Family Research Institute

AI agents promise to empower biomedical discovery, but realizing this promise requires the ability to complete transparent, long-horizon analyses using tools. Agents must make intermediate decisions explicit, and validate each decision and output against data and tool constraints as the analysis unfolds. We present Medea, an AI agent that takes an omics objective and executes a transparent multi-step analysis using tools. Medea comprises four modules: research planning with context and integrity verification, code execution with pre- and post-run checks, literature reasoning with evidence-strength assessment, and a consensus stage that reconciles evidence across datasets, tools, and literature. Medea uses 20 tools spanning single-cell and bulk transcriptomic datasets, cancer vulnerability maps, pathway knowledge bases, and machine learning models. We evaluate Medea across 5,679 analyses in three open-ended domains: target identification across five diseases and cell type contexts (2,400 analyses), synthetic lethality reasoning in seven cell lines (2,385 analyses), and immunotherapy response prediction in bladder cancer (894 patient analyses). In evaluations that vary large language models, tool sets, omics objectives, and agentic modules, Medea improves the performance of existing approaches by up to 46% for target identification, 22% for synthetic lethality, and 24% for immunotherapy response prediction, while maintaining low failure rates and calibrated abstention. Medea shows that verification-aware AI agents improve performance by producing transparent analyses, not simply more efficient workflows.

The accelerating convergence of geopolitical instability and rapid digital transformation poses unprecedented challenges for national resilience, particularly in countries undergoing societal transition. Motivated by Ukraine’s dual struggle to defend against Russian aggression while advancing digital infrastructure, this paper examines how civil-military resource trade-offs affect the ability to maintain essential services and defend against cyber threats during a crisis. We identify institutional, financial, technological, and social barriers to cyber resilience, highlighting how resource allocation can enhance or undermine adaptive capacity. Building on Conservation of Resources (COR) theory, we propose a strategic framework for resource allocation that prioritizes the prevention of cascading loss across military and civilian systems. By conceptualizing resources as interdependent assets, we show how poorly managed trade-offs can trigger long-term vulnerability, while synergistic investments can generate resilience “gain spirals.” We argue that national resilience requires integrated governance, anticipatory planning, and whole-of-society engagement to avoid destabilizing feedback loops. Our framework offers actionable guidance for governments navigating the complex terrain of digital resilience, security policy, and economic development in an increasingly volatile world.

Nursing professional development practitioners (NPDPs) are evolving as simulationists, integrating immersive learning, mixed reality, and gamification to meet modern learners' needs. Within a large academic healthcare system, NPDPs lead interdisciplinary simulation initiatives that improve competency and advance quality improvements. Leveraging their unique clinical and educational expertise, NPDPs collaborate across professions, driving innovation and best practices in simulation-based education and supporting organizational goals for workforce readiness and patient safety.

Raynaud's phenomenon (RP) is a vascular disease characterized by exaggerated vasoconstriction in response to stressors, mainly cold and emotional stress. This vasoconstriction is mediated solely by alpha 2C-adrenoceptors (α2C-AR) expressed in vascular smooth muscle cells of dermal arterioles. Several factors, among which is cigarette smoking, are associated with aggravated symptoms of and increased risk for RP. Evidence shows that cigarette smoking induces the production of reactive oxygen species (ROS), which is a major driver of RP pathogenesis. However, the exact mechanism by which smoking contributes to RP or α2C-AR remains unclear. Here, we show that cigarette smoke extract (CSE) upregulates the expression of α2C-AR in a concentration- and time-dependent manner in VSMCs extracted from human dermal arterioles. This increase is associated with the activation of p38 MAPK, as pretreatment with SB-202190, a p38 specific inhibitor, attenuated CSE-induced α2C-AR expression. Furthermore, our results show that CSE induces ROS production followed by increased RhoA activation. We also show that CSE induces translocation of vascular α2C-AR to the plasma membrane, and that this mobilization is attenuated by inhibiting ROS via N-acetylcysteine or apocynin. Similarly, inhibition of Rho kinase via H- 11522 abolished CSE-induced α2C-AR translocation. Collectively, these results indicate that CSE activates two different signaling pathways to induce the expression and the translocation of α2C-AR. While CSE activates a p38-dependent mechanism to increase α2C-AR expression, it initiates the receptor's spatial and functional rescue via a ROS/RhoA signaling pathway. These results provide mechanistic insight into the effect of cigarette smoking on RP, and further reinforce that smoking avoidance/cessation is critical to manage this disease, especially in the absence of a definitive drug for RP.

