Search

BackgroundCancer treatments can cause a variety of symptoms that impair quality of life and functioning but are frequently missed by clinicians. Smartphone and wearable sensors may capture behavioral and physiological changes indicative of symptom burden, enabling passive and remote real-time monitoring of fluctuating symptoms ObjectiveThe aim of this study was to examine whether smartphone and Fitbit data could be used to estimate daily symptom burden before and after pancreatic surgery. MethodsA total of 44 patients scheduled for pancreatic surgery participated in this prospective longitudinal study and provided sufficient sensor and self-reported symptom data for analyses. Participants collected smartphone sensor and Fitbit data and completed daily symptom ratings starting at least two weeks before surgery, throughout their inpatient recovery, and for up to 60 days after postoperative discharge. Day-level behavioral features reflecting mobility and activity patterns, sleep, screen time, heart rate, and communication were extracted from raw smartphone and Fitbit data and used to classify the next day as high or low symptom burden, adjusted for each individual’s typical level of reported symptoms. In addition to the overall symptom burden, we examined pain, fatigue, and diarrhea specifically. ResultsModels using light gradient boosting machine (LightGBM) were able to correctly predict whether the next day would be a high symptom day with 73.5% accuracy, surpassing baseline models. The most important sensor features for discriminating high symptom days were related to physical activity bouts, sleep, heart rate, and location. LightGBM models predicting next-day diarrhea (79.0% accuracy), fatigue (75.8% accuracy), and pain (79.6% accuracy) performed similarly. ConclusionsResults suggest that digital biomarkers may be useful in predicting patient-reported symptom burden before and after cancer surgery. Although model performance in this small sample may not be adequate for clinical implementation, findings support the feasibility of collecting mobile sensor data from older patients who are acutely ill as well as the potential clinical value of mobile sensing for passive monitoring of patients with cancer and suggest that data from devices that many patients already own and use may be useful in detecting worsening perioperative symptoms and triggering just-in-time symptom management interventions.

Chronic lung diseases are one of the leading causes of death worldwide. Lung transplantation is currently the only causal therapeutic for lung diseases, which is restricted to end-stage disease and limited by low access to donor lungs. Lung tissue engineering (LTE) is a promising approach to regenerating a replacement for at least a part of the damaged lung tissue. Currently, lung regeneration is limited to a simplified local level (e.g., alveolar–capillary barrier) due to the sophisticated and complex structure and physiology of the lung. Here, we introduce an extracellular matrix (ECM)-integrated scaffold using a cellularization–decellularization–recellularization technique. This ECM-integrated scaffold was developed on our artificial co-polymeric BETA (biphasic elastic thin for air–liquid interface cell culture conditions) scaffold, which were initially populated with human lung fibroblasts (IMR90 cell line), as the main generator of ECM proteins. Due to the interconnected porous structure of the thin (4OH and 0.1% Triton X100 with subsequent DNase treatment completely removed the fibroblasts (from the first cellularization) and maintains collagen I and IV as the key ECM proteins on the scaffold. We also showed the repopulation of the primary fibroblast from human (without chronic lung disease (non-CLD) donors) and human bronchial epithelial (16HBE14o−) cells on the ECM-integrated BETA scaffold. With this technique, we developed a biomimetic scaffold that can mimic both the physico-mechanical properties and the native microenvironment of the lung ECM. The results indicate the potential of the presented bioactive scaffold for LTE application.

Evolution has endowed the lung with exceptional design providing a large surface area for gas exchange area (ca. 100 m2) in a relatively small tissue volume (ca. 6 L). This is possible due to a complex tissue architecture that has resulted in one of the most challenging organs to be recreated in the lab. The need for realistic and robust in vitro lung models becomes even more evident as causal therapies, especially for chronic respiratory diseases, are lacking. Here, we describe the Cyclic InVItroCell-stretch (CIVIC) “breathing” lung bioreactor for pulmonary epithelial cells at the air-liquid interface (ALI) experiencing cyclic stretch while monitoring stretch-related parameters (amplitude, frequency, and membrane elastic modulus) under real-time conditions. The previously described biomimetic copolymeric BETA membrane (5 μm thick, bioactive, porous, and elastic) was attempted to be improved for even more biomimetic permeability, elasticity (elastic modulus and stretchability), and bioactivity by changing its chemical composition. This biphasic membrane supports both the initial formation of a tight monolayer of pulmonary epithelial cells (A549 and 16HBE14o−) under submerged conditions and the subsequent cell-stretch experiments at the ALI without preconditioning of the membrane. The newly manufactured versions of the BETA membrane did not improve the characteristics of the previously determined optimum BETA membrane (9.35% PCL and 6.34% gelatin [w/v solvent]). Hence, the optimum BETA membrane was used to investigate quantitatively the role of physiologic cyclic mechanical stretch (10% linear stretch; 0.33 Hz: light exercise conditions) on size-dependent cellular uptake and transepithelial transport of nanoparticles (100 nm) and microparticles (1,000 nm) for alveolar epithelial cells (A549) under ALI conditions. Our results show that physiologic stretch enhances cellular uptake of 100 nm nanoparticles across the epithelial cell barrier, but the barrier becomes permeable for both nano- and micron-sized particles (100 and 1,000 nm). This suggests that currently used static in vitro assays may underestimate cellular uptake and transbarrier transport of nanoparticles in the lung.

