95 research outputs found
The Burden of Human Parechoviruses on Children in Oman: A retrospective study
Objectives: To study the burden, clinical and laboratory features, and outcome of Human Parechoviruses (HPeVs) infection among children managed at Sultan Qaboos University Hospital (SQUH). Methods: This is a retrospective study of children (< 18 years of age) with molecular proven HPeVinfection managed at SQUH between January 2017 and December 2019. Data was collected from patients’ medical records and analyzed to describe the demographic, clinical and laboratory features, management and outcome. Results: HPeV was detected in 61 patients, 44 (72%) of whom were males. The median age of these patients was 9 months (IQR, 6-15 months). HPeV was detected throughout the year without any significant peaks. The majority of our patients (51; 84%) had co-infection with other viruses. Forty-eight (79%) children with HPeV infection required hospitalization and their median hospital length of stay was 5 days (IQR, 3 - 8 days). Ex-prematurity (10; 16%) was the most common comorbidity seen among this group. Fever (41; 67%) and cough (41; 67%) were the most common presenting symptoms among these children.Two-third of children with HPeV infection in this cohort were managed for lower respiratory tract infection and none for meningitis. Gastroenteritis was not common in our study, only 8 children had diarrhoea. All children had a full recovery. Conclusion: HPeVs does not show a clear seasonality in Oman. Most of the children were < 2 years of age and had a viral co-infection. Outcomes of HPeVs were favorable, with no mortalities, but thorough follow-up of neurological outcomes was lacking.
Keywords: Children; Parechovirus; Infection; Outcome; Oman
INDIGO - INtegrated Data Warehouse of MIcrobial GenOmes with Examples from the Red Sea Extremophiles.
Background: The next generation sequencing technologies substantially increased the throughput of microbial
genome sequencing. To functionally annotate newly sequenced microbial genomes, a variety of experimental and
computational methods are used. Integration of information from different sources is a powerful approach to enhance
such annotation. Functional analysis of microbial genomes, necessary for downstream experiments, crucially
depends on this annotation but it is hampered by the current lack of suitable information integration and exploration
systems for microbial genomes.
Results: We developed a data warehouse system (INDIGO) that enables the integration of annotations for
exploration and analysis of newly sequenced microbial genomes. INDIGO offers an opportunity to construct complex
queries and combine annotations from multiple sources starting from genomic sequence to protein domain, gene
ontology and pathway levels. This data warehouse is aimed at being populated with information from genomes of
pure cultures and uncultured single cells of Red Sea bacteria and Archaea. Currently, INDIGO contains information
from Salinisphaera shabanensis, Haloplasma contractile, and Halorhabdus tiamatea - extremophiles isolated from
deep-sea anoxic brine lakes of the Red Sea. We provide examples of utilizing the system to gain new insights into
specific aspects on the unique lifestyle and adaptations of these organisms to extreme environments.
Conclusions: We developed a data warehouse system, INDIGO, which enables comprehensive integration of
information from various resources to be used for annotation, exploration and analysis of microbial genomes. It will
be regularly updated and extended with new genomes. It is aimed to serve as a resource dedicated to the Red Sea
microbes. In addition, through INDIGO, we provide our Automatic Annotation of Microbial Genomes (AAMG) pipeline.
