Evaluation of phenotypic and genotypic characteristics of carbapnemases-producing enterobacteriaceae and its prevalence in a referral hospital in Tehran city

Background & Objective: Carbapenem-resistant Enterobacteriaceae is a growing concern worldwide including Iran. The emergence of this pathogen is worrying as carbapenem is one of the �last-line� antibiotics for treatment of infections caused by multi drug resistant gram- negative bacteria. The main objective of this study was to determine the prevalence of carbapenem-resistant Enterobacteriaceae in a referral hospital in Tehran, Iran. Methods: In this study, all positive isolates of Enterobacteriaceae recorded in blood, urine, and other body fluids were studied during April 2017 to April 2018 in a referral hospital in Tehran. All cases of resistance to carbapenems were first tested by modified Hodge test. All cases with positive or negative test, after gene extraction, were examined genotypically based on the primers designed for the three Klebsiella pneumoniae carbapenemase (KPC), New Delhi metallo-β-lactamase (NDM), and OXA-48 genes by conventional PCR method. Results: 108 isolates (13.6) were resistant to all cephalosporins as well as to imipenem and meropenem. In a genotypic study, including 45 isolates, 13 isolates were positive for OXA-48 gene, 11 isolates for OXA-48 and NDM genes, 11 isolates for OXA-48, NDM and KPC genes, 4 isolates for OXA-48 genes and KPC, 3 isolates for NDM, one isolate for KPC. On the other hand, two isolates were negative for all three genes examined. Conclusion: OXA-48 gene was one of the most common genes resistant to carbapenems in Iran. According to studies, the prevalence of antibiotic resistance in Iran is rising dramatically, which reduces the choice of antibiotics to treat severe infections in the future. © 2020, Iranian Society of Pathology. All rights reserved

Iranome: A catalogue of genomic variations in the Iranian population

Considering the application of human genome variation databases in precision medicine, population-specific genome projects are continuously being developed. However, the Middle Eastern population is underrepresented in current databases. Accordingly, we established Iranome database (www.iranome.com) by performing whole exome sequencing on 800 individuals from eight major Iranian ethnic groups representing the second largest population of Middle East. We identified 1,575,702 variants of which 308,311 were novel (19.6%). Also, by presenting higher frequency for 37,384 novel or known rare variants, Iranome database can improve the power of molecular diagnosis. Moreover, attainable clinical information makes this database a good resource for classifying pathogenicity of rare variants. Principal components analysis indicated that, apart from Iranian-Baluchs, Iranian-Turkmen, and Iranian-Persian Gulf Islanders, who form their own clusters, rest of the population were genetically linked, forming a super-population. Furthermore, only 0.6% of novel variants showed counterparts in "Greater Middle East Variome Project", emphasizing the value of Iranome at national level by releasing a comprehensive catalog of Iranian genomic variations and also filling another gap in the catalog of human genome variations at international level. We introduce Iranome as a resource which may also be applicable in other countries located in neighboring regions historically called Greater Iran (Persia)

Panel 6 : Vaccines

Objective. To review the literature on progress regarding (1) effectiveness of vaccines for prevention of otitis media (OM) and (2) development of vaccine antigens for OM bacterial and viral pathogens. Data Sources. PubMed database of the National Library of Science. Review Methods. We performed literature searches in PubMed for OM pathogens and candidate vaccine antigens, and we restricted the searches to articles in English that were published between July 2011 and June 2015. Panel members reviewed literature in their area of expertise. Conclusions. Pneumococcal conjugate vaccines (PCVs) are somewhat effective for the prevention of pneumococcal OM, recurrent OM, OM visits, and tympanostomy tube insertions. Widespread use of PCVs has been associated with shifts in pneumococcal serotypes and bacterial pathogens associated with OM, diminishing PCV effectiveness against AOM. The 10-valent pneumococcal vaccine containing Haemophilus influenzae protein D (PHiD-CV) is effective for pneumococcal OM, but results from studies describing the potential impact on OM due to H influenzae have been inconsistent. Progress in vaccine development for H influenzae, Moraxella catarrhalis, and OM-associated respiratory viruses has been limited. Additional research is needed to extend vaccine protection to additional pneumococcal serotypes and other otopathogens. There are likely to be licensure challenges for protein-based vaccines, and data on correlates of protection for OM vaccine antigens are urgently needed. Implications for Practice. OM continues to be a significant health care burden globally. Prevention is preferable to treatment, and vaccine development remains an important goal. As a polymicrobial disease, OM poses significant but not insurmountable challenges for vaccine development.Peer reviewe

Developing an interesting electrochemical biosensing system from an enzyme inhibition study: Binding, inhibition and determination of catalase by ascorbate

By this article, we are going to report results of one of works which has been performed on investigation of the binding and inhibition of catalase (CAT) by ascorbate (ASC). To achieve this goal, different electrochemical experiments have been performed and their data have been analyzed by conventional and chemometric methods. Conventional methods were including direct analysis of the electrochemical data by observation of them and using simple mathematical equations while chemometric analyses of the electrochemical data helped us to obtain more information which completed the previous information and gave us a new insight to the binding of the ASC with CAT. The next step of our study was devoted to the investigation of the binding of ASC with CAT by molecular docking methods which gave us new information about binding and inhibition of the CAT by ASC. All the steps gave specific information which not only confirmed each other but also gave new information which helped us to better understanding the mechanism of the binding and inhibition of the CAT by ASC. Finally, based on inhibition of the CAT by ASC, we have developed a novel impedimetric method for determination of the CAT. © 2020 The Author(s

Exploring data-driven models for spatiotemporally local classification of Alfven eigenmodes

Alfven eigenmodes (AEs) are an important and complex class of plasma dynamics commonly observed in tokamaks and other plasma devices. In this work, we manually labeled a small database of 26 discharges from the DIII-D tokamak in order to train simple neural-network-based models for classifying AEs. The models provide spatiotemporally local identification of four types of AEs by using an array of 40 electron cyclotron emission (ECE) signals as inputs. Despite the minimal dataset, this strategy performs well at spatiotemporally localized classification of AEs, indicating future opportunities for more sophisticated models and incorporation into real-time control strategies. The trained model is then used to generate spatiotemporally-resolved labels for each of the 40 ECE measurements on a much larger database of 1112 DIII-D discharges. This large set of precision labels can be used in future studies for advanced deep predictors and new physical insights

Alfven eigenmode classification based on ECE diagnostics at DIII-D using deep recurrent neural networks

Modern tokamaks have achieved significant fusion production, but further progress towards steady-state operation has been stymied by a host of kinetic and MHD instabilities. Control and identification of these instabilities is often complicated, warranting the application of data-driven methods to complement and improve physical understanding. In particular, Alfven eigenmodes are a class of ubiquitous mixed kinetic and MHD instabilities that are important to identify and control because they can lead to loss of confinement and potential damage to the walls of a plasma device. In the present work, we use reservoir computing networks to classify Alfven eigenmodes in a large labeled database of DIII-D discharges, covering a broad range of operational parameter space. Despite the large parameter space, we show excellent classification and prediction performance, with an average hit rate of 91% and false alarm ratio of 7%, indicating promise for future implementation with additional diagnostic data and consolidation into a real-time control strategy