234 research outputs found
Infectious diseases epidemic threats and mass gatherings: refocusing global attention on the continuing spread of the Middle East Respiratory syndrome coronavirus (MERS-CoV)
No full textMedia and World Health Organization (WHO) attention on Zika virus transmission at the 2016 Rio Olympic Games and the 2015 Ebola virus outbreak in West Africa diverted the attention of global public health authorities from other lethal infectious diseases with epidemic potential. Mass gatherings such as the annual Hajj pilgrimage hosted by Kingdom of Saudi Arabia attract huge crowds from all continents, creating high-risk conditions for the rapid global spread of infectious diseases. The highly lethal Middle Eastern respiratory syndrome coronavirus (MERS-CoV) remains in the WHO list of top emerging diseases likely to cause major epidemics. The 2015 MERS-CoV outbreak in South Korea, in which 184 MERS cases including 33 deaths occurred in 2 months, that was imported from the Middle East by a South Korean businessman was a wake-up call for the global community to refocus attention on MERS-CoV and other emerging and re-emerging infectious diseases with epidemic potential. The international donor community and Middle Eastern countries should make available resources for, and make a serious commitment to, taking forward a “One Health” global network for proactive surveillance, rapid detection, and prevention of MERS-CoV and other epidemic infectious diseases threats
Comparative metagenomics and characterization of antimicrobial resistance genes in pasteurized and homemade fermented Arabian laban
No full textThe Middle East Respiratory Syndrome Coronavirus – A Continuing Risk to Global Health Security
No full textCoronavirus disease of 2019 (COVID-19) in the Gulf Cooperation Council (GCC) countries: Current status and management practices
No full textCoronavirus disease 2019 (COVID-19), caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is currently a global threat to human population. The numbers of cases and deaths due to COVID-19 are escalating daily, putting health care systems worldwide under tremendous pressure. Policymakers in the affected countries have adopted varying strategies to deal with this crisis. As a result, the current COVID-19 status in terms of number of cases and deaths hugely varies between countries. The Gulf Cooperation Council (GCC) countries have managed to limit the massive spread of the infection among their populations by implementing proactive plans and timely decisions in response to COVID-19 outbreak; measures taken included suspension of flights, closure of educational institutes, curfew and lockdown of major cities, and provision of free-of-charge healthcare to patients. This review summarizes the COVID-19 status as of 18 May 2020 and highlights prevention and control measures applied in the GCC countries
CS12-3. CCR5 32 and CCR5-59029 Allele Frequency among Hepatitis C Virus Infected and in Non-HCV Infected Saudi Population
No full textCrystal structure of M<sup>pro</sup> (PDB ID: 6W63) representing Domain I, II and III with the co-crystalized ligand (PDB code: X77).
No full textCrystal structure of Mpro (PDB ID: 6W63) representing Domain I, II and III with the co-crystalized ligand (PDB code: X77).</p
Emerg Infect Dis
No full textRisk factors for human-to-human transmission of Middle East respiratory syndrome coronavirus (MERS-CoV) are largely unknown. After MERS-CoV infections occurred in an extended family in Saudi Arabia in 2014, relatives were tested by using real-time reverse transcription PCR (rRT-PCR) and serologic methods. Among 79 relatives, 19 (24%) were MERS-CoV positive; 11 were hospitalized, and 2 died. Eleven (58%) tested positive by rRT-PCR; 8 (42%) tested negative by rRT-PCR but positive by serology. Compared with MERS-CoV-negative adult relatives, MERS-CoV-positive adult relatives were older and more likely to be male and to have chronic medical conditions. Risk factors for household transmission included sleeping in an index patient's room and touching respiratory secretions from an index patient. Casual contact and simple proximity were not associated with transmission. Serology was more sensitive than standard rRT-PCR for identifying infected relatives, highlighting the value of including serology in future investigations
Kaumoebavirus, a New Virus That Clusters with Faustoviruses and Asfarviridae
No full textInternational audienceIn this study, we report the isolation of a new giant virus found in sewage water from the southern area of Jeddah (Saudi Arabia), with morphological and genomic resemblance to Faustoviruses. This new giant virus, named Kaumoebavirus, was obtained from co-culture with Vermamoeba vermiformis, an amoeboid protozoa considered to be of special interest to human health and the environment. This new virus has similar to 250 nm icosahedral capsids and a 350,731 bp DNA genome length. The genome of Kaumoebavirus has a coding density of 86%, corresponding to 465 genes. Most of these genes (59%) are closely related to genes from members of the proposed order Megavirales, and the best matches to its proteins with other members of the Megavirales are Faustoviruses (43%) and Asfarviruses (23%). Unsurprisingly, phylogenetic reconstruction places Kaumoebavirus as a distant relative of Faustoviruses and Asfarviruses
Application of Decision-Tree-Based Machine Learning Algorithms for Prediction of Antimicrobial Resistance
No full textTimely and efficacious antibiotic treatment depends on precise and quick in silico antimicrobial-resistance predictions. Limited treatment choices due to antimicrobial resistance (AMR) highlight the necessity to optimize the available diagnostics. AMR can be explicitly anticipated on the basis of genome sequence. In this study, we used transcriptomes of 410 multidrug-resistant isolates of Pseudomonas aeruginosa. We trained 10 machine learning (ML) classifiers on the basis of data on gene expression (GEXP) information and generated predictive models for meropenem, ciprofloxacin, and ceftazidime drugs. Among all the used ML models, four models showed high F1-score, accuracy, precision, and specificity compared with the other models. However, RandomForestClassifier showed a moderate F1-score (0.6), precision (0.61), and specificity (0.625) for ciprofloxacin. In the case of ceftazidime, RidgeClassifier performed well and showed F1-score (0.652), precision (0.654), and specificity (0.652) values. For meropenem, KNeighborsClassifier exhibited moderate F1-score (0.629), precision (0.629), and specificity (0.629). Among these three antibiotics, GEXP data on meropenem and ceftazidime improved diagnostic performance. The findings will pave the way for the establishment of a resistance profiling tool that can predict AMR on the basis of transcriptomic markers
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