Compulsive sexual behavior disorder in 42 countries: Insights from the International Sex Survey and introduction of standardized assessment tools

Background and aims: Despite its inclusion in the 11th revision of the International Classification of Diseases, there is a virtual paucity of high-quality scientific evidence about compulsive sexual behavior disorder (CSBD), especially in underrepresented and underserved populations. Therefore, we comprehensively examined CSBD across 42 countries, genders, and sexual orientations, and validated the original (CSBD-19) and short (CSBD-7) versions of the Compulsive Sexual Behavior Disorder Scale to provide standardized, state-of-the-art screening tools for research and clinical practice. Method: Using data from the International Sex Survey (N = 82,243; Mage = 32.39 years, SD = 12.52), we evaluated the psychometric properties of the CSBD-19 and CSBD-7 and compared CSBD across 42 countries, three genders, eight sexual orientations, and individuals with low vs. high risk of experiencing CSBD. Results: A total of 4.8% of the participants were at high risk of experiencing CSBD. Country- and gender-based differences were observed, while no sexual-orientation-based differences were present in CSBD levels. Only 14% of individuals with CSBD have ever sought treatment for this disorder, with an additional 33% not having sought treatment because of various reasons. Both versions of the scale demonstrated excellent validity and reliability. Discussion and conclusions: This study contributes to a better understanding of CSBD in underrepresented and underserved populations and facilitates its identification in diverse populations by providing freely accessible ICD-11-based screening tools in 26 languages. The findings may also serve as a crucial building block to stimulate research into evidence-based, culturally sensitive prevention and intervention strategies for CSBD that are currently missing from the literature

Differences in responses of platelets to fluid shear stress in patients with peripheral artery disease (PAD) and coronary artery disease (CAD).

Information on differences in platelet function between patients with peripheral arterial disease (PAD) and patients with coronary artery disease (CAD) is limited. We sought to examine the differences in the platelets response to shear stress in patients with PAD compared to those with CAD. Men with symptomatic PAD (ankle brachial index [ABI] \u3c 0.9; n = 29) were compared with similarly aged men with CAD (post coronary artery bypass grafting; n = 40) but without PAD. All participants were on aspirin, and none were on clopidogrel. We measured changes in shear-induced platelet aggregation (SIPA) and shear-induced P-selectin expression (SIPE) under fluid shear rates of 5000 and 10,000 s(-1)which are typically found in arterioles and stenosed arteries, respectively. Aggregation was also induced by a combined stimulation of collagen, fluid shear stress, and adenosine diphosphate (ADP) or epinephrine using a platelet function analyzer (PFA-100) as well as optical aggregometry (arachidonic acid, collagen and epinephrine). Analyses of covariance adjusted for age, aspirin dose, and statin use were used to estimate differences between the groups. Values of SIPA at fluid shear rates of 5000 and 10,000 s(-1) were significantly higher in the PAD group, while there were no differences between the PAD and CAD groups in SIPE at both fluid shear rates. However, baseline shear-induced P-selectin expression was higher in patients with PAD than CAD (mean fluorescence intensity [MFI] = 2.93 +/- 1.37 vs.1.94 +/- 0.67; p = 0.01), while the percentage increases in SIPA and SIPE at fluid shear rates of 5000 and 10,000 s(-1) were significantly higher in patients with CAD when compared to PAD (p \u3c 0.001 for all comparisons). Although there were several similarities in platelet function between men with PAD and men with CAD, significant differences in platelet responses to shear stress were observed in men with PAD when compared to those with CAD. Although the mechanism for these observed differences are not clear, we hypothesize that in vivo platelet activation in PAD patients may contribute to the differences and will need to be further investigated

Mechanical Activation of a Multimeric Adhesive Protein Through Domain Conformational Change

The mechanical force-induced activation of the adhesive protein von Willebrand factor (VWF), which experiences high hydrodynamic forces, is essential in initiating platelet adhesion. The importance of the mechanical force-induced functional change is manifested in the multimeric VWF's crucial role in blood coagulation, when high fluid shear stress activates plasma VWF (PVWF) multimers to bind platelets. Here, we showed that a pathological level of high shear stress exposure of PVWF multimers results in domain conformational changes, and the subsequent shifts in the unfolding force allow us to use force as a marker to track the dynamic states of the multimeric VWF. We found that shear-activated PVWF multimers are more resistant to mechanical unfolding than nonsheared PVWF multimers, as indicated in the higher peak unfolding force. These results provide insight into the mechanism of shear-induced activation of PVWF multimers

A Redshift z=5.4 Lyman alpha Emitting Galaxy with Linear Morphology in the GRAPES/UDF Field

We have discovered an extended Lyman alpha plume associated with a compact source at redshift 5.4 in slitless spectroscopic data from the Grism ACS Program for Extragalactic Science (GRAPES) project. The spatial extent of the emission is about 6 x 1.5 kpc (1 x 0.25 arcsec). Combining our grism data and the broadband images from the Hubble UltraDeep Field (UDF) images, we find a Lyman alpha line flux of 2e-17 erg/cm2/s and surface brightness 7e-17 erg/cm2/s/arcsec2. The UDF images show diffuse continuum emission associated with the Lyman alpha plume (hereafter UDF 5225), with three embedded knots. The morphology of UDF 5225 is highly suggestive of a galaxy in assembly. It is moreover possible that the prominent Lyman alpha emission from this object is due to an active nucleus, and that we are seeing the simultaneous growth through accretion of a galaxy and its central black hole. Followup observations at higher spectral resolution could test this hypothesis.Comment: 14 pages, accepted for publication in the Astrophysical Journa

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies

Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner’s ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person’s own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships