Publications by authors named "Samir Akre"

This study examines the relationship between self-reported and physiologically measured sleep quality and their impact on neurocognitive performance in individuals with depression. Using data from 249 participants with medium to severe depression monitored over 13 weeks, sleep quality was assessed via retrospective self-report and physiological measures from consumer smartphones and smartwatches. Correlations between self-reported and physiological sleep measures were generally weak.

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Digital sensing tools, like smartphones and wearables, offer transformative potential for mental health research by enabling scalable, longitudinal data collection. Realizing this promise requires overcoming significant challenges including limited data standards, underpowered studies, and a disconnect between research aims and community needs. This report, based on the 2023 Workshop on Advancing Digital Sensing Tools for Mental Health, articulates strategies to address these challenges to ensure rigorous, equitable, and impactful research.

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  • Aging affects our immune system because of changes in stem cells that regenerate immune cells.
  • In a study comparing mice with different aging phenotypes, researchers found that certain stem cells in early aging mice showed increased aging-related gene activity, while those in delayed aging mice had genes helping with regulation and external responses.
  • The shifts in blood cell lineage biases among hematopoietic stem cells (HSCs) reveal that targeting specific HSC subsets could be key in developing strategies to delay aging and improve immune function.
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Passive smartphone measures hold significant potential and are increasingly employed in psychological and biomedical research to capture an individual's behavior. These measures involve the near-continuous and unobtrusive collection of data from smartphones without requiring active input from participants. For example, GPS sensors are used to determine the (social) context of a person, and accelerometers to measure movement.

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  • This study developed a machine learning model called Partometer to predict the likelihood of vaginal delivery using real-time data from labor recorded in electronic health records.
  • It analyzed deliveries from 2013 to 2019 at a tertiary care hospital, focusing on two groups: those with lower cesarean rates and a control group, revealing a prediction accuracy of 87.1% for vaginal delivery.
  • The findings suggest that automated machine learning, along with specific clinical factors, enhances the prediction accuracy compared to earlier published models.
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COVID-19 mortality forecasting models provide critical information about the trajectory of the pandemic, which is used by policymakers and public health officials to guide decision-making. However, thousands of published COVID-19 mortality forecasts now exist, many with their own unique methods, assumptions, format, and visualization. As a result, it is difficult to compare models and understand under which circumstances a model performs best.

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Cellular heterogeneity is a major cause of treatment resistance in cancer. Despite recent advances in single-cell genomic and transcriptomic sequencing, it remains difficult to relate measured molecular profiles to the cellular activities underlying cancer. Here, we present an integrated experimental system that connects single cell gene expression to heterogeneous cancer cell growth, metastasis, and treatment response.

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To determine the magnitude of increases in monthly drug-related overdose mortality during the COVID-19 pandemic in the United States. We leveraged provisional records from the Centers for Disease Control and Prevention provided as rolling 12-month sums, which are helpful for smoothing, yet may mask pandemic-related spikes in overdose mortality. We cross-referenced these rolling aggregates with previous monthly data to estimate monthly drug-related overdose mortality for January through July 2020.

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Dedicated lactation rooms are a modern development as mothers return to work while still providing breastmilk to their absent infants. This study describes the built environment microbiome of lactation rooms and daycares, and explores the influence of temperature and humidity on the microbiome of lactation rooms. Sterile swabs were used to collect samples from five different sites in lactation rooms at University of California, Davis and from five different sites in daycares located in Davis, California.

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Elevated triglyceride-rich lipoproteins (TGRL) in circulation is a risk factor for atherosclerosis. TGRL from subjects consuming a high saturated fat test meal elicited a variable inflammatory response in TNFα-stimulated endothelial cells (EC) that correlated strongly with the polyunsaturated fatty acid (PUFA) content. This study investigates how the relative abundance of oxygenated metabolites of PUFA, oxylipins, is altered in TGRL postprandially, and how these changes promote endothelial inflammation.

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species are important commensals capable of dominating the infant gut microbiome, in part by producing acids that suppress growth of other taxa. species are less prone to possessing antimicrobial resistance (AMR) genes (ARGs) than other taxa that may colonize infants. Given that AMR is a growing public health crisis and ARGs are present in the gut microbiome of humans from early life, this study examines the correlation between a dominated infant gut microbiome and AMR levels, measured by a culture-independent metagenomic approach both in early life and as infants become toddlers.

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