Personal Social Network Factors Associated with Overdose Prevention Training Participation

We investigated social network factors associated with participation in overdose prevention training among injection drug users (IDUs). From 2008 to 2010, 106 IDUs who had witnessed an overdose in the past year from two syringe exchange programs in Los Angeles provided data on overdose prevention training status (trained vs. untrained), social networks, history of overdose, and demographics. In multivariate logistic regression, naming at least one network member who had been trained in overdose prevention was significantly associated with being trained (Adjusted Odds Ratio 3.25, 95% Confidence Interval 1.09, 9.68). Using social network approaches may help increase training participation. Limitations are noted.     

EDITORIAL

There is a growing literature that seeks to evaluate the role of “recovery capital” in the resolution of substance use disorders. In this study, a structured instrument (the Assessment of Recovery Capital), along with an assessment of social networks among 176 former illicit drug users and drinkers, is measured in three locations in England. There were relationships between recovery capital and vocational activity (training or employment), physical health, psychological health, and overall quality of life, with larger social networks associated with enhanced personal and social recovery capital. The study demonstrates high levels of personal and social well-being—manifesting in engagement in community activities and strong social networks—among persons engaged in recovery support activities that was not linked to time in recovery.     

Factors Influencing the Use of Antitumoral Chemotherapy in the South East of England

Abstract

Influences on the use of chemotherapy for the treatment of cancer within the South East region of England for patients diagnosed with colorectal, lung, breast and prostate cancer were investigated. The variables investigated as possibly influencing the selection of chemotherapy were the sex of the patients, their age, the year of diagnosis, the cancer site, the cancer stage, the index of multiple deprivation (IMD) and the cancer network of residence. Logistic regression used to adjust the proportion receiving chemotherapy in relation to other variables considered showed significant differences in the proportion of patients receiving chemotherapy between different cancer sites and different networks. There was also a highly significant trend seen in use of chemotherapy over time; the adjusted proportion of patients receiving chemotherapy increasing from 10.6% in 1993 to 24.3% in 2002. Age, stage and cancer site seemed to have the most influence on the use of chemotherapy.     

A Longitudinal Investigation of the Predictability of the Three-Factor Model of the Important People Inventory

Objective: Because of psychometric limitations and varied adaptations of the Important People Inventory (IP; a measure of alcohol social support), Groh et al. (7) performed factor analyses and created a three-factor model (i.e., Support for Drinking from Network Members, Drinking Behaviors of Network Members, and General Social Support). This present study examined the ability of the three-factor model to predict alcohol use. Methods: This study consisted of 293 women and 604 men who were US residents of a network of self-run recovery homes known as Oxford House (OH). Logistic regression models were run. The first model examined which of the three IP factors was the best predictor of alcohol use over a 4-month period; next, models compared Drinking Behaviors of Network Members (the three-factor model) and Network Support for Drinking from Network Members (the original two-factor model) as predictors of 4-month alcohol use. Results: Of the three factors measuring general support, network drinking behaviors, and support for drinking, Drinking Behaviors of Network Members was the only significant predictor of alcohol use over a 4-month period. Additionally, this component was a better predictor of drinking than the Support for Drinking from Network Members summary score from the original model. Conclusions: Compared to the original model, this new three-factor model of the IP is shorter, has stronger internal reliability, and is a better predictor of alcohol use over time. It is strongly recommended that researchers continue to explore the utility of this new model.     

Social medicine,feminism and the politics of population: From transnational knowledge networks to national social movements in Brazil and Mexico

ABSTRACT

This article examines the role of national actors articulated with an explicitly counter-hegemonic transnational knowledge network (TKN) mobilising around social medicine in policy debates on population control and family planning. It focuses primarily on Brazil, using Mexico as a shadow case to highlight salient points of contrast. In doing so, it makes two contributions to larger debates about TKNs. First, it highlights the plural and contested nature of the knowledge production they enact, underscoring contestation around a global reproductive regime that consolidated around family planning. Second, it underscores how the position and relative influence of actors articulated with TKNs is shaped by political and institutional contexts at the national level, producing variable opportunities for the mobilisation of applied knowledge. Reflecting its advocates’ embeddedness in larger opposition movements to authoritarian states, social medicine had a greater influence on these debates in Brazil, where synergies with a resurgent feminist movement reinforced a shared insistence on comprehensive women’s healthcare and increased the salience of sterilisation abuse on the political agenda.     