The INDIGO web server is freely available at http://www.cbrc.kaust.edu.sa/indigo.IA and AAK were supported from the KAUST CBRC Base Fund of VBB. WBa and VBB were supported from the KAUST Base Funds of VBB. US was supported by the KAUST Base Fund of US. This study was partly supported by the Saudi Economic and Development Company (SEDCO) Research Excellence award to US and VBB. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript
Identification of Asymptomatic Severe Acute Respiratory Syndrome Coronavirus 2 Infections among Healthcare Workers at Sultan Qaboos University Hospital, Oman
Objectives: This study aimed to describe the incidence and features of asymptomatic COVID-19 infections among HCWs at a tertiary hospital in Oman. Methods: This cross-sectional study was conducted between August 2020 and February 2021 among HCWs with no history of COVID-19 infection using an online questionnaire to collect sociodemographic and clinical data. COVID-19 infection was diagnosed using nasopharyngeal/throat swabs, which were tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Analyses were performed using Chi-squared test, Fisher’s exact test, or univariate ordinary least squares regression, as appropriate. Results: A total of 583 HCWs participated in the study. Most were female (56.6%) and the mean age was 35 ± 8 years . Only 9.6% (95% confidence interval [CI]: 7.3–12.3%) of the HCWs were at high exposure risk as they were directly involved in the care of COVID-19-infected patients. Overall, 4.1% (95% CI: 2.7–6.1%) of the HCWs screened positive for SARS-CoV-2; of these, five (20.8%) developed symptoms within two weeks. The frequency of SARS-CoV-2 positivity among HCWs working in high, intermediate, low, and miscellaneous risk areas was 1.8% (95% CI: <0.1–9.6%), 2.6% (95% CI: <0.1–6.5%), 5.3% (95% CI: 0.3–9.3%), and 4.8% (95% CI: <0.1–69.3%), respectively. Working in high-risk areas was associated with increased compliance with various infection control strategies (P <0.001). Conclusion: There was a greater frequency of SARS-CoV-2 positivity among HCWs working in lower-risk areas, whereas HCWs who worked in high-risk areas were significantly more likely to report increased compliance with infection control strategies.
Keywords: SARS-CoV-2; COVID-19 Nucleic Acid Testing; Asymptomatic Infections; Health Personnel; Occupational Exposure; Infection Control; Real-Time Polymerase Chain Reaction; Oman
The Creation of the System of Heir-Apparent by Mu’awiyah bin Abu Sufian in (680 A.D) and Reactions against it
The study highlights the political issue that played a major role in shaping the system of government in the Islamic state. This is the creation of the system of “heir apparent” by Mu’awiyah bin Abu Sufian, the Umayyad Caliph. The study follows a descriptive approach based on historical analysis and comparison of accounts, and then follows the reactions against that system. The findings of the study show that the system of “heir apparent” was one of the causes of bloody conflicts and violent strife experienced by the Islamic State, and was, in fact, a major cause of the fall of the Umayyad State in the East, in 132 AH / 750 AD
The implications of model–informed drug discovery and development for tuberculosis
The research leading to these results received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement n°115337, the resources of which comprise financial contributions from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contribution.Despite promising advances in the field and highly effective first-line treatment, an estimated 9.6 million people are still infected with tuberculosis (TB). Innovative methods are required to effectively transition the growing number of compounds into novel combination regimens. However, progression of compounds into patients occurs despite the lack of clear understanding of the pharmacokinetic-pharmacodynamic (PK/PD) relations. The PreDiCT-TB consortium was established in response to the existing gaps in TB drug development. The aim of the consortium is to develop new preclinical tools in concert with an in silico model-based approach, grounded in PKPD principles. Here, we highlight the potential impact of such an integrated framework on various stages in TB drug development and on the dose rationale for drug combinations.PostprintPeer reviewe
DETECTION OF PNEUMONIA BY USING NINE PRE-TRAINED TRANSFER LEARNING MODELS BASED ON DEEP LEARNING TECHNIQUES
Pneumonia is a serious chest disease that affects the lungs. This disease has become an important issue that must be taken care of in the field of medicine due to its rapid and intense spread, especially among people who are addicted to smoking. This paper presents an efficient prediction system for detecting pneumonia using nine pre-trained transfer learning models based on deep learning technique (Inception v4, SeNet-154, Xception, PolyNet, ResNet-50, DenseNet-121, DenseNet-169, AlexNet, and SqueezeNet). The dataset in this study consisted of 5856 chest x-rays, which were divided into 5216 for training and 624 for the test. In the training phase, the images were pre-processed by resizing the input images to the same dimensions to reduce complexity and computation. The images are then forwarded to the proposed models (Inception v4, SeNet-154, Xception, PolyNet, ResNet-50, DenseNet-121, DenseNet-169, AlexNet, SqueezeNet) to extract features and classify the images as normal or pneumonia. The results of the proposed models (Inception v4, SeNet-154, Xception, PolyNet, ResNet-50, DenseNet-121 DenseNet-169, AlexNet and SqueezeNet) give accuracies (98.72%, 98.94%, 98.88%, 98.72%, 96.2%, 94.69%, 96.29%, 95.01% and 96.10%) respectively. We found that the SeNet-154 model gave the best result with an accuracy of 98.94% with a validation loss (0.018103). When comparing our results with older studies, it should be noted that the proposed method is superior to other methods
