Structural Brain Correlates of Childhood Inhibited Temperament: An ENIGMA-Anxiety Mega-analysis

Temperament involves stable behavioral and emotional tendencies that differ between individuals, which can be first observed in infancy or early childhood and relate to behavior in many contexts and over many years.1 One of the most rigorously characterized temperament classifications relates to the tendency of individuals to avoid the unfamiliar and to withdraw from unfamiliar people, objects, and unexpected events. This temperament is referred to as behavioral inhibition or inhibited temperament (IT).2 IT is a moderately heritable trait1 that can be measured in multiple species.3 In humans, levels of IT can be quantified from the first year of life through direct behavioral observations or reports by caregivers or teachers. Similar approaches as well as self-report questionnaires on current and/or retrospective levels of IT1 can be used later in life

Multimodal Theranostic Nanoformulations Permit Magnetic Resonance Bioimaging of Antiretroviral Drug Particle Tissue-Cell Biodistribution

RATIONALE: Long-acting slow effective release antiretroviral therapy (LASER ART) was developed to improve patient regimen adherence, prevent new infections, and facilitate drug delivery to human immunodeficiency virus cell and tissue reservoirs. In an effort to facilitate LASER ART development, “multimodal imaging theranostic nanoprobes” were created. These allow combined bioimaging, drug pharmacokinetics and tissue biodistribution tests in animal models. METHODS: Europium (Eu3+)- doped cobalt ferrite (CF) dolutegravir (DTG)- loaded (EuCF-DTG) nanoparticles were synthesized then fully characterized based on their size, shape and stability. These were then used as platforms for nanoformulated drug biodistribution. RESULTS: Folic acid (FA) decoration of EuCF-DTG (FA-EuCF-DTG) nanoparticles facilitated macrophage targeting and sped drug entry across cell barriers. Macrophage uptake was higher for FA-EuCF-DTG than EuCF-DTG nanoparticles with relaxivities of r2 = 546 mM-1s-1 and r2 = 564 mM-1s-1 in saline, and r2 = 850 mM-1s-1 and r2 = 876 mM-1s-1 in cells, respectively. The values were ten or more times higher than what was observed for ultrasmall superparamagnetic iron oxide particles (r2 = 31.15 mM-1s-1 in saline) using identical iron concentrations. Drug particles were detected in macrophage Rab compartments by dual fluorescence labeling. Replicate particles elicited sustained antiretroviral responses. After parenteral injection of FA-EuCF-DTG and EuCF-DTG into rats and rhesus macaques, drug, iron and cobalt levels, measured by LC-MS/MS, magnetic resonance imaging, and ICP-MS were coordinate. CONCLUSION: We posit that these theranostic nanoprobes can assess LASER ART drug delivery and be used as part of a precision nanomedicine therapeutic strategy

Comparative Genomics of Gardnerella vaginalis Strains Reveals Substantial Differences in Metabolic and Virulence Potential

Gardnerella vaginalis is described as a common vaginal bacterial species whose presence correlates strongly with bacterial vaginosis (BV). Here we report the genome sequencing and comparative analyses of three strains of G. vaginalis. Strains 317 (ATCC 14019) and 594 (ATCC 14018) were isolated from the vaginal tracts of women with symptomatic BV, while Strain 409-05 was isolated from a healthy, asymptomatic individual with a Nugent score of 9.Substantial genomic rearrangement and heterogeneity were observed that appeared to have resulted from both mobile elements and substantial lateral gene transfer. These genomic differences translated to differences in metabolic potential. All strains are equipped with significant virulence potential, including genes encoding the previously described vaginolysin, pili for cytoadhesion, EPS biosynthetic genes for biofilm formation, and antimicrobial resistance systems, We also observed systems promoting multi-drug and lantibiotic extrusion. All G. vaginalis strains possess a large number of genes that may enhance their ability to compete with and exclude other vaginal colonists. These include up to six toxin-antitoxin systems and up to nine additional antitoxins lacking cognate toxins, several of which are clustered within each genome. All strains encode bacteriocidal toxins, including two lysozyme-like toxins produced uniquely by strain 409-05. Interestingly, the BV isolates encode numerous proteins not found in strain 409-05 that likely increase their pathogenic potential. These include enzymes enabling mucin degradation, a trait previously described to strongly correlate with BV, although commonly attributed to non-G. vaginalis species.Collectively, our results indicate that all three strains are able to thrive in vaginal environments, and therein the BV isolates are capable of occupying a niche that is unique from 409-05. Each strain has significant virulence potential, although genomic and metabolic differences, such as the ability to degrade mucin, indicate that the detection of G. vaginalis in the vaginal tract provides only partial information on the physiological potential of the organism