Detection and characterization of MRI breast lesions using deep learning

Purpose

The purpose of this study was to assess the potential of a deep learning model to discriminate between benign and malignant breast lesions using magnetic resonance imaging (MRI) and characterize different histological subtypes of breast lesions.

Social network influences on smoking,drinking and drug use in secondary school: centrifugal and centripetal forces

We explore how school experiences and social networks structure young people’s substance use in different institutional contexts. The concepts of ‘selection’ and ‘influence’ are situated within the context of bounded agency, counter‐school cultures and Bourdieusian notions of capital. We employed individual and group interviews, network‐mapping, and observations at two contrasting English secondary schools. Both schools were characterised by extended social network structures that appeared to influence patterns of substance use, although the mechanisms via which this occurred varied according to school context. At Grange House school (suburban context) a minority of students from disadvantaged families were alienated by the attainment‐focused regime, marginalised by a strong peer‐led centrifugal force pushing them outwards, and substance use was an alternative source of bonding and identity for these students. In contrast, at North Street a centripetal force operated whereby the majority of students were pulled towards highly‐visible, normative markers of ‘safe’, ‘road culture’, such as cannabis use and gang‐involvement, as they attempted to fit in and survive in an inner‐city school environment. We conclude that health inequalities may be reproduced through these distinctive centrifugal and centripetal forces in different institutional contexts, and this should be the focus of quantitative examination in the UK and elsewhere.     

Artificial neural networks assessing adolescent idiopathic scoliosis: comparison with Lenke classification

Background contextVariability in classifying and selecting levels of fusion in adolescent idiopathic scoliosis (AIS) has been repeatedly documented. Several computer algorithms have been used to classify AIS based on the geometrical features, but none have attempted to analyze its treatment patterns.PurposeTo use self-organizing maps (SOM), a kind of artificial neural networks, to reliably classify AIS cases from a large database. To analyze surgeon's treatment pattern in selecting curve regions to fuse in AIS using Lenke classification and SOM.Study designThis is a technical concept article on the possibility and benefits of using neural networks to classify AIS and a retrospective analysis of AIS curve regions selected for fusion.Patient sampleA total of 1,776 patients surgically treated for AIS were prospectively enrolled in a multicentric database. Cobb angles were measured on AIS patient spine radiographies, and patients were classified according to Lenke classification.Outcome measuresFor each patient in the database, surgical approach and levels of fusion selected by the treating surgeon were recorded.MethodsA Kohonen SOM was generated using 1,776 surgically treated AIS cases. The quality of the SOM was tested using topological error. Percentages of prediction of fusion based on Lenke classification for each patient in the database and for each node in the SOM were calculated. Lenke curve types, treatment pattern, and kappa statistics for agreement between fusion realized and fusion recommended by Lenke classification were plotted on each node of the map.ResultsThe topographic error for the SOM generated was 0.02, which demonstrates high accuracy. The SOM differentiates clear clusters of curve type nodes on the map. The SOM also shows epicenters for main thoracic, double thoracic, and thoracolumbar/lumbar curve types and transition zones between clusters. When cases are taken individually, Lenke classification predicted curve regions fused by the surgeon in 46% of cases. When those cases are reorganized by the SOM into nodes, Lenke classification predicted the curve regions to fuse in 82% of the nodes. Agreement with Lenke classification principles was high in epicenters for curve types 1, 2, and 5, moderate in cluster for curve types 3, 4, and 6, and low in transition zones between curve types.ConclusionsAn AIS SOM with high accuracy was successfully generated. Lenke classification principles are followed in 46% of the cases but in 82% of the nodes on the SOM. The SOM highlights the tendency of surgeons to follow Lenke classification principles for similar curves on the SOM. Self-organizing map classification of AIS could be valuable to surgeons because it bypasses the limitations imposed by rigid classification such as cutoff values on Cobb angle to define curve types. It can extract similar cases from large databases to analyze and guide treatment.     

