Rheumatoid arthritis (RA) is an inflammatory disease of the joints, which causes severe pain and excessive systemic circulation of harmful inflammatory cytokines. Current treatments are limited, with some patients not responding well, and some experiencing severe and detrimental side effects. Mesenchymal stem cells (MSC) are cell-based therapeutics being evaluated as potent immunomodulators in RA and may provide relief to patients not responding well to drug-based treatments. We evaluated the safety and efficacy of BX-U001 human umbilical cord tissue-derived mesenchymal stem cells (hUC-MSC) to treat RA, in support of a successful investigational new drug application. A collagen-induced arthritis (CIA) mouse model of RA was established in DBA/1 J mice. Mice from the treatment assessment group were given a tail vein infusion of hUC-MSC 24 days after primary RA induction, while control assessment (CA) group mice were given cell-free carrier solution. All animals were evaluated daily for RA symptoms via clinical scoring, blood was taken periodically for cytokine analysis, and mice were dissected at end point for histological analysis. A linear mixed model was used to compare the rate of change among groups. The clinical scores of TA group were significantly reduced compared with CA group (P < 0.01), indicating therapeutic effects. The histological scores of the joints in TA group were significantly lower than those in the CA group (P < 0.05), but had no significant difference compared with Healthy groups (P > 0.05). The concentration of (interleukin) IL-6 in TA group was significantly reduced by 80.0% (P < 0.0001) 2 days after treatment and by 93.4% at the experimental endpoint compared with levels prior to hUC-MSC injection. A single intravenous infusion of hUC-MSC (2 × 10⁶ cells/mouse), to CIA-induced DBA/1 J mice, resulted in significant alleviation of RA symptoms and may provide significant therapeutic benefits in humans.

This dataset was collected from university students before, during, and after the COVID-19 lockdown in Southern California. Data collection happened continuously for the average of 7.8 months (SD=3.8, MIN=1.0, MAX=13.4) from a population of 21 students of which 12 have also completed an exit survey, and 7 started before the California COVID-19 lockdown order. This multimodal dataset included different means of data collection such as Samsung Galaxy Watch, Oura Ring, a Life-logger app named Personicle, a questionnaire mobile app named Personicle Questions, and periodical and personalised surveys. The dataset contains raw data from Photoplethysmogram (PPG), Inertial measurement unit (IMU), and pressure sensors in addition to processed data on heart rate, heart rate variability, sleep (bedtime, sleep stages, quality), and physical activity (step, active calories, type of activity). Ecological momentary assessments were collected from participants on daily and weekly bases containing their Positive and Negative Affect Schedule (PANAS) questionnaire and their emotional responses to COVID-19 and their health. Subjective data was also collected through monthly surveys containing standard mood and mental health surveys such as Beck Depression Inventory II (BDI-II), Brief Symptom Inventory (BSI), GAD-7, Inclusion of Other in the Self Scale (IOS-Partner), Acceptability of Intervention Measure (AIM), Intervention Appropriateness Measure (IAM), Feasibility of Intervention Measure (FIM), Experiences in Close Relationships Scale Short Form (ECR-S), UCLA Three-Item Loneliness Scale (ULS), Multidimensional Scale of Perceived Social Support (MSPSS), Investment Model Scale (IMS), Conflict Management Scale (CMS), etc in addition to their response to important events and COVID-19. This dataset can be used to study emotions, mood, physical activity, and lifestyle of young adults through longitudinal subjective and objective measures. This dataset also contains valuable data regarding adjustment of lifestyle and emotions during the events of 2020 and 2021 including COVID-19 discovery and lockdown, Black Life Matter movement, 2020 US presidential elections, etc. On average, participants engaged in the EMA collection study at a rate of 86% (SD=10, MIN=65, MAX=99). Smartwatch usage saw an average participation rate of 51% (SD=20, MIN=16, MAX=88), while engagement with the Oura ring averaged at 85% (SD=12, MIN=60, MAX=99).

Loneliness is linked to wide ranging physical and mental health problems, including increased rates of mortality. Understanding how loneliness manifests is important for targeted public health treatment and intervention. With advances in mobile sending and wearable technologies, it is possible to collect data on human phenomena in a continuous and uninterrupted way. In doing so, such approaches can be used to monitor physiological and behavioral aspects relevant to an individuals loneliness. In this study, we proposed a method for continuous detection of loneliness using fully objective data from smart devices and passive mobile sensing. We also investigated whether physiological and behavioral features differed in their importance in predicting loneliness across individuals. Finally, we examined how informative data from each device is for loneliness detection tasks. We assessed subjective feelings of loneliness while monitoring behavioral and physiological patterns in 30 college students over a 2-month period. We used smartphones to monitor behavioral patterns (e.g., location changes, type of notifications, in-coming and out-going calls/text messages) and smart watches and rings to monitor physiology and sleep patterns (e.g., heart-rate, heart-rate variability, sleep duration). Participants reported their loneliness feeling multiple times a day through a questionnaire app on their phone. Using the data collected from their devices, we trained a random forest machine learning based model to detect loneliness levels. We found support for loneliness prediction using a multi-device and fully-objective approach. Furthermore, behavioral data collected by smartphones generally were the most important features across all participants. The study provides promising results for using objective data to monitor mental health indicators, which could provide a continuous and uninterrupted source of information in mental healthcare applications.