Current and Potential Applications of Artificial Intelligence in Metabolic Bariatric Surgery

Artificial intelligence (AI) is an umbrella term, which refers to different methods that simulate the process of human learning. As is the case with medicine in general, the field of bariatric metabolic surgery has lately been overwhelmed by evidence relevant to the applications of AI in numerous aspects of its clinical practice, including prediction of complications, effectiveness for weight loss and remission of associated medical problems, improvement of quality of life, intraoperative features, and cost-effectiveness. Current studies are highly heterogeneous regarding their datasets, as well as their metrics and benchmarking, which has a direct impact on the quality of research. For the non-familiar clinician, AI should be deemed as a novel statistical tool, which, in contradistinction to traditional statistics, draws their source data from real-world databases and registries rather than idealized cohorts of patients and is capable of managing vast amounts of data. This way, AI is supposed to support decision-making rather than substitute critical thinking or surgical skill development. As with any novelty, the clinical usefulness of AI remains to be proven and validated against established methods

An Analytical Prediction Model of Time Diversity Performance for Earth-Space Fade Mitigation

Time diversity (TD) has recently attracted attention as a promising and cost-efficient solution for high-frequency broadcast satellite applications. The present work proposes a general prediction model for the application of TD by approximating the time dynamics of rain attenuation through the use of the joint lognormal distribution. The proposed method is tested against experimental data and its performance is investigated with respect to the basic parameters of a satellite link

Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders

Genetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci. However, the nature and mechanisms of these pleiotropic effects remain unclear. We performed analyses of 232,964 cases and 494,162 controls from genome-wide studies of anorexia nervosa, attention-deficit/hyper-activity disorder, autism spectrum disorder, bipolar disorder, major depression, obsessive-compulsive disorder, schizophrenia, and Tourette syndrome. Genetic correlation analyses revealed a meaningful structure within the eight disorders, identifying three groups of inter-related disorders. Meta-analysis across these eight disorders detected 109 loci associated with at least two psychiatric disorders, including 23 loci with pleiotropic effects on four or more disorders and 11 loci with antagonistic effects on multiple disorders. The pleiotropic loci are located within genes that show heightened expression in the brain throughout the lifespan, beginning prenatally in the second trimester, and play prominent roles in neurodevelopmental processes. These findings have important implications for psychiatric nosology, drug development, and risk prediction.Peer reviewe

Analysis of shared heritability in common disorders of the brain

ience, this issue p. eaap8757 Structured Abstract INTRODUCTION Brain disorders may exhibit shared symptoms and substantial epidemiological comorbidity, inciting debate about their etiologic overlap. However, detailed study of phenotypes with different ages of onset, severity, and presentation poses a considerable challenge. Recently developed heritability methods allow us to accurately measure correlation of genome-wide common variant risk between two phenotypes from pools of different individuals and assess how connected they, or at least their genetic risks, are on the genomic level. We used genome-wide association data for 265,218 patients and 784,643 control participants, as well as 17 phenotypes from a total of 1,191,588 individuals, to quantify the degree of overlap for genetic risk factors of 25 common brain disorders. RATIONALE Over the past century, the classification of brain disorders has evolved to reflect the medical and scientific communities' assessments of the presumed root causes of clinical phenomena such as behavioral change, loss of motor function, or alterations of consciousness. Directly observable phenomena (such as the presence of emboli, protein tangles, or unusual electrical activity patterns) generally define and separate neurological disorders from psychiatric disorders. Understanding the genetic underpinnings and categorical distinctions for brain disorders and related phenotypes may inform the search for their biological mechanisms. RESULTS Common variant risk for psychiatric disorders was shown to correlate significantly, especially among attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder (MDD), and schizophrenia. By contrast, neurological disorders appear more distinct from one another and from the psychiatric disorders, except for migraine, which was significantly correlated to ADHD, MDD, and Tourette syndrome. We demonstrate that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine. We also identify significant genetic sharing between disorders and early life cognitive measures (e.g., years of education and college attainment) in the general population, demonstrating positive correlation with several psychiatric disorders (e.g., anorexia nervosa and bipolar disorder) and negative correlation with several neurological phenotypes (e.g., Alzheimer's disease and ischemic stroke), even though the latter are considered to result from specific processes that occur later in life. Extensive simulations were also performed to inform how statistical power, diagnostic misclassification, and phenotypic heterogeneity influence genetic correlations. CONCLUSION The high degree of genetic correlation among many of the psychiatric disorders adds further evidence that their current clinical boundaries do not reflect distinct underlying pathogenic processes, at least on the genetic level. This suggests a deeply interconnected nature for psychiatric disorders, in contrast to neurological disorders, and underscores the need to refine psychiatric diagnostics. Genetically informed analyses may provide important "scaffolding" to support such restructuring of psychiatric nosology, which likely requires incorporating many levels of information. By contrast, we find limited evidence for widespread common genetic risk sharing among neurological disorders or across neurological and psychiatric disorders. We show that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures. Further study is needed to evaluate whether overlapping genetic contributions to psychiatric pathology may influence treatment choices. Ultimately, such developments may pave the way toward reduced heterogeneity and improved diagnosis and treatment of psychiatric disorders

