The impact of online classes on sleep, physical activity, and cognition functioning among physical education students.

Introduction: Online education has become a crucial component of teachers' professional development, and universities incorporate innovative pedagogical approaches to enhance teachers' training. These approaches have proven invaluable, particularly during the COVID-19 pandemic. This study investigates the impact of online versus face-to-face learning environments on sleep quality, physical activity, and cognitive functioning among physical education students. Methods: Utilizing a unique methodological approach that combines wrist actigraphy, the Pittsburgh Sleep Quality Index, and the Cambridge Neuropsychological Test Automated Battery, we provide a comprehensive assessment of these variables. Over 4 weeks, 19 male students participated in alternating online and face-to-face class formats. Results: Our results reveal no significant differences in sleep quality or cognitive function between learning environments. However, notable findings include significant differences in Paired Associates Learning and weekday step counts in the face-to-face setting. Discussion: These insights suggest that while online learning environments may not adversely affect sleep or cognitive functions, they could impact certain aspects of physical activity and specific cognitive tasks. These findings contribute to the nuanced understanding of online learning's implications and can inform the design of educational strategies that promote student well-being.

Use of machine learning to assess factors affecting progression, retention, and graduation in first-year health professions students in Qatar: a longitudinal study.

BACKGROUND: Across higher education, student retention, progression, and graduation are considered essential elements of students' academic success. However, there is scarce literature analyzing these attributes across health professions education. The current study aims to explore rates of student retention, progression, and graduation across five colleges of the Health Cluster at Qatar University, and identify predictive factors. METHODS: Secondary longitudinal data for students enrolled at the Health Cluster between 2015 and 2021 were subject to descriptive statistics to obtain retention, progression and graduation rates. The importance of student demographic and academic variables in predicting retention, progression, or graduation was determined by a predictive model using XGBoost, after preparation and feature engineering. A predictive model was constructed, in which weak decision tree models were combined to capture the relationships between the initial predictors and student outcomes. A feature importance score for each predictor was estimated; features that had higher scores were indicative of higher influence on student retention, progression, or graduation. RESULTS: A total of 88% of the studied cohorts were female Qatari students. The rates of retention and progression across the studied period showed variable distribution, and the majority of students graduated from health colleges within a timeframe of 4-7 years. The first academic year performance, followed by high school GPA, were factors that respectively ranked first and second in importance in predicting retention, progression, and graduation of health majors students. The health college ranked third in importance affecting retention and graduation and fifth regarding progression. The remaining factors including nationality, gender, and whether students were enrolled in a common first year experience for all colleges, had lower predictive importance. CONCLUSIONS: Student retention, progression, and graduation at Qatar University Health Cluster is complex and multifactorial. First year performance and secondary education before college are important in predicting progress in health majors after the first year of university study. Efforts to increase retention, progression, and graduation rates should include academic advising, student support, engagement and communication. Machine learning-based predictive algorithms remain a useful tool that can be precisely leveraged to identify key variables affecting health professions students' performance.

Monitoring SEIRD model parameters using MEWMA for the COVID-19 pandemic with application to the state of Qatar.

During the current COVID-19 pandemic, decision-makers are tasked with implementing and evaluating strategies for both treatment and disease prevention. In order to make effective decisions, they need to simultaneously monitor various attributes of the pandemic such as transmission rate and infection rate for disease prevention, recovery rate which indicates treatment effectiveness as well as the mortality rate and others. This work presents a technique for monitoring the pandemic by employing an Susceptible, Exposed, Infected, Recovered, Death model regularly estimated by an augmented particle Markov chain Monte Carlo scheme in which the posterior distribution samples are monitored via Multivariate Exponentially Weighted Average process monitoring. This is illustrated on the COVID-19 data for the State of Qatar.

Association of Serum Vitamin D level and COVID-19 infection: A Case-control Study.

