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Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.
To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.
This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.
The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.
Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.
Infectious disease outbreaks on the scale of the current coronavirus disease 2019 (COVID-19) pandemic are a new phenomenon in many parts of the world. Many isolation unit designs with corresponding workflow dynamics and personal protective equipment postures have been proposed for each emerging disease at the health facility level, depending on the mode of transmission. However, personnel and resource management at the isolation units for a resilient response will vary by human resource capacity, reporting requirements, and practice setting. This study describes an approach to isolation unit management at a rural Uganda Hospital and shares lessons from the Uganda experience for isolation unit managers in low- and middle-income settings.
The coronavirus disease 2019 (COVID-19) pandemic has significantly increased depression rates, particularly in emerging adults. The aim of this study was to examine longitudinal changes in depression risk before and during COVID-19 in a cohort of emerging adults in the U.S. and to determine whether prior drinking or sleep habits could predict the severity of depressive symptoms during the pandemic.
Participants were 525 emerging adults from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA), a five-site community sample including moderate-to-heavy drinkers. Poisson mixed-effect models evaluated changes in the Center for Epidemiological Studies Depression Scale (CES-D-10) from before to during COVID-19, also testing for sex and age interactions. Additional analyses examined whether alcohol use frequency or sleep duration measured in the last pre-COVID assessment predicted pandemic-related increase in depressive symptoms.
The prevalence of risk for clinical depression tripled due to a substantial and sustained increase in depressive symptoms during COVID-19 relative to pre-COVID years. Effects were strongest for younger women. Frequent alcohol use and short sleep duration during the closest pre-COVID visit predicted a greater increase in COVID-19 depressive symptoms.
The sharp increase in depression risk among emerging adults heralds a public health crisis with alarming implications for their social and emotional functioning as this generation matures. In addition to the heightened risk for younger women, the role of alcohol use and sleep behavior should be tracked through preventive care aiming to mitigate this looming mental health crisis.
This chapter focuses on individual-level aspects of inclusion in entrepreneurial ecosystems, using examples from gender-focused ecosystems research in Boston, Massachusetts, as part of broader research carried out by the authors between 2014 and 2017. Using data from fieldwork carried out in Boston, we outline how individual-level gender biases operate in entrepreneurial ecosystems and how they impact women entrepreneurs differently than male entrepreneurs. Our focus is explicitly on the gendering of social capital and trust within entrepreneurial ecosystems, as we highlight their gendered dimensions which lead to exclusion for women, even if ‘unintentionally’.
The third chapter focuses explicitly on the relevance of gender for entrepreneurial ecosystems. The chapter first discusses the differences between sex, gender, and gender relations to lay the foundation for a dynamic understanding of actors and entrepreneurial ecosystems. Guided by feminist perspectives in entrepreneurship and sociology the chapter provides theoretical insights, derived from the various ways in which gender is studied, on how gender can be conceptualized and how inclusion is a multifaceted concept and practice. The chapter then offers a guiding definition of gender inclusion in relation to entrepreneurial ecosystems and moves on to provide insights about how to study it in relation to individuals, organizations, and sociocultural norms at the same time. In doing so, this chapter provides a multifactor and multilevel gender framework for understanding economic inclusion in relation to entrepreneurial ecosystems.
This chapter examines all three levels to present a holistic framework for understanding gender in relation to entrepreneurial ecosystems and policies for supporting inclusive economic development. It builds off the authors’ previous research in this area within the technology startup sector. The chapter provides effective approaches for building inclusive entrepreneurial ecosystems that range from individual approaches to organizational ones and, finally, to approaches by policymakers at local, state, and country levels. The chapter also outlines how gender should be an important dimension of policy efforts aimed at helping cities, states, and nations combat rising economic inequality in the midst of economic development efforts. More urgently, the impact of the ongoing pandemic is also examined given the gendered outcomes it will likely have on entrepreneurial success.
This chapter focuses on the city of Boston and delves into how intersectional differences among women entrepreneurs result in additional and different biasing forces for women of color and immigrant women entrepreneurs compared to White women engaging in entrepreneurship. As such, the chapter provides a holistic consideration of how gender, race, and other relations of difference may play out in experiences of entrepreneurship within entrepreneurial ecosystems. The chapter aims to provide a complex and holistic picture of how entrepreneurial ecosystems essentially provide very different experiences, interactions, and institutional support for actors in ecosystems, thereby supporting our argument that actors, even if in the same category, are indeed heterogeneous and not homogeneous.
The first chapter provides an overview of the rise and popularity of entrepreneurship as a practice and as a scholarly field of research. It notes how entrepreneurship has been shown to contribute positively to economic development and that scholarship related to supporting entrepreneurs through building robust entrepreneurial ecosystems is on the rise. Entrepreneurial ecosystems can be defined as a community of entrepreneurs engaged in reciprocal social and economic exchanges in the context of intermediary organizations, other actors, and institutions. Such research has focused on theory refinement as well as the development of metrics and ‘playbooks’ for communities that want to foster entrepreneurship. While policymakers are increasingly support building successful entrepreneurial ecosystems in their cities and states through public funding, there continues to be a dearth of research that addresses the relevance of gender for understanding and supporting entrepreneurial ecosystems. This chapter emphasizes the relevance and importance of a gender perspective for understanding how and why entrepreneurial ecosystems may not benefit female entrepreneurs in the same ways that they benefit male entrepreneurs. It provides insights into the ways a gender perspective can contribute to a new conceptual model of entrepreneurial ecosystems and eventually lead to effective policies for inclusive economic development.
