Patterns of tobacco smoking and nicotine vaping among university students in the united arab emirates: A cross-sectional study

Various forms of tobacco smoking and nicotine vaping tools are available on the market. This study quantified the prevalence of and identified factors associated with patterns of smoking and nicotine vaping among university students in the United Arab Emirates (UAE). A cross-sectional sample of students enrolled in three public universities was surveyed. Self-reported current smoking and nicotine vaping were recorded. Of 1123 students, 81.7% completed the online survey (mean age, 20.7 ± 3.4 (SD) years; 70.7% females). The prevalence of current smoking was 15.1% while the prevalence of current nicotine vaping was nearly 4.0%. Among current smokers, 54.7% reported conventional smoking only, 15.1% reported nicotine vaping only, and 28.8% were poly-users. Conventional midwakh (47.5%), followed by conventional shisha/waterpipe (36.7%), conventional cigarettes (36.7%), electronic shisha/waterpipe (25.2%), and electronic cigarettes (24.5%), were most commonly reported by students. Students aged 20–25 years (adjusted odds ratios (aOR): 2.08, 95% confidence interval (CI): 1.18–3.67) or \u3e25 years (aOR: 4.24, 95% CI: 1.41–12.80) had higher odds of being current smokers compared to those aged 17–19 years. The male gender was also independently associated with higher odds of being a current smoker (aOR: 5.45, 95% CI: 3.31–8.97) as well as higher odds of smoking cigarettes, shisha, and midwakh, or nicotine vaping compared to being female. Of nicotine vaping users, 36.1% reported using nicotine vaping because they enjoyed the flavor and vaporizing experience and 34.4% used it to help them to quit smoking. A relatively high prevalence of self-reported smoking was reported among university students in the UAE. The findings also suggest that nicotine vaping use is relatively widespread, but still less common than traditional smoking. Vigilant and tailored university-based smoking control and preventive measures are warranted

Natural history, with clinical, biochemical and molecular characterization, of classical homocystinuria in the Qatari population

Classical homocystinuria (HCU) is the most common inborn error of metabolism in Qatar, with an incidence of 1:1800, and is caused by the Qatari founder p.R336C mutation in the CBS gene. This study describes the natural history and clinical manifestations of HCU in the Qatari population. A single center study was performed between 2016 and 2017 in 126 Qatari patients, from 82 families. Detailed clinical and biochemical data were collected and Stanford-Binet intelligence, quality of life and adherence to treatment assessments were conducted prospectively. Patients were assigned to one of three groups, according to mode of diagnosis: 1) Late Diagnosis Group (LDG), 2) Family Screening Group (FSG), and 3) Newborn Screening Group (NSG). Of the 126 patients, 69 (55%) were in the LDG, 44 (35%) in the NSG, and 13 (10%) in the FSG. The leading factors for diagnosis in the LDG were ocular manifestations (49%), neurological manifestations (45%), thromboembolic events (4%), and hyperactivity and behavioral changes (1%). Both FSG and NSG groups were asymptomatic at time of diagnosis. NSG had significantly higher IQ, QoL, and adherence values compared with the LDG. The LDG and FSG had significantly higher Met levels than the NSG. The LDG also had significantly higher tHcy levels than the NSG and FSG. Regression analysis confirmed these results even when adjusting for age at diagnosis, current age or adherence. These findings increase understanding of the natural history of HCU and highlight the importance of early diagnosis and treatment. This article is protected by copyright. All rights reserved.Qatar National Research Fund , Grant/Award Number: 7‐355‐3‐08

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe

Status of the Jiangmen Underground Neutrino Observatory to measure Neutrino Mass Ordering

The Jiangmen Underground Neutrino Observatory (JUNO), a multi-purpose liquid scintillator detector under construction in southern China, has a unique potential to determine the neutrino mass ordering (NMO) with 3σ C.L. in 6 years by observing the vacuum oscillation pattern of reactor antineutrinos at 52.5 km baseline. In order to achieve this challenging goal, a large target mass of 20 kton and an excellent energy resolution of 3% at 1 MeV are prerequisites. Consequently, JUNO will measure the neutrino oscillation parameters $∆m^2_{12}$, $θ_{12}$, and $∆m^2_{13}$ in sub-percent precision. It will also probe the matter-dominated oscillations of atmospheric neutrinos, specifically enhancing the neutrino interaction model at the subGeV energy scale and offering an independent complement to the NMO analysis with reactor anti-neutrinos. This proceeding reviews major updates compared to the previous estimates of JUNO’s sensitivity to NMO as cited in Ref 1

Atmospheric neutrino physics in JUNO: reconstruction of GeV Interaction

The Jiangmen Underground Neutrino Observatory (JUNO), is a 20 kton multi-purpose liquid-scintillator detector to be completed in 2023. Its main goal is the determination of the neutrino mass ordering using the measurement of the vacuum-dominated oscillation pattern of reactor anti-neutrinos from two nearby nuclear power plants. The sensitivity of JUNO to the neutrino mass ordering can be enhanced via a combined analysis of reactor anti-neutrinos with atmospheric neutrinos, in which the matter-dominated oscillation depends on the mass ordering. Such an analysis requires a precise reconstruction of the energy and the direction of atmospheric neutrinos. As the largest liquid-scintillator detector to be built, JUNO will also be able to measure the atmospheric neutrino flux down to lower energies than the current large water/ice Cherenkov detectors. This poster presents the reconstruction of the energy of atmospheric neutrinos with a machine learning approach and the direction reconstruction with a traditional approach. Both approaches are a Monte Carlo based study, the latter focuses on the reconstruction of the photon emission topology in the JUNO detector, while the machine learning approach relies on the geometrical representation of the detector with a Graph Convolutional Neural Network