BACKGROUND: Vitamin D is considered a potent modulator of the immune system, albeit its role in COVID-19 infection is a matter of debate. The present study aimed to estimate the association between serum vitamin D levels and COVID-19 among people in Qatar. METHODS: This case-control study, approved by the Institutional Review Board of Primary Health Care Corporation (PHCC) Qatar, retrospectively evaluated the principal public healthcare sector population data repository retrieved from the cloud-based Electronic Health Record (EHR) software-Cerner, during April 2020-2021. The health records of all adult patients aged >18 years who had undergone the reverse transcription-polymerase chain reaction (RT-PCR) test and whose medical records had documented serum 25-hydroxyvitamin D [25 (OH)D] levels were analyzed. RESULTS: A total of 924,173 EHRs were extracted, of which 62,451 EHR comprised of 16,446 (26.3%) COVID-19 patients and 46,005 (73.7%) negative-control group patients met the inclusion criteria. The odds ratio (OR) among different categories of vitamin D deficiency (VDD) revealed that people with mild/moderate VDD were 1.18 times (95% CI 1.126-1.258) and those with severe VDD were 1.90 times (95% CI 1.116-1.251) more likely to have COVID-19 infection when compared to the people with optimal serum vitamin D level. On applying multiple logistic regression, the odds of having COVID-19 infection were found to be 1.27 times (95% CI 1.184-1.371) higher among those with mild/moderate VDD and 1.32 times (95% CI 1.206-1.405) higher among those with severe VDD when compared to people with optimal vitamin D level (p < 0.001). CONCLUSION: Our findings demonstrated a significant association between the suboptimal serum vitamin D level and COVID-19 infection. Further studies are required to determine the effects of VDD on the severity and outcomes of COVID-19 infections.

Factors affecting university image among graduate alumni: A case study of Qatar university.

Various factors influence the students' perception of universities and university image. This study explores five factors affecting university image among Graduate Alumni. Surveys, administered to 597 graduate alumni were assessed to determine Graduate Alumni perspectives toward their university. Findings revealed that the key factors that impacted graduate alumni affecting the university's image and reputation were gender, nationality, level of study, and the ability of the institution to equip graduates with certain specific skills. Based on these findings, the university should consider further examining these areas to provide a more in-depth understanding of how these factors work to shape graduate students' perspectives of the university and develop ways to address areas that need to be developed and improved.

Student support in higher education: campus service utilization, impact, and challenges.

Worldwide, university student support services facilitate student performance, contribute to students' success, and increase students' chances of degree completion. Student support services programs' success depends on students' help-seeking behavior. This study explores the help-seeking behavior of Foundation Program and Undergraduate students at Qatar University (QU) through their use of campus services to better understand students' use of these services. The study examines the association between help-seeking behavior, as indicated through services, on student success and persistence in two consecutive semesters, Spring 2019 and Fall 2019. Findings report a significant association between students' services and student success and persistence. A significant difference was reported between at-risk students' majors and at-risk students in STEM and non-STEM majors. Also, there was a difference in the help-seeking behavior among males and females, nationals and non-nationals, and student classifications.

Forecasting the impact of environmental stresses on the frequent waves of COVID19.

A novel approach to link the environmental stresses with the COVID-19 cases is adopted during this research. The time-dependent data are extracted from the online repositories that are freely available for knowledge and research. Since the time series data analysis is desired for the COVID-19 time-dependent frequent waves, here in this manuscript, we have developed a time series model with the aid of "nonlinear autoregressive network with exogenous inputs (NARX)" approach. The distribution of infectious agent-containing droplets from an infected person to an uninfected person is a common form of respiratory disease transmission. SARS-CoV-2 has mainly spread via short-range respiratory droplet transmission. Airborne transmission of SARS-CoV-2 seems to have occurred over long distances or times in unusual conditions; SARS-CoV-2 RNA was found in PM10 collected in Italy. This research shows that SARS-CoV-2 particles adsorbed to outdoor PM remained viable for a long time, given the epidemiology of COVID-19, outdoor air pollution is unlikely to be a significant route of transmission. In this research, ANN time series is used to analyze the data resulting from the COVID-19 first and second waves and the forecasted results show that air pollution affects people in different areas of Italy and make more people sick with covid-19. The model is developed based on the disease transmission data of Italy.

Effects of ambient climate and three warming treatments on fruit production in an alpine, subarctic meadow community.

