative malignancy that is frequently fatal. We compared gene expression profiles (GEPs) of leukemic specimens from nine patients with ATLL at the time of diagnosis and immediately after combination therapy with zidovudine (AZT) and interferon alpha (IFNalpha). GEPs were also related to genetic aberrations determined by comparative genomic hybridization. We identified several genes anomalously over-expressed in the ATLL leukemic cells at the mRNA level, including LYN, CSPG2, and LMO2, and confirmed LMO2 expression in ATLL cells at the protein level. In vivo AZT-IFNalpha therapy evoked a marked induction of interferon-induced genes accompanied by repression of cell-cycle regulated genes, including those encoding ribosomal proteins. Remarkably, patients not responding to AZT-IFNalpha differed most from responding patients in lower expression of these same IFN-responsive genes, as well as components of the antigen processing and presentation apparatus. Demonstration of specific gene expression signatures associated with response to AZT-IFNalpha therapy may provide novel insights into the mechanisms of action in ATLL

Random forest for gene selection and microarray data classification

A random forest method has been selected to perform both gene selection and classification of the microarray data. In this embedded method, the selection of smallest possible sets of genes with lowest error rates is the key factor in achieving highest classification accuracy. Hence, improved gene selection method using random forest has been proposed to obtain the smallest subset of genes as well as biggest subset of genes prior to classification. The option for biggest subset selection is done to assist researchers who intend to use the informative genes for further research. Enhanced random forest gene selection has performed better in terms of selecting the smallest subset as well as biggest subset of informative genes with lowest out of bag error rates through gene selection. Furthermore, the classification performed on the selected subset of genes using random forest has lead to lower prediction error rates compared to existing method and other similar available methods

An evaluation model for the implementation of hospital information system in public hospitals using multi-criteria-decision-making (MCDM) approaches

Background: Hospital Information System (HIS) is implemented to provide high-quality patient care. The aim of this study is to identify significant dimensional factors that influence the hospital decision in adopting the HIS. Methods: This study designs the initial integrated model by taking the three main dimensions in adopting HIS technology. Accordingly, DEMATEL was utilized to test the strength of interdependencies among the dimensions and variables. Then ANP approach is adapted to determining how the factors are weighted and prioritized by professionals and main users working in the Iranian public hospitals, in-volved with the HIS system. Results: The results indicated that "Perceived Technical Competence" is a key factor in the Human dimension. The respondents also believed that "Relative Advantage," "Compatibility" and "Security Concern" of Technology dimension should be further assessed in relation to other factors. With respect to Organization dimension, "Top Management Support" and "Vendor Support" are considered more important than others. Conclusion: Applying the TOE and HOT-fit models as the pillar of our developed model with significant findings add to the growing literature on the factors associated with the adoption of HIS and also shed some light for managers of public hospitals in Iran to success-fully adopt the HIS. © 2018 Ali Aliakbar Esfahani et al

An evaluation model for the implementation of hospital information system in public hospitals using multi-criteria-decision-making (MCDM) approaches

Background: Hospital Information System (HIS) is implemented to provide high-quality patient care. The aim of this study is to identify significant dimensional factors that influence the hospital decision in adopting the HIS. Methods: This study designs the initial integrated model by taking the three main dimensions in adopting HIS technology. Accordingly, DEMATEL was utilized to test the strength of interdependencies among the dimensions and variables. Then ANP approach is adapted to determining how the factors are weighted and prioritized by professionals and main users working in the Iranian public hospitals, in-volved with the HIS system. Results: The results indicated that "Perceived Technical Competence" is a key factor in the Human dimension. The respondents also believed that "Relative Advantage," "Compatibility" and "Security Concern" of Technology dimension should be further assessed in relation to other factors. With respect to Organization dimension, "Top Management Support" and "Vendor Support" are considered more important than others. Conclusion: Applying the TOE and HOT-fit models as the pillar of our developed model with significant findings add to the growing literature on the factors associated with the adoption of HIS and also shed some light for managers of public hospitals in Iran to success-fully adopt the HIS. © 2018 Ali Aliakbar Esfahani et al

Evolutionary Computation for Optimal Ensemble Classifier in Lymphoma Cancer Classification

Abstract. Owing to the development of DNA microarray technologies, it is possible to get thousands of expression levels of genes at once. If we make the effective classification system with such acquired data, we can predict the class of new sample, whether it is normal or patient. For the classification system, we can use many feature selection methods and classifiers, but a method cannot be superior to the others absolutely for feature selection or classification. Ensemble classifier has been using to yield improved performance in this situation, but it is almost impossible to get all ensemble results, if there are many feature selection methods and classifiers to be used for ensemble. In this paper, we propose GA based method for searching optimal ensemble of feature-classifier pairs on Lymphoma cancer dataset. We have used two ensemble methods, and GA finds optimal ensemble very efficiently.