Explore the vast range of titles available.

Mobile phone sensors can be used to develop context-aware systems that automatically detect when patients require assistance. Mobile phones can also provide ecological momentary interventions that deliver tailored assistance during problematic situations. However, such approaches have not yet been used to treat major depressive disorder. The purpose of this study was to investigate the technical feasibility, functional reliability, and patient satisfaction with Mobilyze!, a mobile phone- and Internet-based intervention including ecological momentary intervention and context sensing. We developed a mobile phone application and supporting architecture, in which machine learning models (ie, learners) predicted patients' mood, emotions, cognitive/motivational states, activities, environmental context, and social context based on at least 38 concurrent phone sensor values (eg, global positioning system, ambient light, recent calls). The website included feedback graphs illustrating correlations between patients' self-reported states, as well as didactics and tools teaching patients behavioral activation concepts. Brief telephone calls and emails with a clinician were used to promote adherence. We enrolled 8 adults with major depressive disorder in a single-arm pilot study to receive Mobilyze! and complete clinical assessments for 8 weeks. Promising accuracy rates (60% to 91%) were achieved by learners predicting categorical contextual states (eg, location). For states rated on scales (eg, mood), predictive capability was poor. Participants were satisfied with the phone application and improved significantly on self-reported depressive symptoms (beta(week) = -.82, P < .001, per-protocol Cohen d = 3.43) and interview measures of depressive symptoms (beta(week) = -.81, P < .001, per-protocol Cohen d = 3.55). Participants also became less likely to meet criteria for major depressive disorder diagnosis (b(week) = -.65, P = .03, per-protocol remission rate = 85.71%). Comorbid anxiety symptoms also decreased (beta(week) = -.71, P < .001, per-protocol Cohen d = 2.58). Mobilyze! is a scalable, feasible intervention with preliminary evidence of efficacy. To our knowledge, it is the first ecological momentary intervention for unipolar depression, as well as one of the first attempts to use context sensing to identify mental health-related states. Several lessons learned regarding technical functionality, data mining, and software development process are discussed. Clinicaltrials.gov NCT01107041; http://clinicaltrials.gov/ct2/show/NCT01107041 (Archived by WebCite at http://www.webcitation.org/60CVjPH0n).

Current strategies to prevent restenosis following endovascular treatment include the local delivery of anti-proliferative agents to inhibit vascular smooth muscle cell (VSMC) proliferation and migration. These agents, not specific to VSMCs, are deposited on the luminal surface and therefore target endothelial cells and delay vascular healing. Cell-targeted therapies, (e.g., RNA aptamers), can potentially overcome these safety concerns by specifically binding to VSMC and inhibiting proliferation and migration. The purpose of this study was to therefore demonstrate the ability of a perfusion catheter to deliver cell-specific RNA aptamer inhibitors directly to the vessel wall. RNA aptamers specific to VSMCs were developed using an in vitro cell-based systematic evolution of ligand by exponential enrichment selection process. Two aptamers (Apt01 and Apt14) were evaluated ex vivo using harvested pig arteries in a pulsatile flow bioreactor. Local drug delivery of the aptamers into the medial wall was accomplished using a novel perfusion catheter. We demonstrated the feasibility to deliver aptamer-based drugs directly to the medial layer of an artery using a perfusion catheter. Such cell-specific targeted therapeutic drugs provide a potentially safer and more effective treatment option for patients with vascular disease.