Alvarezogden2652

We report on the newly started project "SCH Personalized Depression Treatment Supported by Mobile Sensor Analytics". ICI-182780 The current best practice guidelines for treating depression call for close monitoring of patients, and periodically adjusting treatment as needed. This project will advance personalized depression treatment by developing a system, DepWatch, that leverages mobile health technologies and machine learning tools. The objective of DepWatch is to assist clinicians with their decision making process in the management of depression. The project comprises two studies. Phase I collects sensory data and other data, e.g., clinical data, ecological momentary assessments (EMA), tolerability and safety data from 250 adult participants with unstable depression symptomatology initiating depression treatment. The data thus collected will be used to develop and validate assessment and prediction models, which will be incorporated into DepWatch system. In Phase II, three clinicians will use DepWatch to support their clinical decision making process. A total of 128 participants under treatment by the three participating clinicians will be recruited for the study. A number of new machine learning techniques will be developed.Purpose Brain-computer interface (BCI) techniques may provide computer access for individuals with severe physical impairments. However, the relatively hidden nature of BCI control obscures how BCI systems work behind the scenes, making it difficult to understand how electroencephalography (EEG) records the BCI related brain signals, what brain signals are recorded by EEG, and why these signals are targeted for BCI control. Furthermore, in the field of speech-language-hearing, signals targeted for BCI application have been of primary interest to clinicians and researchers in the area of augmentative and alternative communication (AAC). However, signals utilized for BCI control reflect sensory, cognitive and motor processes, which are of interest to a range of related disciplines including speech science. Method This tutorial was developed by a multidisciplinary team emphasizing primary and secondary BCI-AAC related signals of interest to speech-language-hearing. Results An overview of BCI-AAC related signals are provided discussing 1) how BCI signals are recorded via EEG, 2) what signals are targeted for non-invasive BCI control, including the P300, sensorimotor rhythms, steady state evoked potentials, contingent negative variation, and the N400, and 3) why these signals are targeted. During tutorial creation, attention was given to help support EEG and BCI understanding for those without an engineering background. Conclusion Tutorials highlighting how BCI-AAC signals are elicited and recorded can help increase interest and familiarity with EEG and BCI techniques and provide a framework for understanding key principles behind BCI-AAC design and implementation.Purpose. There is no gold-standard health literacy measure. The Single Item Literacy Screener (SILS) and Subjective Literacy Screener (SLS) ask people to self-report ability to understand health information. They were developed in older adults, before common use of electronic health information. This study explored whether the SILS and SLS related to objective literacy, numeracy, and comprehension among young adults, and whether specifying "online" or "paper-based" wording affected these relationships. Methods. Eligible individuals (18-35 years of age, English-speaking, US residents) from an online survey company were randomized to 1) original measures; 2) measures adding "paper-based" to describe health information/forms; or 3) measures adding "online" to describe health information/forms. We examined how each measure related to e-Health Literacy (eHEALS), subjective numeracy (SNS), objective numeracy (ONS), and comprehension of a short passage. Results. A total of 848/1342 respondents correctly answered attwhen prompted to think about electronic or paper-based information. Researchers should consider clearer instructions or modified wording when using these measures in this population.The following fictional case is intended as a learning tool within the Pathology Competencies for Medical Education (PCME), a set of national standards for teaching pathology. These are divided into three basic competencies Disease Mechanisms and Processes, Organ System Pathology, and Diagnostic Medicine and Therapeutic Pathology. For additional information, and a full list of learning objectives for all three competencies, see http//journals.sagepub.com/doi/10.1177/2374289517715040.1.The following fictional case is intended as a learning tool within the Pathology Competencies for Medical Education (PCME), a set of national standards for teaching pathology. These are divided into three basic competencies Disease Mechanisms and Processes, Organ System Pathology, and Diagnostic Medicine and Therapeutic Pathology. For additional information, and a full list of learning objectives for all three competencies, see http//journals.sagepub.com/doi/10.1177/2374289517715040.1.Coronary and peripheral stents are implants that are inserted into blocked arteries to restore blood flow. After stent deployment, the denudation of the endothelial cell (EC) layer and the resulting inflammatory cascade can lead to restenosis, the renarrowing of the vessel wall due to the hyperproliferation and excessive matrix secretion of smooth muscle cells (SMCs). Despite advances in drug-eluting stents (DES), restenosis remains a clinical challenge and can require repeat revascularizations. In this study, we investigated how vascular cell phenotype can be modulated by nanotopographical cues on the stent surface, with the goal of developing an alternative strategy to DES for decreasing restenosis. We fabricated TiO2 nanotubes and demonstrated that this topography can decrease SMC surface coverage without affecting endothelialization. In addition, to our knowledge, this is the first study reporting that TiO2 nanotube topography dampens the response to inflammatory cytokine stimulation in both endothelial and smooth muscle cells. We observed that compared to flat titanium surfaces, nanotube surfaces attenuated tumor necrosis factor alpha (TNFα)-induced vascular cell adhesion molecule-1 (VCAM-1) expression in ECs by 1.8-fold and decreased TNFα-induced SMC growth by 42%. Further, we found that the resulting cellular phenotype is sensitive to changes in nanotube diameter and that 90 nm diameter nanotubes leads to the greatest magnitude in cell response compared to 30 or 50 nm nanotubes.