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Showing 4 results for Schizophrenia
Validity of a Farsi translation of the composite International Diagnostic Interview (CIDI) to diagnose schizophrenia and bipolar disorder
Amini H, Alaghband-Rad J, Sharifi V, Davari-Ashtiani R, Kaviani K, Shahrivar Z, Shaabani A, Arabgol F, Shirazi E, Hakim-Shooshtari M,
Volume 64, Issue 8 (8-2006)
Abstract
Background: The Composite International Diagnostic Interview (CIDI) is a comprehensive, standardized diagnostic interview for the assessment of psychiatric disorders. There have been few studies on the validity of the CIDI. The objective of present study was to assess the validity of a Farsi translation of the complete CIDI and its psychosis/mania module in five referral clinical psychiatric settings.
Methods: Two hundred and three as well as 104 consecutive admissions were interviewed using the complete and the psychosis/mania module, respectively. Within two days of the CIDI interview, two last year residents of psychiatry or psychiatrist who were blind to the CIDI diagnosis completed the Clinical diagnostic checklists (based on DSM-IV and ICD-10 criteria) simultaneously and reached the consensus diagnosis. Data analysis was performed using SPSS 11 to determine the validity of CIDI.
Results: The sensitivity and specificity for the diagnosis of schizophrenia was 0.12 and 0.96 using DSM-IV criteria. According to ICD-10 criteria, the results were the same with 0.19% sensitivity and 0.96% specificity. The sensitivity for the diagnosis of bipolar I disorder was low (0.21 using DSM-IV criteria and 0.17% using ICD-10) and specificity, high (0.90 compared to DSM-IV and 0.89 compared to ICD-10 criteria). The results were rather similar for the psychosis/mania module of CIDI.
Conclusion: This study suggests that the Farsi translation of both the complete CIDI and the psychosis/mania module of CIDI have good specificity, but poor sensitivity for the diagnosis of schizophrenia and of bipolar I disorder.
Electroconvulsive therapy in drug resistant neuroleptic malignant syndrome: a case report
Yousefi A, Asgari F,
Volume 67, Issue 11 (2-2010)
Abstract

Normal 0 false false false EN-US X-NONE AR-SA MicrosoftInternetExplorer4 Background: Neuroleptic malignant syndrome is an idiosyncratic and potentially fatal reaction to neuroleptic drugs and is characterized by fever, muscular rigidity, altered mental status, autonomic dysfunction, elevated serum CPK and leucocytosis Neuroleptic malignant syndrome is treated with dantrolene, bromocriptin, amantadin and electroconvulsive therapy.
Case: A 22 years old, schizophrenic female was refered to the Emergency ward of Roozbeh hospital in Tehran, Iran in December 2008 with aggression, impulsivity, and reduced sleep. After injection of haloperidol, the patient developed a high grade fever, diaphoresis and muscular stiffness. She was diagnosed as neuroleptic malignant syndrome and the treatment with dantrolen, bromocriptin and amantadin initiated. Although fever subsided, other signs continued, therefore we applied electroconvulsive therapy to this case.
Conclusion: Due to excellent response of the patient to electroconvulsive therapy and the rare reports of electroconvulsive therapy in neuroleptic malignant syndrome in the Iranian population, this case may lead to develop studies for further investigations of this issue.


Comparative study of cytomegalovirus antibody and viral load in schizophrenia and bipolar patients
Farid Suleimani Mohammadi , Abbas Rahimi Foroushani , Mohsen Rokni , Mohammad Farahmand , Kazem Ahmadi Kia , Azadeh Shadab , Hamidreza Ahmadkhaniha, Jila Yavarian ,
Volume 74, Issue 11 (2-2017)
Abstract

Background: Schizophrenia (SC) and bipolar disorder (BD) are two chronic psychiatric illnesses with worldwide distribution. People could be involved at any age, particularly in early adolescence. Main symptoms of SC are non- affective symptoms such as auditory hallucination and illogical thinking. In contrast, BD represents affective symptoms such as depression and mania. Although the main cause of these mood disorders has been remained elusive, there are some potential contributing factors that could be considered in the pathogenesis of mentioned illnesses including, genetic and environmental factors. Cytomegalovirus (CMV) is one of the probable contributing factors in SC and BD. CMV is a prototype of herpesviridae family which may infect different cell types such as endothelial and differentiated hematopoietic cells. CMV infections in immunocompromised patients as well as congenitally infected children represent CNS complication such as microcephaly and hearing loss. This virus has capability to impair the limbic structures in brain.

Methods: This descriptive study was designed to evaluate the role of CMV in these illnesses. We investigated the level of serum IgG antibody and the presence of CMV DNA in serum and peripheral blood mononuclear cells (PBMCs) samples of 46 SC and BD patients admitted to Iran Psychiatry Hospital Tehran, Iran from 2014 to 2015 as well as 46 healthy control groups at Tehran University of Medical Sciences.  First, the level of CMV IgG antibody was evaluated in serum samples, by enzyme-linked immunosorbent assay (ELISA). Then, DNA extraction conducted by using the high pure viral nucleic acid kit (Roche, Germany). Serologically positive sera along with PBMC samples were tested by Real-time PCR, to investigate the presence of CMV DNA.

Results: Results indicated higher levels of CMV IgG antibody in psychiatric patients, compared with a healthy control group. Afterward, we did not observe the presence of CMV DNA in either case or control groups.

Conclusion: According to the CNS impairment mediated by CMV infection, this virus has been supposed to play an important role in pathogenesis of mental disorders such as SC and BD. We suggest further investigation to be conducted, particularly on other samples such as cerebrospinal fluid.

Detection of schizophrenia patients using convolutional neural networks from brain effective connectivity maps of electroencephalogram signals
Sara Bagherzadeh, Arash Maghsoudi, Ahmad Shalbaf,
Volume 79, Issue 10 (1-2022)
Abstract
Background: Schizophrenia is a mental disorder that severely affects the perception and relations of individuals. Nowadays, this disease is diagnosed by psychiatrists based on psychiatric tests, which is highly dependent on their experience and knowledge. This study aimed to design a fully automated framework for the diagnosis of schizophrenia from electroencephalogram signals using advanced deep learning algorithms.
Methods: In this analytic study, which is done from April to October 2021 in Tehran, 19-channel electroencephalogram signals from 14 schizophrenia patients and 14 healthy individuals were recorded and pre-processed. Then, the effective connectivity measure using the transfer entropy method is estimated from them and a 19×19 asymmetric connectivity matrix is constructed and represented by a color map as an image. Then, these effective connectivity images are used as inputs to the five pre-trained neural networks of AlexNet, Resnet-50, Shufflenet, Inception, and Xception. Finally, the parameters of these networks are fine-tuned to diagnose schizophrenia patients. All models are fine-tuned based on newly constructed images using the adaptive moment estimation optimizer algorithm and cross-entropy as the loss function. 10-fold cross-validation and subject-independent validation methods are used to evaluate the proposed method.
Results: The results of the study showed that the highest average accuracy, precision, sensitivity and F-score for classification of two classes of schizophrenia and healthy using the connectivity images and the Inception model achieved equal to 96.52%, 95.89%, 97.22% and 96.55%, respectively, in subject-independent validation method and 98.51%, 98.51%, 98.51% and 98.51% for the 10-fold cross-validation method. Also, there was less effective connectivity between schizophrenic patients than healthy individuals and these patients generally have much less information flow.
Conclusion: Based on our results, the proposed new model can effectively analyze brain function and be useful for psychiatrists to accurately diagnose schizophrenia patients and reduce the possible error and subsequently inappropriate treatment.
 

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