Vascular disease (VD) is a medical condition that adversely affects the blood vessels. Peripheral Artery Disease (PAD), a significant form of VD, is a major global health concern. In developing countries such as Bangladesh, VDs often remain undiagnosed and untreated until reaching critical stages. Artificial Intelligence (AI) based methods have potential for early detection and improved treatment guidelines. This paper narratively reviews both the clinical and the AI aspects of VD, emphasizing PAD, by examining recent studies on clinical diagnostic methods, treatment strategies and clinical outcomes, highlighting AI-driven research, machine learning (ML) algorithms contributing to disease management. Existing research on VDs, addresses their epidemiology, diagnosis, treatment, prevalence and morbidity, and mortality. It shows the need for more context-sensitive treatment data, age-specific studies, better access to technology, precise treatment goals, and rigorous diagnostic methodologies. Several works have explored AI algorithms to analyze diverse data sources - such as electronic health records (EHR), radiology images, genetic data and pulse wave signals for PAD detection, achieving accuracy exceeding 94
Political power in political economy lacks standardized metrics. This study introduces a measurement method combining individual legislators' power with firms' political contributions. This corporate political power measure explains federal contracting success. Results show local politicians representing firms' operational areas provide greater contracting benefits than powerful national politicians. Federal representatives support local firms to boost constituent employment and re-election prospects. Additionally, firms strategically reallocate contributions toward more electable politicians when experiencing political power decline, demonstrating adaptive behavior in maintaining influence.
Schools offer an unequalled access point for youth mental health services, particularly for marginalized youth who experience higher rates of mental health issues and greater barriers to care. However, traditional school-based mental health care models may overlook the social determinants of health that are linked to poor youth mental health outcomes, lower academic functioning, and difficulties with mental health treatment engagement. This article introduces a novel school-centered mental health (SCMH) model that leverages the school as the service delivery hub while extending support into home and community contexts. Rooted in the Ecological Model of School-Based Mental Health Services (Atkins et al., 2017; Cappella et al., 2008), the SCMH model pairs a therapist and family coach to provide comprehensive, culturally responsive school-based mental health services. Key components of the model include (a) school-based therapy for youth; (b) family coaching for resource navigation and parent support; (c) collaboration with teachers and school staff to support youth in the classroom; (d) partnerships with community organizations to enhance service coordination and navigation; and (e) trainings for school staff, parents, and the greater community on mental health, trauma, and development. Preliminary data from 11 urban schools in the Midwest highlight the potential of the SCMH model to reduce mental health symptoms and social determinants of health needs. The article underscores the value of collaborations among family members, schools, and community systems to improve student well-being and offers practical recommendations for implementation and future research directions.
Psychedelic-assisted therapy (PAT) is a novel and promising form of treatment for posttraumatic stress disorder (PTSD). However, clinicians may experience challenges in interpreting the existing evidence for PAT to make informed decisions about treatment recommendations and selection with their patients. Prior commentators have noted the hype surrounding public discussions of PAT. This hype may stem, at least in part, from how some peer-reviewed articles have reported and contextualized their findings. Researchers and disseminators of novel treatments have a responsibility to clinicians and the public alike to accurately represent the current evidence for existing treatments when presenting their findings. This article identifies types of reporting errors in PAT research that foster inaccurate comparisons with existing PTSD therapies, and it provides specific examples of each error from the recent peer-reviewed literature. We offer recommendations for researchers to improve the accuracy and rigor of their reporting, and for clinicians and peer reviewers to engage more critically with the research to identify sources of potential hype.
Whereas many experts believe that fears about AI are wildly overblown, catastrophic predictions are commonplace. Concerns may be particularly prevalent in US creative industries following high-profile labor negotiations around AI-related issues. Although many experts believe AI is most likely to augment rather than eliminate jobs in these fields, this is not true (or equally true) for all kinds of creative work. At the same time, perceptions of its potential effects can vary dramatically, even among people in similar job roles, depending on how the future of AI is imagined in relation to their labor and economic interests. Drawing on interviews with 50 creative workers, we ask, how does the anticipation of - or actual experience with - AI exacerbate perceived differences in interests among occupationally heterogeneous creative workers? We find that these workers fall into three ideal types: the "Hardly Worried" view themselves as uniquely insulated from the detrimental consequences of AI, while the "Heartbroken" respondents were concerned that they are about to lose beloved career options. The third group, "Hired Guns," were often aware of the risks of AI, and how it could be used, but viewed the training or implementation of AI as an opportunity to make fast and easy money or as providing them with new career opportunities. As a result, we argue that efforts to organize these workers, or to rein in how AI can be used, may require addressing larger issues of discordant AI perceptions and financial inequity.