Metabolomics in Bariatric and Metabolic Surgery Research and the Potential of Deep Learning in Bridging the Gap

During the past several years, there has been a shift in terminology from bariatric surgery alone to bariatric and metabolic surgery (BMS). More than a change in name, this signifies a paradigm shift that incorporates the metabolic effects of operations performed for weight loss and the amelioration of related medical problems. Metabolomics is a relatively novel concept in the field of bariatrics, with some consistent changes in metabolite concentrations before and after weight loss. However, the abundance of metabolites is not easy to handle. This is where artificial intelligence, and more specifically deep learning, would aid in revealing hidden relationships and would help the clinician in the decision-making process of patient selection in an individualized way

Artificial Intelligence and Machine Learning in the Diagnosis and Management of Gastroenteropancreatic Neuroendocrine Neoplasms—A Scoping Review

Neuroendocrine neoplasms (NENs) and tumors (NETs) are rare neoplasms that may affect any part of the gastrointestinal system. In this scoping review, we attempt to map existing evidence on the role of artificial intelligence, machine learning and deep learning in the diagnosis and management of NENs of the gastrointestinal system. After implementation of inclusion and exclusion criteria, we retrieved 44 studies with 53 outcome analyses. We then classified the papers according to the type of studied NET (26 Pan-NETs, 59.1%; 3 metastatic liver NETs (6.8%), 2 small intestinal NETs, 4.5%; colorectal, rectal, non-specified gastroenteropancreatic and non-specified gastrointestinal NETs had from 1 study each, 2.3%). The most frequently used AI algorithms were Supporting Vector Classification/Machine (14 analyses, 29.8%), Convolutional Neural Network and Random Forest (10 analyses each, 21.3%), Random Forest (9 analyses, 19.1%), Logistic Regression (8 analyses, 17.0%), and Decision Tree (6 analyses, 12.8%). There was high heterogeneity on the description of the prediction model, structure of datasets, and performance metrics, whereas the majority of studies did not report any external validation set. Future studies should aim at incorporating a uniform structure in accordance with existing guidelines for purposes of reproducibility and research quality, which are prerequisites for integration into clinical practice

Radio Wave Propagation and Channel Modeling for Earth–Space Systems. Chapter 8 - Impact of Clouds from Ka Band to Optical Frequencies

