Experiences With Food Insecurity and Risky Sex Among Low-Income People Living With HIV/AIDS in a Resource-Rich Setting

Background: Forty-nine million individuals are food insecure in the United States, where food insecurity and HIV/AIDS are prevalent among the urban poor. Food insecurity is associated with risky sexual behaviours among people living with HIV/AIDS (PLHIV). No qualitative studies, however, have investigated the mechanisms underlying this relationship either in a resource-rich setting or among populations that include men who have sex with men (MSM). Methods: Semi-structured in-depth interviews were conducted with 34 low-income PLHIV receiving food assistance in the San Francisco Bay Area. The interviews explored experiences with food insecurity and perceived associations with sexual risk behaviours. Interviews were conducted in English, audio-recorded and transcribed verbatim. Transcripts were coded and analyzed according to content analysis methods using an inductive-deductive approach. Results: Food insecurity was reported to be a strong contributor to risky sexual practices among MSM and female participants. Individuals described engaging in transactional sex for food or money to buy food, often during times of destitution. Participants also explained how food insecurity could lead to condomless sex despite knowledge of and desire to use safe sexual practices, largely because the need to obtain food in the short term was prioritized over the desire to use barrier protection. Conclusions: Our data extend previous research by demonstrating that food insecurity contributes to transactional and unprotected sex among urban poor individuals in a resource-rich setting, including among MSM. These findings underscore the importance of public health and social intervention efforts focused on structural inequalities

Biallelic variants in OGDH encoding oxoglutarate dehydrogenase lead to a neurodevelopmental disorder characterized by global developmental delay, movement disorder, and metabolic abnormalities

PURPOSE: This study aimed to establish the genetic cause of a novel autosomal recessive neurodevelopmental disorder characterized by global developmental delay, movement disorder, and metabolic abnormalities. METHODS: We performed a detailed clinical characterization of 4 unrelated individuals from consanguineous families with a neurodevelopmental disorder. We used exome sequencing or targeted-exome sequencing, cosegregation, in silico protein modeling, and functional analyses of variants in HEK293 cells and Drosophila melanogaster, as well as in proband-derived fibroblast cells. RESULTS: In the 4 individuals, we identified 3 novel homozygous variants in oxoglutarate dehydrogenase (OGDH) (NM_002541.3), which encodes a subunit of the tricarboxylic acid cycle enzyme α-ketoglutarate dehydrogenase. In silico homology modeling predicts that c.566C>T:p.(Pro189Leu) and c.890C>A:p.(Ser297Tyr) variants interfere with the structure and function of OGDH. Fibroblasts from individual 1 showed that the p.(Ser297Tyr) variant led to a higher degradation rate of the OGDH protein. OGDH protein with p.(Pro189Leu) or p.(Ser297Tyr) variants in HEK293 cells showed significantly lower levels than the wild-type protein. Furthermore, we showed that expression of Drosophila Ogdh (dOgdh) carrying variants homologous to p.(Pro189Leu) or p.(Ser297Tyr), failed to rescue developmental lethality caused by loss of dOgdh. SpliceAI, a variant splice predictor, predicted that the c.935G>A:p.(Arg312Lys)/p.(Phe264_Arg312del) variant impacts splicing, which was confirmed through a mini-gene assay in HEK293 cells. CONCLUSION: We established that biallelic variants in OGDH cause a neurodevelopmental disorder with metabolic and movement abnormalities

Associations Between Food Insecurity and Psychotropic Medication Use Among Women Living With HIV in the United States

AIMS: Psychotropic prescription rates continue to increase in the United States (USA). Few studies have investigated whether social-structural factors may play a role in psychotropic medication use independent of mental illness. Food insecurity is prevalent among people living with HIV in the USA and has been associated with poor mental health. We investigated whether food insecurity was associated with psychotropic medication use independent of the symptoms of depression and anxiety among women living with HIV in the USA. METHODS: We used cross-sectional data from the Women\u27s Interagency HIV Study (WIHS), a nationwide cohort study. Food security (FS) was the primary explanatory variable, measured using the Household Food Security Survey Module. First, we used multivariable linear regressions to test whether FS was associated with symptoms of depression (Center for Epidemiologic Studies Depression [CESD] score), generalised anxiety disorder (GAD-7 score) and mental health-related quality of life (MOS-HIV Mental Health Summary score; MHS). Next, we examined associations of FS with the use of any psychotropic medications, including antidepressants, sedatives and antipsychotics, using multivariable logistic regressions adjusting for age, race/ethnicity, income, education and alcohol and substance use. In separate models, we additionally adjusted for symptoms of depression (CESD score) and anxiety (GAD-7 score). RESULTS: Of the 905 women in the sample, two-thirds were African-American. Lower FS (i.e. worse food insecurity) was associated with greater symptoms of depression and anxiety in a dose-response relationship. For the psychotropic medication outcomes, marginal and low FS were associated with 2.06 (p \u3c 0.001; 95% confidence interval [CI] = 1.36-3.13) and 1.99 (p \u3c 0.01; 95% CI = 1.26-3.15) times higher odds of any psychotropic medication use, respectively, before adjusting for depression and anxiety. The association of very low FS with any psychotropic medication use was not statistically significant. A similar pattern was found for antidepressant and sedative use. After additionally adjusting for CESD and GAD-7 scores, marginal FS remained associated with 1.93 (p \u3c 0.05; 95% CI = 1.16-3.19) times higher odds of any psychotropic medication use. Very low FS, conversely, was significantly associated with lower odds of antidepressant use (adjusted odds ratio = 0.42; p \u3c 0.05; 95% CI = 0.19-0.96). CONCLUSIONS: Marginal FS was associated with higher odds of using psychotropic medications independent of depression and anxiety, while very low FS was associated with lower odds. These complex findings may indicate that people experiencing very low FS face barriers to accessing mental health services, while those experiencing marginal FS who do access services are more likely to be prescribed psychotropic medications for distress arising from social and structural factors

