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This review provides important information to help clinical anesthesiologists to recognize potential risks for pulmonary complications and allows strategies to create an appropriate perioperative plan for patients.Oral anticoagulants (OACs) are a class of medications commonly used in the long-term management of patients at risk of thrombosis. They include warfarin and direct oral anticoagulants (DOACs). The aging of the population and improvements in perioperative care have led to an increase in the number of patients on OACs and presenting for different types of elective and emergency surgery. Perioperative management of OACs constitutes a unique challenge. It is based on the quantification of a patient's individual hemorrhagic and thrombotic risk together with the intrinsic surgical bleeding risk. We reviewed current guidelines to define effective discontinuation of OACs, the need for bridging with different anticoagulants, and post-surgery OACs re-initiation. We also discussed the option for acute reversal of anticoagulation.With the increasing prevalence of obesity worldwide, it is inevitable that anesthesiologists will encounter patients with metabolic syndrome. Metabolic syndrome encompasses multiple diseases, which include central obesity, hypertension, dyslipidemia, and hyperglycemia. HG6-64-1 Given the involvement of multiple diseases, metabolic syndrome involves numerous complex pathophysiological processes that negatively impact several organ systems. Some of the organ systems that have been well-documented to be adversely affected include the cardiovascular, pulmonary, and endocrine systems. Metabolic syndrome also leads to prolonged hospital stays, increased rates of infections, a greater need for care after discharge, and overall increased healthcare costs. Several interventions have been suggested to mitigate these negative outcomes ranging from lifestyle modifications to surgeries. Therefore, anesthesiologists should understand metabolic syndrome and formulate management strategies that may modify perianesthetic and surgical risks.

To investigate 1) whether baseline non-flourishing mental health is associated with a higher probability of all-cause mortality over 18-year follow-up after controlling for many risk factors for premature mortality; and 2) what other factors, independent of mental health status, are associated with all-cause mortality after adjustment for known risk factors.

Data were derived from waves 1 and 9 (1994/1995; 2010/2011) of the Canadian National Population Health Survey. An analytic sample of 12,424 participants 18years and above was selected. Baseline information on flourishing and predictors of all-cause mortality was from wave 1 and mortality data was ascertained by the Canadian Vital Statistics-Death Database in wave 9. Mean time to all-cause mortality was estimated using Kaplan-Meir procedure. Cox proportional hazards models were used to assess the association of baseline non-flourishing mental health and potential predictors with time to all-cause mortality.