Hospital readmissions impose an extreme burden on both health systems and patients. Timely management of the postoperative complications that result in readmissions is necessary to mitigate the effects of these events. However, accurately predicting readmissions is very challenging, and current approaches demonstrated a limited ability to forecast which patients are likely to be readmitted. Our research addresses the challenge of daily readmission risk prediction after the hospital discharge via leveraging the abilities of mobile data streams collected from patients devices in a probabilistic deep learning framework. Through extensive experiments on a real-world dataset that includes smartphone and Fitbit device data from 49 patients collected for 60 days after discharge, we demonstrate our framework’s ability to closely simulate the readmission risk trajectories for cancer patients.

BackgroundFeelings of loneliness are associated with poor physical and mental health. Detection of loneliness through passive sensing on personal devices can lead to the development of interventions aimed at decreasing rates of loneliness. ObjectiveThe aim of this study was to explore the potential of using passive sensing to infer levels of loneliness and to identify the corresponding behavioral patterns. MethodsData were collected from smartphones and Fitbits (Flex 2) of 160 college students over a semester. The participants completed the University of California, Los Angeles (UCLA) loneliness questionnaire at the beginning and end of the semester. For a classification purpose, the scores were categorized into high (questionnaire score>40) and low (≤40) levels of loneliness. Daily features were extracted from both devices to capture activity and mobility, communication and phone usage, and sleep behaviors. The features were then averaged to generate semester-level features. We used 3 analytic methods: (1) statistical analysis to provide an overview of loneliness in college students, (2) data mining using the Apriori algorithm to extract behavior patterns associated with loneliness, and (3) machine learning classification to infer the level of loneliness and the change in levels of loneliness using an ensemble of gradient boosting and logistic regression algorithms with feature selection in a leave-one-student-out cross-validation manner. ResultsThe average loneliness score from the presurveys and postsurveys was above 43 (presurvey SD 9.4 and postsurvey SD 10.4), and the majority of participants fell into the high loneliness category (scores above 40) with 63.8% (102/160) in the presurvey and 58.8% (94/160) in the postsurvey. Scores greater than 1 standard deviation above the mean were observed in 12.5% (20/160) of the participants in both pre- and postsurvey scores. The majority of scores, however, fell between 1 standard deviation below and above the mean (pre=66.9% [107/160] and post=73.1% [117/160]). ConclusionsPassive sensing has the potential for detecting loneliness in college students and identifying the associated behavioral patterns. These findings highlight intervention opportunities through mobile technology to reduce the impact of loneliness on individuals’ health and well-being.

Deep reinforcement learning (DRL) has proven capable of superhuman performance on many complex tasks. To achieve this success, DRL algorithms train a decision-making agent to select the actions that maximize some long-term performance measure. In many consequential real-world domains, however, optimal performance is not enough to justify an algorithm's use--for example, sometimes a system's robustness, stability, or safety must be rigorously ensured. Thus, methods for verifying DRL systems have emerged. These algorithms can guarantee a system's properties over an infinite set of inputs, but the task is not trivial. DRL relies on deep neural networks (DNNs). DNNs are often referred to as "black boxes" because examining their respective structures does not elucidate their decision-making processes. Moreover, the sequential nature of the problems DRL is used to solve promotes significant scalability challenges. Finally, because DRL environments are often stochastic, verification methods must account for probabilistic behavior. To address these complications, a new subfield has emerged. In this survey, we establish the foundations of DRL and DRL verification, define a taxonomy for DRL verification methods, describe approaches for dealing with stochasticity, characterize considerations related to writing specifications, enumerate common testing tasks/environments, and detail opportunities for future research. [ABSTRACT FROM AUTHOR]

AbstractGanoderma lucidum is one of the most well-known fungi, and has many applications in medicine. Ganoderic acid is among the valuable secondary metabolites of Ganoderma lucidum, and responsible for the inhibition of the tumor cell growth and cancer treatment. Application of ganoderic acid has been limited because of low yields of its production from Ganoderma lucidum. The present study aims to investigate the synergistic effect of elicitors including methyl jasmonate and aspirin on the production of ganoderic acid derived from Ganoderma lucidum mushroom in a shaken flasks using response surface methodology. The results showed that the optimal dose of methyl jasmonate and asprin significantly impacts on the amount of ganoderic acid production as a response (p