DenseNet and Support Vector Machine classifications of major depressive disorder using vertex-wise cortical features

Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD. However, previous attempts to demarcate MDD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies. In this study, we used globally representative data from the ENIGMA-MDD working group containing an extensive sample of people with MDD (N=2,772) and HC (N=4,240), which allows a comprehensive analysis with generalizable results. Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter. As we analyzed a multi-site sample, we additionally applied the ComBat harmonization tool to remove potential nuisance effects of site. We found that both classifiers exhibited close to chance performance (balanced accuracy DenseNet: 51%; SVM: 53%), when estimated on unseen sites. Slightly higher classification performance (balanced accuracy DenseNet: 58%; SVM: 55%) was found when the cross-validation folds contained subjects from all sites, indicating site effect. In conclusion, the integration of vertex-wise morphometric features and the use of the non-linear classifier did not lead to the differentiability between MDD and HC. Our results support the notion that MDD classification on this combination of features and classifiers is unfeasible

Structural brain correlates of childhood inhibited temperament: an ENIGMA-Anxiety Mega-analysis

NWORubicon 019.201SG.022Pathways through Adolescenc

End-Stage Renal Disease Among HIV-Infected Adults in North America

Background. Human immunodeficiency virus (HIV)-infected adults, particularly those of black race, are at high-risk for end-stage renal disease (ESRD), but contributing factors are evolving. We hypothesized that improvements in HIV treatment have led to declines in risk of ESRD, particularly among HIV-infected blacks

Cortical and subcortical brain structure in generalized anxiety disorder: findings from 28 research sites in the enigma-anxiety working group

The goal of this study was to compare brain structure between individuals with generalized anxiety disorder (GAD) and healthy controls. Previous studies have generated inconsistent findings, possibly due to small sample sizes, or clinical/analytic heterogeneity. To address these concerns, we combined data from 28 research sites worldwide through the ENIGMA-Anxiety Working Group, using a single, pre-registered mega-analysis. Structural magnetic resonance imaging data from children and adults (5–90 years) were processed using FreeSurfer. The main analysis included the regional and vertex-wise cortical thickness, cortical surface area, and subcortical volume as dependent variables, and GAD, age, age-squared, sex, and their interactions as independent variables. Nuisance variables included IQ, years of education, medication use, comorbidities, and global brain measures. The main analysis (1020 individuals with GAD and 2999 healthy controls) included random slopes per site and random intercepts per scanner. A secondary analysis (1112 individuals with GAD and 3282 healthy controls) included fixed slopes and random intercepts per scanner with the same variables. The main analysis showed no effect of GAD on brain structure, nor interactions involving GAD, age, or sex. The secondary analysis showed increased volume in the right ventral diencephalon in male individuals with GAD compared to male healthy controls, whereas female individuals with GAD did not differ from female healthy controls. This mega-analysis combining worldwide data showed that differences in brain structure related to GAD are small, possibly reflecting heterogeneity or those structural alterations are not a major component of its pathophysiology

Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects

Less Bone Loss With Maraviroc- Versus Tenofovir-Containing Antiretroviral Therapy in the AIDS Clinical Trials Group A5303 Study

Background. There is a need to prevent or minimize bone loss associated with antiretroviral treatment (ART) initiation. We compared maraviroc (MVC)- to tenofovir disoproxil fumarate (TDF)–containing ART