From the Cover: An ancient,conserved gene regulatory network led to the rise of oral venom systems

Oral venom systems evolved multiple times in numerous vertebrates enabling the exploitation of unique predatory niches. Yet how and when they evolved remains poorly understood. Up to now, most research on venom evolution has focused strictly on the toxins. However, using toxins present in modern day animals to trace the origin of the venom system is difficult, since they tend to evolve rapidly, show complex patterns of expression, and were incorporated into the venom arsenal relatively recently. Here we focus on gene regulatory networks associated with the production of toxins in snakes, rather than the toxins themselves. We found that overall venom gland gene expression was surprisingly well conserved when compared to salivary glands of other amniotes. We characterized the “metavenom network,” a network of ∼3,000 nonsecreted housekeeping genes that are strongly coexpressed with the toxins, and are primarily involved in protein folding and modification. Conserved across amniotes, this network was coopted for venom evolution by exaptation of existing members and the recruitment of new toxin genes. For instance, starting from this common molecular foundation, Heloderma lizards, shrews, and solenodon, evolved venoms in parallel by overexpression of kallikreins, which were common in ancestral saliva and induce vasodilation when injected, causing circulatory shock. Derived venoms, such as those of snakes, incorporated novel toxins, though still rely on hypotension for prey immobilization. These similarities suggest repeated cooption of shared molecular machinery for the evolution of oral venom in mammals and reptiles, blurring the line between truly venomous animals and their ancestors.

Venoms are proteinaceous mixtures that can be traced and quantified to distinct genomic loci, providing a level of genetic tractability that is rare in other traits (1–4). This advantage of venom systems provides insights into processes of molecular evolution that are otherwise difficult to obtain. For example, studies in cnidarians showed that gene duplication is an effective way to increase protein dosage in tissues where different ecological roles can give rise to different patterns of gene expression (2, 5). Studies of venom in snakes have allowed comparisons of the relative importance of sequence evolution vs. gene expression evolution, as well as how a lack of genetic constraint enables diversity in complex traits (6, 7).Despite the wealth of knowledge venoms have provided about general evolutionary processes, the common molecular basis for the evolution of venom systems themselves is unknown. Even in snakes, which have perhaps the best studied venom systems, very little is known about the molecular architecture of these systems at their origin (8, 9). Using toxin families present in modern snakes to understand evolution at its origin is difficult because toxins evolve rapidly, both in terms of sequence and gene expression (10, 11). Toxins experience varying degrees of selection and drift, complicating interpretations of evolutionary models (12), and estimation of gene family evolution is often inconsistent, varying with which part of the gene (exon or intron) is used to construct the phylogeny (13). Most importantly, present-day toxins became a part of the venom over time; this diminishes their utility in trying to understand events that lead to the rise of venom systems in the nonvenomous ancestors of snakes (14, 15).A gene coexpression network aims to identify genes that interact with one another based on common expression profiles (16). Groups of coexpressed genes that have similar expression patterns across samples are identified using hierarchical clustering and are placed in gene “modules” (17). Constructing a network and comparing expression profiles of modules across taxa can identify key drivers of phenotypic change, as well as aid in identifying initial genetic targets of natural selection (18, 19). Comparative analysis using gene coexpression networks allows us to distinguish between ancient genetic modules representing core cellular processes, evolving modules that give rise to lineage-specific differences, and highly flexible modules that have evolved differently in different taxa (20). Gene coexpression networks are also widely used to construct gene regulatory networks (GRNs) owing to their reliability in capturing biologically relevant interactions between genes, as well as their high power in reproducing known protein–protein interactions (21, 22).Here we focus on gene coexpression networks involved in the production of snake venom, rather than the venom toxins themselves. Using a coexpression network we characterized the genes associated with venom production, which we term the “metavenom network,” and determine its biological role. We traced the origin of this network to the common ancestor of amniotes, which suggests that the venom system originated from a conserved gene regulatory network. The conserved nature of the metavenom network across amniotes suggests that oral venom systems started with a common gene regulatory foundation, and underwent lineage-specific changes to give rise to diverse venom systems in snakes, lizards, and even mammals.