Psychological comorbidities in autism spectrum disorders

Autism spectrum disorder (ASD) is characterized by impairment in behavior, communication, and social interaction. Thus, accurate identification, regular behavioral and other nonmedical interventions would improve the diagnosis, management, and treatment of this condition.In this chapter, we investigate the importance of diagnosing and identifying comorbid psychiatric disorders that occur with ASD as these conditions can often complicate treatment, and failure to recognize them can result in deficits that can persist into adolescence and adulthood. In addition, we explore the impact of comprehensive psychological intervention in ASD patients with comorbid psychiatric disorders with the ultimate goal of improving overall quality of life

Iron and Vitamin D levels among autism spectrum disorders children

The aim of this study was to investigate iron deficiency anemia and Vitamin D deficiency among autism children and to assess the importance of risk factors (determinants). Subjects and Methods: This was a case-control study conducted among children suffering from autism at the Hamad Medical Corporation in Qatar. A total of 308 cases and equal number of controls were enrolled. The Autism Diagnostic Observation Schedule-Generic was the instrument used for diagnosis of Autism. Results: The mean age (+/- standard deviation, in years) for autistic versus control children was 5.39 +/- 1.66 versus 5.62 +/- 1.81, respectively. The mean value of serum iron levels in autistic children was severely reduced and significantly lower than in control children (74.13 +/- 21.61 mu g/dL with a median 74 in autistic children 87.59 +/- 23.36 mu g/dL in controls) (P = 0.003). Similarly, the study revealed that Vitamin D deficiency was considerably more common among autistic children (18.79 +/- 8.35 ng/mL) as compared to healthy children (22.18 +/- 9.00 ng/mL) (P = 0.004). Finally, mean values ofhemoglobin, ferritin, magnesium; potassium, calcium; phosphorous; glucose, alkaline phosphate, hematocrit, white blood cell, and mean corpuscular volume were all statistically significantly higher in healthy control children as compared to autistic children (P < 0.001). Multivariate logistic regression analysis revealed that serum iron deficiency, serum calcium levels, serum Vitamin I) levels; ferritin, reduced physical activity; child order, body mass index percentiles, and parental consanguinity can all be considered strong predictors and major factors associated with autism spectrum disorders. Conclusion: This study suggests that deficiency of iron and Vitamin D as well as anemia were more common in autistic compared to control children.Objectif: L’objectif de cette étude était d’étudier l’anémie ferriprive et la carence en vitamine D parmi les enfants autistiques et d’évaluer l’importance des facteurs de risque (déterminants). Sujets et méthodes: il s’agissait d’une étude cas-témoins réalisée chez les enfants atteints d’autisme à la Hamad Medical Corporation au Qatar. Au total, 308 cas et un nombre égal de contrôles ont été inscrits. Le programme d’observation diagnostique de l’autisme générique (ADOS) était l’instrument utilisé pour diagnostiquer l’autisme. Résultats: L’âge moyen (± SD, en années) pour les enfants autistes versus témoins était de 5,39 ± 1,66 vs 5,62 ± 1,81. La valeur moyenne des taux de sérum dans les enfants autistes a été considérablement réduite et significativement plus faible que dans les enfants témoins (74,13 ± 21,61 ug/dL avec une médiane de 74 chez les enfants autistes 87,59 ± 23,36 ug/dL dans les témoins) (P = 0,003). De même, l’étude a révélé que la carence en vitamine D était considérablement plus fréquente chez les enfants autistes (18,79 ± 8,35 ng/mL) par rapport aux enfants en bonne santé (22,18 ± 9,00 ng/mL) (P = 0,004). Enfin, les valeurs moyennes de l’hémoglobine, de la ferritine, du magnésium; potassium calcium; phosphoreux; le glucose, le phosphate alcalin, l’hématocrite, le globule blanc (CMB) et le volume corpusculaire moyen [MCV] étaient statistiquement significativement plus élevés chez les enfants témoins sains que chez les enfants autistes (P < 0,001). L’analyse de régression logistique multivariée a révélé que la carence sérique en fer, les taux sériques de calcium, les taux sériques de vitamine D; ferritine, réduction de l’activité physique; l’ordre des enfants, les percentiles de l’IMC et la consanguinité parentale peuvent tous être considérés comme des prédicteurs forts et des facteurs majeurs associés aux troubles du spectre autistique. Conclusion: Cette étude suggère que la carence en fer et en vitamine D ainsi que l’anémie étaient plus fréquentes chez les enfants autistiques par rapport aux enfants témoins.Qatar Foundation Hamad Medical Corporatio

A review of cyberbullying legislation in Qatar : considerations for policy makers and educators

Cyberbullying is a worldwide problem affecting mental health, education, safety and general well-being for individuals across the globe. Despite the widespread availability of the Internet, research into prevalence rates of cyberbullying in Qatar is lacking and legislating for the crime has been slow to develop. Recently there have been some positive initiatives in the country such as a Cybercrime Prevention Law, the development of a National ICT Strategy, and a website detailing safe practice guidelines for Internet usage. However, the implementation and usage of these initiatives are still limited and there is a lack of awareness of cyberbullying in Qatar. As a result, the risk factors and consequences among school-aged children are unknown. The current paper presents an evaluation of the legislative and public policy solutions to cyberbullying available in Qatar, and outlines the critical challenges that could potentially face educators in shaping best practice guidelines for the future

Cloud Intrusion Detection System Based on SVM

The demand for better intrusion detection and prevention solutions has elevated due to the current global uptick in hacking and computer network attacks. The Intrusion Detection System (IDS) is essential for spotting network attacks and anomalies, which have increased in size and scope. A detection system has become an effective security method that monitors and investigates security in cloud computing. However, several existing methods have faced issues such as low classification accuracy, high false positive rates, and low true positive rates. To solve these problems, a detection system based on Support Vector Machine (SVM) is proposed in this paper. In this method, the SVM classifier is utilized for network data classification into normal and abnormal behaviors. The Cloud Intrusion Detection Dataset is used to test the effectiveness of the suggested system. The experimental results show which the suggested system can detect abnormal behaviors with high accuracy