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

Biallelic variants in OGDH encoding oxoglutarate dehydrogenase lead to a neurodevelopmental disorder characterized by global developmental delay, movement disorder, and metabolic abnormalities

Purpose: This study aimed to establish the genetic cause of a novel autosomal recessive neurodevelopmental disorder characterized by global developmental delay, movement disorder, and metabolic abnormalities.Methods: We performed a detailed clinical characterization of 4 unrelated individuals from consanguineous families with a neurodevelopmental disorder. We used exome sequencing or targeted-exome sequencing, cosegregation, in silico protein modeling, and functional analyses of variants in HEK293 cells and Drosophila melanogaster, as well as in proband-derived fibroblast cells.Results: In the 4 individuals, we identified 3 novel homozygous variants in oxoglutarate dehydrogenase (OGDH) (NM_002541.3), which encodes a subunit of the tricarboxylic acid cycle enzyme alpha-ketoglutarate dehydrogenase. In silico homology modeling predicts that c.566C > T:p.(Pro189Leu) and c.890C > A:p.(Ser297Tyr) variants interfere with the structure and function of OGDH. Fibroblasts from individual 1 showed that the p.(Ser297Tyr) variant led to a higher degradation rate of the OGDH protein. OGDH protein with p.(Pro189Leu) or p.(Ser297Tyr) variants in HEK293 cells showed significantly lower levels than the wild-type protein. Furthermore, we showed that expression of Drosophila Ogdh (dOgdh) carrying variants homologous to p.(Pro189Leu) or p.(Ser297Tyr), failed to rescue developmental lethality caused by loss of dOgdh. SpliceAI, a variant splice predictor, predicted that the c.935G > A:p.(Arg312Lys)/p.(Phe264_Arg312del) variant impacts splicing, which was confirmed through a mini-gene assay in HEK293 cells.Conclusion: We established that biallelic variants in OGDH cause a neurodevelopmental disorder with metabolic and movement abnormalities.(c) 2022 The Authors. Published by Elsevier Inc. on behalf of American College of Medical Genetics and Genomics. This is an open access article under the CC BY licensePeer reviewe

GW170104: Observation of a 50-Solar-Mass Binary Black Hole Coalescence at Redshift 0.2

We describe the observation of GW170104, a gravitational-wave signal produced by the coalescence of a pair of stellar-mass black holes. The signal was measured on January 4, 2017 at 10: 11: 58.6 UTC by the twin advanced detectors of the Laser Interferometer Gravitational-Wave Observatory during their second observing run, with a network signal-to-noise ratio of 13 and a false alarm rate less than 1 in 70 000 years. The inferred component black hole masses are 31.2(-6.0)(+8.4)M-circle dot and 19.4(-5.9)(+5.3)M(circle dot) (at the 90% credible level). The black hole spins are best constrained through measurement of the effective inspiral spin parameter, a mass-weighted combination of the spin components perpendicular to the orbital plane, chi(eff) = -0.12(-0.30)(+0.21) . This result implies that spin configurations with both component spins positively aligned with the orbital angular momentum are disfavored. The source luminosity distance is 880(-390)(+450) Mpc corresponding to a redshift of z = 0.18(-0.07)(+0.08) . We constrain the magnitude of modifications to the gravitational-wave dispersion relation and perform null tests of general relativity. Assuming that gravitons are dispersed in vacuum like massive particles, we bound the graviton mass to m(g) <= 7.7 x 10(-23) eV/c(2). In all cases, we find that GW170104 is consistent with general relativity

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

Background Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. Methods We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. Results Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. Conclusions Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders.Peer reviewe

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