Evaluation of statistical approaches for association testing in noisy drug screening data
Background Identifying associations among biological variables is a major challenge in modern quantitative biological research, particularly given the systemic and statistical noise endemic to biological systems. Drug sensitivity data has proven to be a particularly challenging field for identifying associations to inform patient treatment. Results To address this, we introduce two semi-parametric variations on the commonly used concordance index: the robust concordance index and the kernelized concordance index (rCI, kCI), which incorporate measurements about the noise distribution from the data. We demonstrate that common statistical tests applied to the concordance index and its variations fail to control for false positives, and introduce efficient implementations to compute p-values using adaptive permutation testing. We then evaluate the statistical power of these coefficients under simulation and compare with Pearson and Spearman correlation coefficients. Finally, we evaluate the various statistics in matching drugs across pharmacogenomic datasets. Conclusions We observe that the rCI and kCI are better powered than the concordance index in simulation and show some improvement on real data. Surprisingly, we observe that the Pearson correlation was the most robust to measurement noise among the different metrics.Peer reviewe
Creating reproducible pharmacogenomic analysis pipelines
BSTRACT"/jats:title""jats:p"The field of Pharmacogenomics presents great challenges for researchers that are willing to make their studies reproducible and shareable. This is attributed to the generation of large volumes of high-throughput multimodal data, and the lack of standardized workflows that are robust, scalable, and flexible to perform large-scale analyses. To address this issue, we developed pharmacogenomic workflows in the Common Workflow Language to process two breast cancer datasets in a reproducible and transparent manner. Our pipelines combine both pharmacological and molecular profiles into a portable data object that can be used for future analyses in cancer research. Our data objects and workflows are shared on Harvard Dataverse and Code Ocean where they have been assigned a unique Digital Object Identifier, providing a level of data provenance and a persistent location to access and share our data with the community.
Document type: Preprin
PRMT inhibition induces a viral mimicry response in triple-negative breast cancer
Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype with the worst prognosis and few effective therapies. Here we identified MS023, an inhibitor of type I protein arginine methyltransferases (PRMTs), which has antitumor growth activity in TNBC. Pathway analysis of TNBC cell lines indicates that the activation of interferon responses before and after MS023 treatment is a functional biomarker and determinant of response, and these observations extend to a panel of human-derived organoids. Inhibition of type I PRMT triggers an interferon response through the antiviral defense pathway with the induction of double-stranded RNA, which is derived, at least in part, from inverted repeat Alu elements. Together, our results represent a shift in understanding the antitumor mechanism of type I PRMT inhibitors and provide a rationale and biomarker approach for the clinical development of type I PRMT inhibitors
Assessing the importance of car meanings and attitudes in consumer evaluations of electric vehicles
This paper reports findings from a research study which assesses the importance of attitudinal constructs related to general car attitudes and the meanings attached to car ownership over evaluations of electric vehicles (EVs). The data are assessed using principal component analysis to evaluate the structure of the underlying attitudinal constructs. The identified constructs are then entered into a hierarchical regression analysis which uses either positive or negative evaluations of the instrumental capabilities of EVs as the dependent variable. Results show that attitudinal constructs offer additional predictive power over socioeconomic characteristics and that the symbolic and emotive meanings of car ownership are as, if not more, effective in explaining the assessment of EV instrumental capability as compared to issues of cost and environmental concern. Additionally, the more important an individual considers their car to be in their everyday life, the more negative their evaluations are of EVs whilst individuals who claim to be knowledgeable about cars in general and EVs in particular have a lower propensity for negative EV attitudes. However, positive and negative EV attitudes are related to different attitudinal constructs suggesting that it is possible for someone to hold both negative and positive assessments at the same time
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