PREMISE: Climate change is having major impacts on alpine and arctic regions, and inter-annual variations in temperature are likely to increase. How increased climate variability will impact plant reproduction is unclear. METHODS: In a 4-year study on fruit production by an alpine plant community in northern Sweden, we applied three warming regimes: (1) a static level of warming with open-top chambers (OTC), (2) press warming, a yearly stepwise increase in warming, and (3) pulse warming, a single-year pulse event of higher warming. We analyzed the relationship between fruit production and monthly temperatures during the budding period, fruiting period, and whole fruit production period and the effect of winter and summer precipitation on fruit production. RESULTS: Year and treatment had a significant effect on total fruit production by evergreen shrubs, Cassiope tetragona, and Dryas octopetala, with large variations between treatments and years. Year, but not treatment, had a significant effect on deciduous shrubs and graminoids, both of which increased fruit production over the 4 years, while forbs were negatively affected by the press warming, but not by year. Fruit production was influenced by ambient temperature during the previous-year budding period, current-year fruiting period, and whole fruit production period. Minimum and average temperatures were more important than maximum temperature. In general, fruit production was negatively correlated with increased precipitation. CONCLUSIONS: These results indicate that predicted increased climate variability and increased precipitation due to climate change may affect plant reproductive output and long-term community dynamics in alpine meadow communities.

The Reliability, Validity and Normative Scores of the Bene-Anthony Family Relations Test for Use With Arab Children.

BACKGROUND: The Bene-Anthony Family Relations Test (BAFRT) is one of the most widely used measures of family dynamics seen from a child's perspective. However, the most common issue surrounding this test is the lack of accurate normative scores for use with non-white ethnic groups. The purpose of this study was to examine the BAFRT's reliability and validity for use with Arab children, as well as to provide normative data for this group. METHODS: The BAFRT was translated into Arabic and back-translated to ensure accuracy. The test was administered to a cohort of 394 Arab children, consisting of both cognitively normal children (n = 269) and children diagnosed with a psychological disorder (n = 125), all aged 5-8 years old. Test-retest reliability was assessed using a sub-set of children and validity was tested against clinical status as well as CBCL and SDQ measures. Normative measures were calculated after examining the impact of influencing variables such as age and gender. RESULTS: Statistical analyses showed that in our cohort of Arab children the BAFRT has good test-retest reliability, correlates well with measures of emotional and behavioral adjustment, and discriminates accurately between clinical and non-clinical children. Age, gender, and clinical status all significantly impacted upon BAFRT scores and therefore normative values are presented from our cohort when considering these variables. CONCLUSION: The normative scores we present will provide researchers and clinicians an appropriate reference point for the comparison of scores from Arab children and a starting point for future research into this area.

Assessment of lung cancer risk factors and mortality in Qatar: A case series study.

BACKGROUND: The global burden of cancer has exponentially increased over the last few years. In 2018 alone, approximately more than half of the 18.1 million individuals who had cancer succumbed to it. Lung cancer cases and fatalities are particularly on the rise. Therefore, exploring the factors surrounding lung cancer mortality is of utmost importance. AIMS: We investigate the clinicopathological and epidemiological characteristics of patients with lung cancer undergoing treatments, and their 5-year survival rates from a case series study in Qatar. METHODS AND RESULTS: All patients' data (between January 2010 and December 2014) in this case series study were retrieved from Al-Amal Hospital database. Kaplan-Meier survival plots, life tables and Cox regression were utilized for the statistical analysis. A total of 229 lung cancer patients were included in this study; of which 23.6% are Qatari (40 males and 14 females) and 76.4% non-Qatari (133 males and 42 females). Approximately 57.6% of our patients received at least one type of treatment. We observe a 5-year survival rate of 9.4% in our patient cohort. We also observe other predictive factors, such as distant metastasis (adjusted hazards ratio, HR = 2.414, 95% CI: 1.035-5.632), smoking status (adjusted HR = 3.909, 95% CI: 1.664-9.180) and the treatment history (adjusted HR = 0.432, 95% CI: 0.270-0.691), to be significant. CONCLUSION: Lung cancer is a prevalent health condition in Qatar, particularly owing to the rising use of tobacco in the country. The survival rate for lung cancer patients in this country is lower, compared to the global average. Moreover, several factors such as distant metastasis, smoking status, and treatment history are associated with lung cancer survival in Qatar.

