Jumeira University

Invasive meningococcal disease poses a substantial disease burden worldwide, including older adults (aged ≥ 56years) along with higher case fatality rates. MenACYW-TT, a quadrivalent meningococcal tetanus toxoid-conjugate vaccine, is indicated worldwide in individuals ≥ 56years of age and is the only licensed vaccine in the USA for this age group. The annual Hajj pilgrimage is considered an epidemiologic event with approximately two million pilgrims per year; hence, immunization with a quadrivalent meningococcal vaccine is mandatory for visitors traveling to Saudi Arabia for Umrah and Hajj. We evaluated the immunogenicity and safety of a single dose of MenACYW-TT in prospective Hajj/Umrah pilgrims aged ≥ 56years in Turkiye and Lebanon. In this open-label phaseIII study (NCT03869866), conducted in Turkiye and Lebanon, healthy adults aged ≥ 56years received a single dose of the MenACYW-TT vaccine. Serum bactericidal assays were performed using human (hSBA) and rabbit (rSBA) complements to measure antibody titers against all four serogroups at baseline (D0) and 30days post vaccination (D30). Safety data were collected up to 30days (+ 14days) post vaccination. A total of 290 individuals were enrolled in the study. On D30, vaccine seroprotection rates (hSBA titers ≥ 1:8) for serogroupsA, C, Y, and W were 82.8%, 92.5%, 92.1%, and 83.5%, respectively, and rSBA titers (≥ 1:128) were 83.4%, 90.0%, 94.3%, and 88.9%, respectively. hSBA geometric mean titers (GMTs) were 32 [95% confidence interval (CI) 26.3-39.1], 132 (102-169), 126 (99.3-161), and 56.4 (43.8-72.6) for serogroupsA, C, Y, and W, respectively. On D30, seroresponse rates in the per-protocol analysis set (PPAS) were 48-71.0%. Safety concerns and any serious adverse events related to the study vaccine were not observed. MenACYW-TT vaccine induced a robust immune response against all serogroups (per seroprotection rates and GMTs). A single dose of the vaccine demonstrated acceptable safety profile in potential pilgrims aged ≥ 56years. ClinicalTrials.gov, NCT03869866. A Graphical Abstract is available for this article.

BackgroundStroke remains a leading cause of long-term disability, with limited recovery potential during the chronic phase. Hyperbaric oxygen therapy has shown promise in promoting neuroplasticity and functional recovery through mechanisms such as the hyperoxic-hypoxic paradox.Case presentationWe report the case of a 45-year-old Arabic male who experienced a left-sided hemorrhagic stroke and presented with persistent neurological deficits 15 months post-event. He exhibited right hemiparesis, impaired gait requiring a wheelchair, and cognitive dysfunction. The patient underwent 83 sessions of hyperbaric oxygen therapy over 16 weeks (2.0 ATA, 90 minutes with air breaks). Pre- and postintervention assessments included neurological and cognitive evaluations alongside advanced imaging: diffusion tensor imaging and Single photon emission computed tomography. Clinically, the patient showed marked improvements in muscle strength, spasticity, balance, and walking—progressing from wheelchair dependence to ambulation with a quadruped cane. Cognitive testing demonstrated improved attention, verbal memory, and processing speed. Imaging findings supported these changes: diffusion tensor imaging showed increased fractional anisotropy in major white matter tracts, and single photon emission computed tomography demonstrated significant perfusion increases in the right motor cortex (+ 15.83%) and right frontal lobe (+ 15.92%).ConclusionThis case highlights hyperbaric oxygen therapy’s potential to facilitate recovery in chronic post-stroke stages by enhancing neuroplasticity and neurovascular function in nonnecrotic brain regions. Advanced imaging techniques, such as diffusion tensor imaging and single photon emission computed tomography, provide valuable insights into treatment efficacy and may support personalized therapeutic protocols in the future.

