Gram-positive anaerobe <i>cocci </i>(GPAC) are underrepresented in the microbiome of filaggrin-deficient human skin

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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

Minimally-invasive Sampling of Interleukin-1α and Interleukin-1 Receptor Antagonist from the Skin: A Systematic Review of In vivo Studies in Humans

Interleukin-1α (IL-1α) and its receptor antagonist IL-1RA play a pivotal role in skin homeostasis and disease. Although the use of biopsies to sample these cytokines from human skin is widely employed in dermatological practice, knowledge about less invasive, in vivo sampling methods is scarce. The aim of this study was to provide an overview of such methods by systematically reviewing studies in Medline, EMBASE, Web of Science and Cochrane Library using combinations of the terms “IL-1α”, IL-1RA”, “skin”, “human”, including all possible synonyms. Quality was assessed using the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) checklist. The search, performed on 14 October 2016, revealed 10 different sampling methods, with varying degrees of invasiveness and wide application spectrum, including assessment of both normal and diseased skin, from several body sites. The possibility to sample quantifiable amounts of cytokines from human skin with no or minimal discomfort holds promise for linking clinical outcomes to molecular profiles of skin inflammation

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Stress detection with encoding physiological signals and convolutional neural network

Stress is a significant and growing phenomenon in the modern world that leads to numerous health problems. Robust and non-invasive method developments for early and accurate stress detection are crucial in enhancing people's quality of life. Previous researches show that using machine learning approaches on physiological signals is a reliable stress predictor by achieving significant results. However, it requires determining features by hand. Such a selection is a challenge in this context since stress determines nonspecific human responses. This work overcomes such limitations by considering STREDWES, an approach for Stress Detection from Wearable Sensors Data. STREDWES encodes signal fragments of physiological signals into images and classifies them by a Convolutional Neural Network (CNN). This study aims to study several encoding methods, including the Gramian Angular Summation/Difference Field method and Markov Transition Field, to evaluate the best way to encode signals into images in this domain. Such a study is performed on the NEURO dataset. Moreover, we investigate the usefulness of STREDWES in real scenarios by considering the SWELL dataset and a personalized approach. Finally, we compare the proposed approach with its competitors by considering the WESAD dataset. It outperforms the others

Serotonin Transporter Binding in Major Depressive Disorder: Impact of Serotonin System Anatomy

Serotonin transporter (5-HTT) binding deficits are reported in major depressive disorder (MDD). However, most studies have not considered serotonin system anatomy when parcellating brain regions of interest (ROIs). We now investigate 5-HTT binding in MDD in two novel ways: (1) use of a 5-HTT tract-based analysis examining binding along serotonergic axons; and (2) using the Copenhagen University Hospital Neurobiology Research Unit (NRU) 5-HT Atlas, based on brain-wide binding patterns of multiple serotonin receptor types. [11C]DASB 5-HTT PET scans were obtained in 59 unmedicated participants with MDD in a current depressive episode and 32 healthy volunteers (HVs). Binding potential (BPP) was quantified with empirical Bayesian estimation in graphical analysis (EBEGA). Within the [11C]DASB tract, MDD showed significantly lower BPP compared with HVs (p=0.02). The BPP diagnosis difference varied by tract location at a trend-level (p=0.08), with MDD binding deficit strongest most proximal to brainstem raphe nuclei. NRU 5-HT Atlas ROIs showed trend-level lower BPP in MDD relative to HVs (p=0.06) and BPP diagnosis difference that varied by region (p=0.001). BPP was lower in MDD in 4/10 regions (p-values&lt;0.05). Neither [11C]DASB tract or NRU 5-HT Atlas BPP correlated with depression severity, suicidal ideation or suicide attempt history. Future studies are needed to determine the causes of this deficit in 5-HTT binding being more pronounced in proximal axon segments and in only a subset of ROIs for the pathogenesis of MDD. Such regional specificity may have implications for targeting antidepressant treatment, and may extend to other serotonin-related disorders

Incidence and Prognosis of Ventilator-Associated Pneumonia in Critically Ill Patients with COVID-19: A Multicenter Study

The primary objective of this multicenter, observational, retrospective study was to assess the incidence rate of ventilator-associated pneumonia (VAP) in coronavirus disease 2019 (COVID-19) patients in intensive care units (ICU). The secondary objective was to assess predictors of 30-day case-fatality of VAP. From 15 February to 15 May 2020, 586 COVID-19 patients were admitted to the participating ICU. Of them, 171 developed VAP (29%) and were included in the study. The incidence rate of VAP was of 18 events per 1000 ventilator days (95% confidence intervals [CI] 16\u201321). Deep respiratory cultures were available and positive in 77/171 patients (45%). The most frequent organisms were Pseudomonas aeruginosa (27/77, 35%) and Staphylococcus aureus (18/77, 23%). The 30-day case-fatality of VAP was 46% (78/171). In multivariable analysis, septic shock at VAP onset (odds ratio [OR] 3.30, 95% CI 1.43\u20137.61, p = 0.005) and acute respiratory distress syndrome at VAP onset (OR 13.21, 95% CI 3.05\u201357.26, p < 0.001) were associated with fatality. In conclusion, VAP is frequent in critically ill COVID-19 patients. The related high fatality is likely the sum of the unfavorable prognostic impacts of the underlying viral and the superimposed bacterial diseases