Assessing spatial confounding in cancer disease mapping using R.

BACKGROUND: Exploring spatial patterns in the context of cancer disease mapping (DM) is a decisive approach to bring evidence of geographical tendencies in assessing disease status and progression. However, this framework is not insulated from spatial confounding, a topic of significant interest in cancer epidemiology, where the latent correlation between the spatial random effects and fixed effects (such as covariates), often lead to misleading interpretation. AIMS: To introduce three popular approaches (RHZ, HH and SPOCK; details in paper) often employed to tackle spatial confounding, and illustrate their implementation in cancer research via the popular statistical software R. METHODS: As a solution to alleviate spatial confounding, restricted spatial regressions are constructed by either projecting the latent effect onto the orthogonal space of covariates, or by displacing the spatial locations. Popular parametric count data models, such as the Poisson, generalized Poisson and negative binomial, were considered for the areal count responses, while the spatial association is quantified via the conditional autoregressive (CAR) model. Our method of inference in Bayesian, sometimes aided by the integrated nested Laplace approximation (INLA) to accelerate computing. The methods are implemented in the R package RASCO available from the first author's GitHub page. RESULTS: The results reveal that all three methods perform well in alleviating the bias and variance inflation present in the spatial models. The effects of spatial confounding were also explored, which, if ignored in practice, may lead to wrong conclusions. CONCLUSION: Spatial confounding continues to remain a critical bottleneck in deriving precise inference from spatial DM models. Hence, its effects must be investigated, and mitigated. Several approaches are available in the literature, and they produce trustworthy results. The central contribution of this paper is providing the practitioners the R package RASCO, capable of fitting a large number of spatial models, as well as their restricted versions.

Association between Soft Drink Consumption and Aggressive Behaviour among a Quarter Million Adolescents from 64 Countries Based on the Global School-Based Student Health Survey (GSHS).

Soft drink consumption has become a significant public health concern that is associated with various adverse health outcomes. We aim to examine the association between soft drink consumption and aggressive behavior among adolescents. We used open access data from 79 studies in 64 countries, including 263,890 adolescents aged 12-18 years who completed the global school-based student health survey (GSHS). Self-reported data on past 30-day carbonated soft drink consumption (number of times per day) and past 12-month physical fighting were utilized for analysis. Of the 263,890 participants (48% boys) aged 12-18 years, the weighted mean frequency of soft drink consumption varied from 0.5 in Kiribati to 2.5 times/day in Surname, while the weighted prevalence of frequent aggressive behavior varied from to 2.7% in Laos to 49.2% in Tuvalu. We found that each increment of soft drink consumption (time/day) was associated with an 11% (95%CI 10-13%) increase of the likelihood of frequent physical fighting. This result remained significant after adjusting for various covariates. In this large pooled sample of multinational data, there is a significant positive association between soft drink consumption and aggressive behavior among adolescents. Reducing soft drink consumption may help reduce aggressive behavior, a major risk factor for violence.

Statistical analysis for the impact of smoking on the behavior and health of Qatari adolescents.

The links between the use of tobacco and health risks are well known. Most of the younger smokers reside in Asia which includes Qatar, the focus country of this study. Cigarette smoking among children is rising at an alarming rate worldwide including Qatar. As youth make up a significant percentage of the population and to achieve the health objectives of the Qatar Vision 2030, it is essential to ensure the health and well-being of adolescents, as they are the future of Qatar. This study focuses on exploring the patterns of tobacco use and its impacts on the adolescents by conducting a survey in different schools across Qatar. The questionnaire was administered in five schools, selected by proportional random sampling. The responses were recorded from the sample for general questions regarding interest in physical activities, relationship with family and friends, mental satisfaction, health, academics and access to cigarettes.

Kynurenine 3-Monooxygenase Gene Associated With Nicotine Initiation and Addiction: Analysis of Novel Regulatory Features at 5' and 3'-Regions.

