Rethinking stigma: Prejudicial beliefs impact psychiatric treatment in U.S. soldiers.

Two thirds of military personnel diagnosed with posttraumatic stress disorder (PTSD) do not engage in treatment. We examined the degree that prejudicial beliefs about people with PTSD negatively affected psychiatric medication acceptance. Public stigma is best defined as negative stereotypes regarding individuals being judged as inferior or weak for having PTSD.

Technical and clinical considerations for electroencephalography-based biomarkers for major depressive disorder.

Major depressive disorder (MDD) is a prevalent and debilitating psychiatric disease that leads to substantial loss of quality of life. There has been little progress in developing new MDD therapeutics due to a poor understanding of disease heterogeneity and individuals' responses to treatments. Electroencephalography (EEG) is poised to improve this, owing to the ease of large-scale data collection and the advancement of computational methods to address artifacts.

Machine learning hypothesis-generation for patient stratification and target discovery in rare disease: our experience with Open Science in ALS.

Introduction: Advances in machine learning (ML) methodologies, combined with multidisciplinary collaborations across biological and physical sciences, has the potential to propel drug discovery and development. Open Science fosters this collaboration by releasing datasets and methods into the public space; however, further education and widespread acceptance and adoption of Open Science approaches are necessary to tackle the plethora of known disease states.

Motivation: In addition to providing much needed insights into potential therapeutic protein targets, we also aim to demonstrate that small patient datasets have the potential to provide insights that usually require many samples (>5,000).

Veteran Cultural Competence Training: Initial Effectiveness and National-Level Implementation.

Objective: Because service professionals often lack cultural competence in working with veterans, veterans often perceive such professionals as "not understanding." The authors developed, evaluated, and implemented Veteran Cultural Competence Training (VCCT), combining educational and experiential components in an in-person training focused on building awareness, knowledge, and skills to better work with veterans.

Methods: Study 1 was a type 1 effectiveness-implementation hybrid trial examining VCCT effectiveness in a sample of social service professionals (N=41) compared with a matched comparison group (N=41) via the Multicultural Counseling Self-Efficacy Scale-Veteran Form (MCSE-V) instrument.

Respondents of health survey powered by the innovative NURO app exhibit correlations between exercise frequencies and diet habits, and between stress levels and sleep wellness.

Nurosene's NURO app (nurosene.com) is an innovative smartphone application that gathers and analyzes active self-report metrics from users, empowering them with data-driven health machine intelligence. We present the data collected and analyzed from the initial round of participants who responded to a 12-question survey on their life-style and health status.

Supporting the nation's transitioning veterans: Narrative review of practices and recommendations for psychotherapy and counseling of veterans separating from military service.

Each year, approximately 200,000 service members transition out of military service and return to civilian life. For many, the stresses of this military-to-civilian transition are vast and include instabilities in mental health, relationships, employment, education, and housing. Given their unique training, mental health professionals often find themselves on the front lines of efforts to support this population.

Dialectical behavior therapy for justice-involved veterans (DBT-J): Feasibility and acceptability.

Justice-involved veterans are a high-risk, high-need subgroup serviced by behavioral health services within the Veterans Health Administration (VHA) system. Justice-involved veterans often have complex mental health and substance use difficulties, a myriad of case management needs, and a range of criminogenic needs that are difficult to treat with traditional outpatient VHA services. The present study represents an initial evaluation of dialectical behavior therapy for justice-involved veterans (DBT-J), a novel psychotherapy program providing 16 weeks of skills-based group therapy and individualized case management services to veterans with current or recent involvement with the criminal justice system.

Prevalence of Personality Disorders Across Veteran Samples: A Meta-Analysis.

Despite priorities around mental health, Veteran health care organizations have historically considered personality disorders to be preexisting conditions ineligible for disability benefits. However, growing evidence suggests potentially elevated prevalence of these disorders among military and Veteran samples and attests to implications of risk. The current study provides a meta-analytic review of literature on the prevalence of personality disorders in Veteran samples.

Using Artificial Intelligence-based Methods to Address the Placebo Response in Clinical Trials.

The placebo response is a highly complex psychosocial-biological phenomenon that has challenged drug development for decades, particularly in neurological and psychiatric disease. While decades of research have aimed to understand clinical trial factors that contribute to the placebo response, a comprehensive solution to manage the placebo response in drug development has yet to emerge. Advanced data analytic techniques, such as artificial intelligence (AI), might be needed to take the next leap forward in mitigating the negative consequences of high placebo-response rates.

Evaluation of postmortem microarray data in bipolar disorder using traditional data comparison and artificial intelligence reveals novel gene targets.

Large-scale microarray studies on post-mortem brain tissues have been utilized to investigate the complex molecular pathology of bipolar disorder. However, a major challenge in characterizing the dysregulation of gene expression in patients with bipolar disorder includes the lack of convergence between different studies, limiting comprehensive understanding from individual results. In this study, we aimed to identify genes that are both validated in published literature and are important classification features of unsupervised machine learning analysis of Stanley Brain Bank microarray database, followed by augmented intelligence method to identify distinct patient molecular subgroups.

Shattering cancer with quantum machine learning: A preview.

Machine learning has become a standard tool for medical researchers attempting to model disease in various ways, including building models to predict response to medications, classifying disease subtypes, and discovering new therapies. In this preview, we review a paper that utilizes quantum computation in order to tackle a critical issue that exists with medical datasets: they are small, in that they contain few samples. The authors' work demonstrates the possibility that these quantum-based methods may provide an advantage for small datasets and thus have a real impact for medical researchers in the future.