The aim of this review was to evaluate the risk of COVID-19 cytokine release syndrome (CRS) with HIV infection and meta-regress for indicator covariates. Electronic databases, including Google Scholar, Cochrane Library, Web of Sciences (WOS), EMBASE, Medline/PubMed, COVID-19 Research Database, and Scopus, were systematically searched till February 30, 2022. All human studies were included, irrespective of publication date or region. Eleven studies, with a total of 2,005,274 detailing cytokine release syndrome defined by specific parameters, were included. To pool the estimate, a random-effects model with risk ratio (RR) as the effect measure was used. Moreover, publication bias and sensitivity analysis were evaluated followed by meta-regression analysis to account for any possible covariates. This systematic review, meta-analysis, and meta-regression trial was registered (CRD42021264761) on the PROSPERO register. HIV infection showed an increased risk for COVID-19 cytokine release syndrome (RR= 1.48, 95% CI (1.16, 1.88) (P=0.002)) with substantial heterogeneity (I2 > 80%) and a 4.6% cumulative incidence. The true effects size in 95% of all the comparable populations (prediction interval) fell between 0.67 to 3.29. HIV infection further showed an increased risk for intensive care unit (ICU) admission ((P<0.0001) (I² = 0%)] and mechanical ventilation (MV) ((P=0.04) (I² = 0%)) as the key indicators of cytokine release syndrome. Meta-regression analysis demonstrated that COVID-19 cytokine release syndrome was influenced by the year a study was published (R² = 0.55) and the region from where the study was conducted (R² = 0.11). On meta-regression analysis, the combined impact of all covariates in the model explained at least some of the variance in effect size (Q = 16.21, df = 6, P= 0.0127), and the proportion of variance explained by covariates on comparing the model with and without the covariates was 73 % and highly significant (Tau² = 0.1100, Tau = 0.3317, I² = 86.5%, Q = .99, df = 10, P<0.0001) (R² = 0.73). Our updated meta-analysis indicated that HIV infection was significantly associated with an increased risk for COVID-19 cytokine release syndrome, which, in addition, might be moderated by the year a study was published and the region in which the study was conducted. Further, the risk for intensive care unit (ICU) admission and mechanical ventilation (MV) were identified as the key indicators of cytokine release syndrome. We believe the updated data anchoring cytokine release syndrome will contribute to more substantiation of the findings reported by similar earlier studies.

This research study is an attempt to investigate and recommend the applications and usefulness of social media as a knowledge sharing platform using smart mobile devices and students’ academic performances. Research literature reveals that further examination of this idea is a must as it is increasingly becoming an integral part and a current mandate for digital-era learners. This study is exploratory based on learners’ data which includes students from foundation, junior and senior courses at a university. The objective was to cognize learners’ perceptions on collaborative learning while using applications of social media knowledge platform and smart mobile devices. Structural Model approach is followed to validate the data and undertake the required analysis. The resulting analysis shows that a significantly positive relation exists between the available platforms of knowledge applications using smart devices and its substantial impact on students’ overall performances. In addition, it could be observed that generally students have positive and valuable perception on the use of social media knowledge platform and smart mobile devices in collaborative learning settings. Moreover, such settings encourage students-students’ interactions, studentsteachers’ interactions, and the growing trends of students to online experts’ interactions. Furthermore, it was noticed that these interactions improve students’ engagement with such platforms using smart devices, which consequently has significant positive impact on students’ academic performances. The findings propose the inculcation in students the positive role of the use of social media knowledge platforms using smart devices so to encourage collaborative learning, to enhance active learning, to be more dynamic &amp; engaging, to broaden their domain-knowledge, and to promote academic researchoriented learning, which are all crucial for graduate employability.

Given a square integrable m-dimensional random variable $ X $ on a probability space $ (\Omega, {{\mathcal{F}}}, {{\mathbb P}}) $ and a sub sigma algebra $ {{\mathcal{A}}} $, we show that there exists another m-dimensional random variable $ Y $, independent of $ {{\mathcal{A}}} $ and minimising the $ L^2 $ distance to $ X $. The construction of $ Y $ is done in two steps. First, using transport theory we determine the distribution of $ Y $, in a second step we construct the variable $ Y $ using the existence of a uniformly $ [0, 1] $ distributed random variable independent of $ {{\mathcal{A}}} $. Such results have an importance to fairness and bias reduction in Artificial Intelligence, Machine Learning and Network Theory. We believe that the problem was never studied in the past.

