<span style="font-size:15.0pt;line-height: 115%;font-family:"Times New Roman";mso-fareast-font-family:"Times New Roman"; mso-bidi-font-family:"Times New Roman";mso-ansi-language:EN-US;mso-fareast-language: EN-US;mso-bidi-language:AR-SA" lang="EN-US">Comparative assessment of metal (Zn, Cu, Pb, Cd and Hg) concentrations in soft tissue and shell of <i>Barbatia helblingii</i> (family: Arcidae) from Qeshm Island (Persian Gulf) during winter and spring</span>

1770-1780<span style="font-size:9.0pt;line-height: 115%;font-family:" times="" new="" roman";mso-fareast-font-family:"times="" roman";="" mso-bidi-font-family:"times="" roman";mso-ansi-language:en-us;mso-fareast-language:="" en-us;mso-bidi-language:ar-sa"="" lang="EN-US">Specimens of the bivalve, Barbatia helblingii, were collected from the northern coast of Qeshm Island (the biggest Island of Persian Gulf) during winter and spring (2010), to assess metal concentrations. Zn, Cu, Pb, Cd and Hg concentrations were measured in soft tissue and shell of the samples, using Atomic Absorption Spectrophotometer Technique and Cold Vapor Production System. Metal concentrations of soft tissue and shell were higher in winter than those of the spring.  Pattern of metals mean concentration in soft tissue was as the following: Zn>Cu>Cd>Pb>Hg, but it was not constant in shell (during two seasons). However, Zn and Cu had the highest concentrations of metals in shell, while Hg had the lowest. Layer stratification of Persian Gulf, and its turnover, phytoplankton bloom, and also the biologic factors such as biometric characteristics variation were influenced the seasonal variation of metal concentrations, but proportion of each factor was different for each element. </span

Optimal DNA Isolation Method for Detection of Nontuberculous Mycobacteria by Polymerase Chain Reaction

Background: Nontuberculous mycobacteria (NTM) are a group of opportunistic pathogens and these are widely dispersed in water and soil resources. Identification of mycobacteria isolates by conventional methods including biochemical tests, growth rates, colony pigmentation, and presence of acid-fast bacilli is widely used, but these methods are time-consuming, labor-intensive, and may sometimes remain inconclusive. Materials and Methods: The DNA was extracted from NTM cultures using CTAB, Chelex, Chelex + Nonidet P-40, FTA® Elute card, and boiling The quantity and quality of the DNA extracted via these methods were determined using UV-photometer at 260 and 280 nm, and polymerase chain reaction (PCR) amplification of the heat-shock protein 65 gene with serially diluted DNA samples. Results: The CTAB method showed more positive results at 1:10–1:100,000 at which the DNA amount was substantial. With the Chelex method of DNA extraction, PCR amplification was detected at 1:10 and 1:1000 dilutions. Conclusions: According to the electrophoresis results, the CTAB and Chelex DNA extraction methods were more successful in comparison with the others as regard producing suitable concentrations of DNA with the minimum use of PCR inhibitor

Rapid Detection of Streptomycin-Resistant Mycobacterium tuberculosis by rpsL-Restriction Fragment Length Polymorphism

Background: Molecular methods for the detection of drug-resistant tuberculosis (DR-TB) are potentially more rapid than conventional culture-based drug susceptibility testing, facilitating the commencement of appropriate treatment for patients with DR-TB. The aim of this study was to evaluate and develop polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) assays for the detection of mutations within rpsL, and for the determination of streptomycin (STR) resistance in Mycobacterium tuberculosis. Materials and Methods: Clinical specimens were collected from individuals with suspected TB referred to the TB Center of Isfahan' from which 205 M. tuberclosis were isolated and identified by conventional phenotypic methods. The minimum inhibitory concentration of STR for all isolates was determined using the proportion method and 10 isolates were recognized as STR resistant M. tuberculosis. The effect of genetic alterations in the rpsL gene for these resistant isolates were investigated by PCR-RFLP method. Results: Three (30%) isolates showed point mutation at codon 43 by RLFP analysis. Conclusion: Our results suggest that RFLP analysis of the rpsL gene is useful for the rapid prediction of STR resistant strains of M. tuberculosis

Pathological Assessment of Brain White Matter in Relapsing-Remitting MS Patients using Quantitative Magnetization Transfer Imaging

Introduction: Multiple sclerosis (MS) is characterized by lesions in the white matter (WM) of the central nervous system. Magnetic resonance imaging is the most specific and sensitive method for diagnosis of multiple sclerosis. However, the ability of conventional MRI to show histopathologic heterogeneity of MS lesions is insufficient. Quantitative magnetization transfer imaging (qMTI) is a relatively new method to investigate pathologic processes of the brain tissue occurring in MS patients. Material and Methods: Voxel-based analyses allow regional comparisons between groups to be made for the whole brain in a single analysis. This is done by coregistering data from all individual subjects to a reference brain, generally referred to as the "standard space", and then comparing them on a voxel-by-voxel basis. This study aimed to analyze whole-brain quantitative T1 maps, not to find global changes or changes in selected regions, but specifically to investigate the spatial distribution throughout the brain of T1 increases in MS WM with respect to control WM. In this study, 11 healthy controls, 10 relapsing-remitting (RR) MS patients and 13 CIS patients were studied using MT-MRI imaging. MT parameters, including magnetization transfer ratio (MTR), magnetization transfer rate between free protons and restricted macromolecular protons, Ksat and longitudinal relaxation times (with and without MT saturation pulse), T1sat and T1free values were evaluated. Results: The results showed that, at a group level, there is widespread involvement of WM throughout the brain in CIS MS and especially in RRMS, where a significant T1 increase was found in 15.58% of WM voxels (normals < RR). Discussion and Conclusion: This study demonstrates that WM in large parts of the brain is susceptible to disease processes in RR and CIS M

Classification algorithms with multi-modal data fusion could accurately distinguish neuromyelitis optica from multiple sclerosis

International audienceNeuromyelitis optica (NMO) exhibits substantial similarities to multiple sclerosis (MS) in clinical manifestations and imaging results and has long been considered a variant of MS. With the advent of a specific biomarker in NMO, known as anti-aquaporin 4, this assumption has changed; however, the differential diagnosis remains challenging and it is still not clear whether a combination of neuroimaging and clinical data could be used to aid clinical decision-making. Computer-aided diagnosis is a rapidly evolving process that holds great promise to facilitate objective differential diagnoses of disorders that show similar presentations. In this study, we aimed to use a powerful method for multi-modal data fusion, known as a multi-kernel learning and performed automatic diagnosis of subjects. We included 30 patients with NMO, 25 patients with MS and 35 healthy volunteers and performed multi-modal imaging with T1-weighted high resolution scans, diffusion tensor imaging (DTI) and resting-state functional MRI (fMRI). In addition, subjects underwent clinical examinations and cogni-tive assessments. We included 18 a priori predictors from neuroimaging, clinical and cognitive measures in the initial model. We used 10-fold cross-validation to learn the importance of each modality, train and finally test the model performance. The mean accuracy in differentiating between MS and NMO was 88%, where visible white matter lesion load, normal appearing white matter (DTI) and functional connectivity had the most important contributions to the final classification. In a multi-class classification problem we distinguished between all of 3 groups (MS, NMO and healthy controls) with an average accuracy of 84%. In this classification, visible white matter lesion load, functional connectivity, and cognitive scores were the 3 most important modalities. Our work provides preliminary evidence that computational tools can be used to help make an objective differential diagnosis of NMO and MS