This chapter shares examples of organization-level barriers to full participation of women in entrepreneurial ecosystems by way of the three cities that were the sites of our fieldwork—Boston, Massachusetts, St. Louis, Missouri, and Asheville, North Carolina. Here, the focus is on the ways in which intermediary organizations, such as incubators, accelerators, coworking spaces, and investors among others, can act as gatekeepers to the resources of the ecosystem. The chapter focuses specifically on access to networks, outreach, selection, support mechanisms (i.e., entrepreneur support organizations) available in the ecosystem, and ecosystem culture. In speaking to these issues, the chapter focuses on the role of meso-level organizational actors and how their norms, values, and practices differentially impact entrepreneurs and lead to inclusion or exclusion from the ecosystem.
The second chapter concentrates on the concept of entrepreneurial ecosystems, providing various ways to conceptualize and define it and then moving on to discuss its importance for supporting economic development. Given the growing body of work on entrepreneurial ecosystems, the chapter first outlines how the field of entrepreneurial ecosystems evolved from existing work on clusters and carries much of its assumptions around homogeneity of actors. In contrast to these assumptions, we demonstrate that actors are not homogenous but heterogeneous and that existing concepts of entrepreneurial ecosystems do not differentiate among entrepreneurs as actors within ecosystems. These arguments are further elaborated on with evidence in the chapters that follow.
This chapter outlines the importance and role of institutional factors in the analysis of entrepreneurial ecosystems, particularly in relation to gender. We focus explicitly on informal and formal factors—that is, belief systems as well as economic, political, and legal systems—as important considerations in how entrepreneurial ecosystems are organized and replicated. We conclude this chapter by introducing the concept of ‘ecosystem identity’ as a framework that offers a typology of ecosystems and thereby expands how scholarship attending to entrepreneurial ecosystems can conceptualize and categorize different types of ecosystem. Our goal here is to offer suggestions as to how the institutional organization and identity of an ecosystem can offer different mechanisms and drivers of change towards gender inclusion. We point out that ecosystem identity impacts the possibilities for change, and, on this basis, we offer insights as to challenges as well as opportunities for institutional shifts.
Based on extensive fieldwork, this book demonstrates how gender is an organizing principle of entrepreneurial ecosystems and makes a difference in how ecosystem resources are assembled and how they can be accessed. By bringing visibility to how ecosystem actors are heterogeneous across identities, interactions and experiences, the book highlights the role and complexity of individual, organizational, and institutional factors working in concert to create and maintain gendered inequities. Entrepreneurial Ecosystems provides research-driven insights around effective organizational practices and policies aimed at remedying gendered and intersectional inequalities associated with entrepreneurship activities and economic growth. Proposing a typology of four ecosystem identities, it highlights how some might be more amenable and organized towards gender inclusion and change, while others may be much more difficult to change, reorganize and restructure. It offers scholars, students, practitioners and policymakers insights about gender in relation to analyzing entrepreneurial ecosystems and for fostering inclusive economic development policies.
Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools.
To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics.
Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts.
Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = −0.34 years, s.e. = 0.08), major depression (β = −0.34 years, s.e. = 0.08), schizophrenia (β = −0.39 years, s.e. = 0.08), and educational attainment (β = −0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO.
AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.
The coronavirus disease 2019 (COVID-19) pandemic has resulted in shortages of personal protective equipment (PPE), underscoring the urgent need for simple, efficient, and inexpensive methods to decontaminate masks and respirators exposed to severe acute respiratory coronavirus virus 2 (SARS-CoV-2). We hypothesized that methylene blue (MB) photochemical treatment, which has various clinical applications, could decontaminate PPE contaminated with coronavirus.
The 2 arms of the study included (1) PPE inoculation with coronaviruses followed by MB with light (MBL) decontamination treatment and (2) PPE treatment with MBL for 5 cycles of decontamination to determine maintenance of PPE performance.
MBL treatment was used to inactivate coronaviruses on 3 N95 filtering facepiece respirator (FFR) and 2 medical mask models. We inoculated FFR and medical mask materials with 3 coronaviruses, including SARS-CoV-2, and we treated them with 10 µM MB and exposed them to 50,000 lux of white light or 12,500 lux of red light for 30 minutes. In parallel, integrity was assessed after 5 cycles of decontamination using multiple US and international test methods, and the process was compared with the FDA-authorized vaporized hydrogen peroxide plus ozone (VHP+O3) decontamination method.
Overall, MBL robustly and consistently inactivated all 3 coronaviruses with 99.8% to >99.9% virus inactivation across all FFRs and medical masks tested. FFR and medical mask integrity was maintained after 5 cycles of MBL treatment, whereas 1 FFR model failed after 5 cycles of VHP+O3.
MBL treatment decontaminated respirators and masks by inactivating 3 tested coronaviruses without compromising integrity through 5 cycles of decontamination. MBL decontamination is effective, is low cost, and does not require specialized equipment, making it applicable in low- to high-resource settings.