The wide array of symptoms of and high comorbidity rates between unipolar depressive and anxiety-related disorders have raised questions about the relations among their symptoms. Factor analysis examines these relations, yet factor-analytic symptom-measurement models have relied on a single method of measurement and do not always replicate. We conceptually replicate and extend the trilevel model of anxiety and depression symptoms to encompass interviewer-rated symptoms. In the trilevel model, symptom-specific items load on three levels of factors: a narrow (or disorder-specific) factor, an intermediate-breadth factor, and a general-distress factor. The trilevel model fit well in this sample and fit better than comparison models that eliminated one of the three levels. Extension analysis successfully integrated interviewer-rating variables into the trilevel model—particularly for the broad and narrow levels. These results provide some support for the trilevel model and the use of interviewer ratings of symptoms. Research and treatment implications are discussed.

Recent studies have demonstrated that coronary plaque burden carries greater prognostic value in predicting adverse atherosclerotic cardiovascular disease outcomes than myocardial ischemia, thereby challenging the existing paradigm. Advances in plaque quantification through both noncontrast and contrast-enhanced computed tomography (CT) methods have led to earlier and more cost-effective detection of coronary disease compared with traditional stress testing. The 2 principal techniques of noninvasive coronary plaque quantification assessment are coronary artery calcium scoring by noncontrast CT and coronary CT angiography, both of which correlate with disease burden on invasive angiography. Plaque quantification from these imaging modalities has shown utility in risk stratification and prognostication of adverse cardiovascular events, leading to increased incorporation into clinical practice guidelines and preventive care pathways. Furthermore, due to their expanding clinical value, emerging technologies such as artificial intelligence are being integrated into plaque quantification platforms, placing more advanced measures of plaque burden at the forefront of coronary plaque evaluation. In this review, we summarize recent clinical data on coronary artery calcium scoring and coronary CT angiography plaque quantification in the evaluation of adverse atherosclerotic cardiovascular disease in patients with and without chest pain, highlight how these methods compare to invasive quantification approaches, and directly compare the performance characteristics of coronary artery calcium scoring and coronary CT angiography.

Scoping review methodology is an accessible knowledge synthesis research method for counseling researchers that has the potential to significantly impact the counseling research ecosystem. Scoping reviews can be mobilized to analyze a body of academic literature in order to map concepts, develop critical research questions, and focus research efforts. Learning how to conduct scoping reviews can facilitate counseling researchers’ emergent scholarly interests and agendas. This research guideline aims to introduce scoping review methodology to the counseling profession, present key methodological steps and considerations for counseling researchers, and share lessons learned from previous team-based scoping review studies.

Improving transitions in care is a major focus of health care planning. In the research team's prior intervention study, the length of stay (LOS) was reduced when patients at high risk for readmission were identified early in their acute care stay and received complex management. This study will describe the characteristics of patients receiving complex case management in an urban acute care hospital. Acute care hospital. This was a retrospective chart review of patients in a previous quality assurance study. A random selection of patients who previously underwent high-risk screening using the LACE (Length of stay; Acuity of the admission; Comorbidity of the patient; Emergency department use) index and received complex case management (the intervention group) were reviewed. The charts of a random selection of patients from the previous comparison group were also reviewed. Patient characteristics were collected and compared using descriptive statistics. In the intervention group, more patients had their family physicians (FPs) documented (93.1% [81/87] vs. 89.2% [66/74]). More patients in the intervention group (89.7% [77/87] vs. 85.1% [63/74]) lived at home prior to admission. More patients in the intervention group had a family caregiver involved (44.8% [39/87] vs. 41.9% [31/74]). At discharge, more patients in the intervention group (87.1% [74/85]) were discharged home compared with the comparison group (78.4% [58/74]). (1) Having an identified FP, living at home, and having family caregiver(s) characterized those with lower LOS and discharged home. (2) Case management, risk screening, and discharge planning improve patient outcomes. (3) This study identified the importance of having a FP and engaged family caregivers in improving care outcomes.