Health-related internet use among opioid treatment patients.

The Internet and smartphones have become commonplace and can be effective in overcoming traditional barriers to accessing health information about substance use disorders (SUD), and their prevention or treatment. Little is known, however, about specific factors that may influence the use of these technologies among socioeconomically disadvantaged populations with SUDs. This study characterized the use of digital technologies and the Internet among individuals receiving treatment for opioid use disorder, focusing on identifying predictors of Internet use for health-related purposes. Participants came from an urban opioid replacement therapy program and completed a face-to-face survey on Internet and technology use. We examined the association between online health information seeking and technology acceptance variables, including perceived usefulness, effort expectancy, social influence, and facilitating conditions (e.g., availability of devices/services and technical support). Participants (N = 178, ages 18-64) endorsed high rates of current smartphone ownership (94%) and everyday Internet use (67%). 88% of participants reported searching online for information about health or medical topics in the past 3 months. Predictors of Internet use for health-related purposes were higher technology acceptance for mobile Internet use, younger age, current employment, and less bodily pain. Our results demonstrate high acceptance and use of mobile technology and the Internet among this sample of socioeconomically disadvantaged individuals with SUDs. However, these findings also highlight the importance of identifying barriers that disadvantaged groups face in using mobile technologies when designing technology-based interventions for this population.

Characterization of cell fate probabilities in single-cell data with Palantir.

Single-cell RNA sequencing studies of differentiating systems have raised fundamental questions regarding the discrete versus continuous nature of both differentiation and cell fate. Here we present Palantir, an algorithm that models trajectories of differentiating cells by treating cell fate as a probabilistic process and leverages entropy to measure cell plasticity along the trajectory. Palantir generates a high-resolution pseudo-time ordering of cells and, for each cell state, assigns a probability of differentiating into each terminal state. We apply our algorithm to human bone marrow single-cell RNA sequencing data and detect important landmarks of hematopoietic differentiation. Palantir's resolution enables the identification of key transcription factors that drive lineage fate choice and closely track when cells lose plasticity. We show that Palantir outperforms existing algorithms in identifying cell lineages and recapitulating gene expression trends during differentiation, is generalizable to diverse tissue types, and is well-suited to resolving less-studied differentiating systems.