Forecasting Causes of Death in Northern Iraq Using Neural Network

The availability of models for predicting future events is essential for enhancing the efficiency of systems. This paper attempts to predict demographic variation by employing multi-layer perceptron network. Here we present the implementation of a system for predicting the number and causes of deaths, for a future 2-year period. The system was built using predictive models and data that is as accurate as possible under the current conditions of the northern Region of Iraq (the Autonomous Region of Kurdistan). Our predictive model is based on quarterly periods, with the intention of providing predictions on the number of deaths, classified by gender, cause of death, age at death, administrative district (governorate), and hospital where the death occurred. The data was collected from birth and death registry bureaus and forensic medicine departments for the years 2009-2020. The python programming language was used to test the designed multi-layer perceptron network with backpropagation training algorithm. With learning rate 0.01 and 500 epochs we were able to obtain good results, as the neural network was able to represent the string, and predict future values well, with a mean squared error of 0.43, and we found that number of deaths is quite stable, with a slight increase

Advances in understanding autism spectrum disorder

Autism spectrum disorder (ASD) appears to have a dramatic increase over the last twenty years and according to the latest estimates, 1 out of 68 children has been diagnosed with this disorder. In this context, it is crucial to provide clinicians with the most updated information on the genetic, epigenetic, and environmental understanding of ASD, as well as to provide the best scientific evidence in order to build successful therapeutic strategies for the patients

Are caesarean sections, induced labor and oxytocin regulation linked to Autism Spectrum Disorders

The etiology of Autism Spectrum Disorders (ASDs) continues to be elusive. While ASDs have been shown to be heritable, several environmental co-factors, such as, e.g. pre- or perinatal adverse events, could play a role in the pathogenesis of the disorder as well. Prevalence of ASDs appears to have increased in the last three decades, but the causes of this surge are not fully understood. As perinatal adverse events have increased as well, they have been regarded as logical contributors to the risen prevalence of ASDs. Over the last three decades there has been also a considerable increase in the rates of induced labor and caesarean sections (CS). However, even if a causal association between CS and ASDs increase has been suggested, it has not yet been proven. Nevertheless, we hypothesize here that such an association is actual and that it might help to explain a part of the increase in ASD diagnoses. Our assumption is based on the wider epidemiological picture of ASDs and CS, as well as on the possible biological plausibility of this correlation, by postulating potential epigenetic and neurobiological mechanisms underpinning this relationship. Today, several observations point toward the existence of epigenetic dysregulation in ASDs and this raises the issue of the role of environmental factors in bringing about epigenetic modifications. Epigenetic dysregulations in some brain neuropeptide systems could play a role in the behavioral dysfunctions of ASDs. Particularly, some evidence suggests a dysregulation of the oxytocinergic system in autistic brains. Perinatal alterations of oxytocin (OT) can also have life-long lasting effects on the development of social behaviors. Within the perinatal period, various processes, like pitocin infusion or CS, can alter the OT balance in the newborn; OT dysregulation could then interact with genetic factors, leading ultimately to the development of ASDs. Large long-term prospective studies are needed to identify causal pathways for ASDs and examine whether and how (epi-)genetic susceptibility interacts with obstetric risk factors in the development of ASDs. A better understanding of such a potential interplay could become paradigmatic for a wide range of genetic-environmental interactions in ASDs

New perspectives in Autism spectrum disorder associated with tuberous sclerosis

Recent advances inmolecular genetics and preclinical studies of tuberous sclerosis complex (TSC) have helped to better understand the pathophysiology of TSC-related autism spectrum disorder (ASD). Furthermore, developmental studies have shown that infants with TSC begin to diverge from the neurotypical trajectories at the age of 6 months. Early abnormalities are often characterized by a delay in nonverbal cognitive skills, such as fine motor and visual reception domains followed by qualitative impairment of social communication. The expanding possibilities of an early diagnosis of TSC should increasingly allow the prompt identification of a population of infants at high risk for developing ASD. A presymptomatic diagnosis of TSC could facilitate not only the prospective investigation of developmental trajectories and early markers of ASD but also the evaluation of the efficacy of early interventions. Early identification of infants at high-risk for ASD, such as TSC infants, can allow designing individualized treatment strategies to address deficits in specific developmental domains associated with autism. The involvement of mammalian target of rapamycin (mTOR) in determining the behavioral phenotypes associated with TSC led to the hypothesis that mTOR inhibitors could also have a benefit on ASD symptoms. After the promising results from preclinical studies administrating rapamycin, clinical trials studying mTOR inhibitors are now undergoing