The accurate design of earth–space systems requires a comprehensive understanding of the various propagation media and phenomena that differ depending on frequencies and types of applications. The choice of the relevant channel models is crucial in the design process and constitutes a key step in performance evaluation and testing of earth–space systems. The subject of this book is built around the two characteristic cases of satellite systems: fixed satellites and mobile satellite systems. Radio Wave Propagation and Channel Modeling for Earth–Space Systems discusses the state of the art in channel modeling and characterization of next-generation fixed multiple-antennas and mobile satellite systems, as well as propagation phenomena and fade mitigation techniques. The frequencies of interest range from 100 MHz to 100 GHz (from VHF to W band), whereas the use of optical free-space communications is envisaged. Examining recent research advances in space-time tropospheric propagation fields and optical satellite communication channel models, the book covers land mobile multiple antennas satellite- issues and relative propagation campaigns and stratospheric channel models for various applications and frequencies. It also presents research and well-accepted satellite community results for land mobile satellite and tropospheric attenuation time-series single link and field synthesizers. The book examines aeronautical communications channel characteristics and modeling, relative radio wave propagation campaigns, and stratospheric channel model for various applications and frequencies. Propagation effects on satellite navigation systems and the corresponding models are also covered

Opportunistic screening for hypertension in the general population in Greece: International Society of Hypertension May Measurement Month 2019

Hypertension remains a major public health issue with inadequate control worldwide. The May Measurement Month (MMM) initiative by the International Society of Hypertension was implemented in Greece in 2019 aiming to raise hypertension awareness and control. Adult volunteers (>= 18years) were recruited through opportunistic screening in five urban areas. Information on medical history and triplicate sitting blood pressure (BP) measurements were obtained using validated automated upper-arm devices. Hypertension was defined as systolic BP >= 140mmHg and/or diastolic >= 90mmHg, and/or self-reported use of drugs for hypertension. A total of 5727 were analysed [mean age 52.7 (SD 16.6) years, men 46.5%, 88.3% had BP measurement in the last 18months]. The prevalence of hypertension was (41.6%) and was higher in men and in older individuals. Among individuals with hypertension, 78.7% were diagnosed, 73.1% treated, and 48.3% controlled. Awareness, treatment, and control of hypertension were higher in women and in older individuals. Hypertensives had a higher body mass index (BMI) and were more likely to have diabetes, myocardial infarction and stroke, and less likely to smoke than normotensives (all P<0.001). Among treated hypertensives, 65.1% were on monotherapy, and with increasing number of antihypertensive drugs the BP levels were higher and hypertension control rates lower. The prevalence of hypertension in Greece is high, with considerable potential for improving awareness, treatment, and control. Screening programmes, such as MMM, need to be widely implemented at the population level, together with training programmes for healthcare professionals aiming to optimise management and control

Genetic Analysis and Status of Brown Bear Sub-Populations in Three National Parks of Greece Functioning as Strongholds for the Species’ Conservation

In order to optimize the appropriate conservation actions for the brown bear (Ursus arctos L.) population in Greece, we estimated the census (Nc) and effective (Ne) population size as well as the genetic status of brown bear sub-populations in three National Parks (NP): Prespa (MBPNP), Pindos (PINDNP), and Rhodopi (RMNP). The Prespa and Pindos sub-populations are located in western Greece and the Rhodopi population is located in eastern Greece. We extracted DNA from 472 hair samples and amplified through PCR 10 microsatellite loci. In total, 257 of 472 samples (54.5%) were genotyped for 6–10 microsatellite loci. Genetic analysis revealed that the Ne was 35, 118, and 61 individuals in MBPNP, PINDNP, and RMNP, respectively, while high levels of inbreeding were found in Prespa and Rhodopi but not in Pindos. Moreover, analysis of genetic structure showed that the Pindos population is genetically distinct, whereas Prespa and Rhodopi show mutual overlaps. Finally, we found a notable gene flow from Prespa to Rhodopi (10.19%) and from Rhodopi to Prespa (14.96%). Therefore, targeted actions for the conservation of the bears that live in the abovementioned areas must be undertaken, in order to ensure the species’ viability and to preserve the corridors that allow connectivity between the bear sub-populations in Greece