Despite research on big data analytics being conducted in past, there are no studies focused on the opportunities i.e., growth prospects and its associated challenges in specific industries in UAE. In this study, big data analytics were used in SMEs retail sector in UAE region to assess the use of these technologies. Companies can use these to gain new understandings from their customers and suppliers perspective utilizing big data analytics techniques to take informative decisions on pricing, inventory management, payment solutions and marketing decisions. Big data can be used to help SMEs foresee the needs and preferences of their target audiences. The focus of this study is on SMEs since these are the pillar of every economy and can make quick adjustments to productivity changes. It aims to analyze and identify major chances and threats of Big Data and propose the best ways for SMEs to utilize it for improving their efficiency and business practices.

Stock market indices are considered to be a powerful economic indicator. These indices can be classified based on the methodology of weight allocation for each stock and the rules governing the entry, retention and exit criteria of various stocks in the index. This paper presents a descriptive and an exploratory analysis carried out on the daily returns data of NASDAQ 100 (^NDX) index and shortlist of 20 stocks in the index. Random sampling was conducted at the sector level strata of all stocks that make up the index. This approach was followed to avoid selection bias and that stocks from the varied sectors are represented equally for this analysis. R-squared values and correlation coefficients were used to determine the explain-ability and relationship between the stock returns and the index returns respectively. The paper applied descriptive univariate analysis on daily returns at an individual stock level and at an aggregated sector level. Inter-relationship between stocks and the index returns was carried out by computing Pearson’s correlation coefficient across the different combinations of stocks and index return values. Linear regression was carried out identify the explain ability of the variance in the returns of from the index to the returns from the stocks. All analysis was carried out using the python and the stats-models library. As anticipated, the returns of randomly picked 20 stocks were able to explain ~85 % of the variance of the returns of index. One of the primary focus of the paper was to explore whether NASDAQ-100 index can explain the variability of the technology stocks relatively more than the stocks that belong to other sectors in its portfolio owing to the nature of most stocks that make up the index.

Abstract Faced with the combined challenge of aging sewer infrastructure and rapid population growth, the Abu Dhabi Sewerage Services Company (ADSSC) has embarked on a bold approach to build an enhanced sewer infrastructure with a deep gravity sewer tunnel at the centre. The primary lining of the new 41 km‐long deep sewer tunnel has been completed by tunnel boring machines (TBM). The concrete segmental lining has an internal diameter varying from 4 to 5.5 m internal diameter and is located at depths of 20 to 80 m below the ground surface. This paper discusses the results of observations made on segment cracking experienced at different stages in the installation of concrete lining for the first of three TBM drives in Contract T‐02 of the Abu Dhabi Strategic Tunnel Enhancement Program (STEP). It also attempts to quantify correlations and ranking among various mechanical and geometrical variables of ring installation based on their contribution to segment cracking. The method of assessment used in the case study has been successfully verified on a separate project, which encouraged the author to develop and propose a generalized procedure for investigation to serve as a roadmap in the future projects with segment cracking issues.

In this research, the removal of 2,4 dinitrophenol, 2 nitrophenol and 4 nitrophenol from aqueous solution using char ash from animal bones was investigated using batch technique. Three 2-parameter isotherms (Freundlich, Langmuir, and Temkin) were applied to analyze the experimental data. Both linear and nonlinear regression analyses were performed for these models to estimate the isotherm parameters. Three 3-parameter isotherms (Redlich-Peterson, Sips, Toth) were also tested. Moreover, the kinetic data were tested using pseudo-first order, pseudo-second order, Elovich, Intraparticle diffusion and Boyd methods. Langmuir adsorption isotherm provided the best fit for the experimental data indicating monolayer adsorption. The maximum adsorption capacity was 8.624, 7.55, 7.384 mg/g for 2 nitrophenol, 2,4 dinitrophenol, and 4 nitrophenol, respectively. The experimental data fitted well to pseudo-second order model suggested a chemical nature of the adsorption process. The R2 values for this model were 0.973 up to 0.999. This result with supported by the Temkin model indicating heat of adsorption to be greater than 10 kJ/mol. The rate controlling step was intraparticle diffusion for 2 nitrophenol, and a combination of intraparticle diffusion and film diffusion for the other two phenols. The pH and temperature of solution were found to have a considerable effect, and the temperature indicated the exothermic nature of the adsorption process. The highest adsorption capacity was obtained at pH 9 and 25 °C.