Tobacco smoking is widespread behavior in Qatar and worldwide and is considered one of the major preventable causes of ill health and death. Nicotine is part of tobacco smoke that causes numerous health risks and is incredibly addictive; it binds to the α7 nicotinic acetylcholine receptor (α7nAChR) in the brain. Recent studies showed α7nAChR involvement in the initiation and addiction of smoking. Kynurenic acid (KA), a significant tryptophan metabolite, is an antagonist of α7nAChR. Inhibition of kynurenine 3-monooxygenase enzyme encoded by KMO enhances the KA levels. Modulating KMO gene expression could be a useful tactic for the treatment of tobacco initiation and dependence. Since KMO regulation is still poorly understood, we aimed to investigate the 5' and 3'-regulatory factors of KMO gene to advance our knowledge to modulate KMO gene expression. In this study, bioinformatics methods were used to identify the regulatory sequences associated with expression of KMO. The displayed differential expression of KMO mRNA in the same tissue and different tissues suggested the specific usage of the KMO multiple alternative promoters. Eleven KMO alternative promoters identified at 5'-regulatory region contain TATA-Box, lack CpG Island (CGI) and showed dinucleotide base-stacking energy values specific to transcription factor binding sites (TFBSs). The structural features of regulatory sequences can influence the transcription process and cell type-specific expression. The uncharacterized LOC105373233 locus coding for non-coding RNA (ncRNA) located on the reverse strand in a convergent manner at the 3'-side of KMO locus. The two genes likely expressed by a promoter that lacks TATA-Box harbor CGI and two TFBSs linked to the bidirectional transcription, the NRF1, and ZNF14 motifs. We identified two types of microRNA (miR) in the uncharacterized LOC105373233 ncRNA, which are like hsa-miR-5096 and hsa-miR-1285-3p and can target the miR recognition element (MRE) in the KMO mRNA. Pairwise sequence alignment identified 52 nucleotides sequence hosting MRE in the KMO 3' UTR untranslated region complementary to the ncRNA LOC105373233 sequence. We speculate that the identified miRs can modulate the KMO expression and together with alternative promoters at the 5'-regulatory region of KMO might contribute to the development of novel diagnostic and therapeutic algorithm for tobacco smoking.

Genomic and Bioinformatics Approaches for Analysis of Genes Associated With Cancer Risks Following Exposure to Tobacco Smoking.

Cancer is a significant health problem in the Middle East and global population. It is well established that there is a direct link between tobacco smoking and cancer, which will continue to pose a significant threat to human health. The impact of long-term exposure to tobacco smoke on the risk of cancer encouraged the study of biomarkers for vulnerable individuals to tobacco smoking, especially children, who are more susceptible than adults to the action of environmental carcinogens. The carcinogens in tobacco smoke condensate induce DNA damage and play a significant role in determining the health and well-being of smokers, non-smoker, and primarily children. Cancer is a result of genomic and epigenomic malfunctions that lead to an initial premalignant condition. Although premalignancy genetic cascade is a much-delayed process, it will end with adverse health consequences. In addition to the DNA damage and mutations, tobacco smoke can cause changes in the DNA methylation and gene expression associated with cancer. The genetic events hint on the possible use of genomic-epigenomic changes in genes related to cancer, in predicting cancer risks associated with exposure to tobacco smoking. Bioinformatics provides indispensable tools to identify the cascade of expressed genes in active smokers and non-smokers and could assist the development of a framework to manage this cascade of events linked with the evolvement of disease including cancer. The aim of this mini review is to cognize the essential genomic processes and health risks associated with tobacco smoking and the implications of bioinformatics in cancer prediction, prevention, and intervention.

Outlier robust nonlinear mixed model estimation.

In standard analyses of data well-modeled by a nonlinear mixed model, an aberrant observation, either within a cluster, or an entire cluster itself, can greatly distort parameter estimates and subsequent standard errors. Consequently, inferences about the parameters are misleading. This paper proposes an outlier robust method based on linearization to estimate fixed effects parameters and variance components in the nonlinear mixed model. An example is given using the four-parameter logistic model and bioassay data, comparing the robust parameter estimates with the nonrobust estimates given by SAS(®).