About one in five participants was classifinon-flourishing influences mortality.A reliable diagnosis of autism spectrum disorder (ASD) is difficult to make until after toddlerhood. Detection in an earlier age enables early intervention, which is typically more effective. Recent studies of the development of brain and behavior in infants and toddlers have provided important insights in the diagnosis of autism. This extensive review focuses on published studies of predicting the diagnosis of autism during infancy and toddlerhood younger than 3 years using behavioral and neuroimaging approaches. After screening a total of 782 papers, 17 neuroimaging and 43 behavioral studies were reviewed. The features for prediction consist of behavioral measures using screening tools, observational and experimental methods, brain volumetric measures, and neural functional activation and connectivity patterns. The classification approaches include logistic regression, linear discriminant function, decision trees, support vector machine, and deep learning based methods. Prediction performance has large variance across different studies. For behavioral studies, the sensitivity varies from 20% to 100%, and specificity ranges from 48% to 100%. The accuracy rates range from 61% to 94% in neuroimaging studies. Possible factors contributing to this inconsistency may be partially due to the heterogeneity of ASD, different targeted populations (i.e., high-risk group for ASD and general population), age when the features were collected, and validation procedures. The translation to clinical practice requires extensive further research including external validation with large sample size and optimized feature selection. The use of multi-modal features, e.g., combination of neuroimaging and behavior, is worth further investigation to improve the prediction accuracy.Highly complex endophenotypes and underlying molecular mechanisms have prevented effective diagnosis and treatment of autism spectrum disorder. link2 Despite extensive studies to identify relevant biosignatures, no biomarker and therapeutic targets are available in the current clinical practice. While our current knowledge is still largely incomplete, -omics technology and machine learning-based big data analysis have provided novel insights on the etiology of autism spectrum disorders, elucidating systemic impairments that can be translated into biomarker and therapy target candidates. However, more integrated and sophisticated approaches are vital to realize molecular stratification and individualized treatment strategy. Ultimately, systemic approaches based on -omics and big data analysis will significantly contribute to more effective biomarker and therapy development for autism spectrum disorder.Autism spectrum disorder (ASD) is a set of pervasive neurodevelopmental disorders. The causation is multigenic in most cases, which makes it difficult to model the condition in vitro. Advances in pluripotent stem cell technology has made it possible to generate in vitro models of human brain development. Induced pluripotent stem cells (iPSCs) can be generated from somatic cells and have the ability to differentiate to all of the body's cells. This chapter aims to give an overview of the iPSC technology for generating neural cells and cerebral organoids as models for neurodevelopment and how these models are utilized in the study of ASD. The combination of iPSC technology and the genetic modification tool CRISPR/Cas9 is described, and current limitations and future perspectives of iPSC technology is discussed.Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder thought to be caused by predisposing high-risk genes that may be altered during the early development by environmental factors. link3 The impact of maternal challenges during pregnancy on the prevalence of ASD has been widely studied in clinical and animal studies. Here, we review some clinical and pre-clinical evidence that links environmental factors (i.e., infection, air pollution, pesticides, valproic acid and folic acid) and the risk of ASD. Additionally, certain prenatal environmental challenges such as the valproate and folate prenatal exposures allow us to study mechanisms possibly linked to the etiology of ASD, for instance the epigenetic processes. These mechanistic pathways are also presented and discussed in this chapter.Autism spectrum disorder is a neurodevelopmental disorder characterized by impaired development and by abnormal function in regards to social interaction, communication and restricted, repetitive behavior. It affects approximately 1% of the worldwide population. Like other psychiatric disorders the diagnosis is based on observation of, and interview with the patient and next of kin, and diagnostic tests. Many genes have been associated with autism, but only few highly penetrant. Some researchers have instead focused on oxidative stress, metabolic abnormalities and mitochondrial dysfunction as an explanation of the disorder. Currently no cure exists for the disorder, making these abnormalities interesting as they are possibly correctable with supplements or treatment. These various processes cannot be seen independently as they are influencing and interacting with each other. Furthermore many of the metabolic changes seen in autism have also been shown in other psychiatric disorders such as attention deficit hyperactivity disorder, schizophrenia and bipolar disorder along with often comorbid disorders like epilepsy and intellectual disability. As such some of these abnormalities are not specific, however, could indicate a similar mechanism for the development of these disorders, with symptomatology and severity varying according to the location and the amount of damage done to proteins, cells and DNA. Clinical studies trying to treat these abnormalities, have widely been successful in correcting the metabolic abnormalities seen, but only some studies have also shown bettering of autistic symptoms. Hopefully with increased knowledge of the pathophysiology of the disorder, future preventive measures or treatment can be developed.Neuroglia are a large class of neural cells of ectodermal (astroglia, oligodendroglia, and peripheral glial cells) and mesodermal (microglia) origin. Neuroglial cells provide homeostatic support, protection, and defense to the nervous tissue. Pathological potential of neuroglia has been acknowledged since their discovery. Research of the recent decade has shown the key role of all classes of glial cells in autism spectrum disorders (ASD), although molecular mechanisms defining glial contribution to ASD are yet to be fully characterized. This narrative conceptualizes recent findings of the broader roles of glial cells, including their active participation in the control of cerebral environment and regulation of synaptic development and scaling, highlighting their putative involvement in the etiopathogenesis of ASD.The development of new approaches for the clinical management of autism spectrum disorder (ASD) can only be realized through a better understanding of the neurobiological changes associated with ASD. One strategy for gaining deeper insight into the neurobiological mechanisms associated with ASD is to identify converging pathogenic processes associated with human idiopathic clinicopathology that are conserved in translational models of ASD. In this chapter, we first present the early overgrowth theory of ASD. Second, we introduce valproic acid (VPA), one of the most robust and well-known environmental risk factors associated with ASD, and we summarize the rapidly growing body of animal research literature using VPA as an ASD translational model. Lastly, we will detail the mechanisms of action of VPA and its impact on functional neural systems, as well as discuss future research directions that could have a lasting impact on the field.Various genetic and environmental factors have been suggested to cause autism spectrum disorders (ASDs). A variety of animal models of ASDs have been developed and used to investigate the mechanisms underlying the pathogenesis of ASDs. These animal models have contributed to clarifying that abnormalities in neuronal morphology and neurotransmission are responsible for the onset of ASDs. In recent years, researchers have started to focus not only on neurons but also on glial cells, particularly microglia. This is because microglial malfunction is strongly associated with structural and functional abnormalities of neurons, as well as the inflammation that is commonly observed both in the brains of patients with ASDs and in animal models of ASDs. In this chapter, we first introduce a list of commonly available animal models of ASDs and describe the validity of each model from the viewpoint of behaviors and neuroanatomy. We next detail the malfunction of microglia that has been reported in animal models of ASDs and discuss the roles of microglia in ASD pathogenesis.