Innate immune cells: Key players of orchestra in modulating tumor microenvironment (TME).

The tumor microenvironment (TME) with vital role in cancer progression is composed of various cells such as endothelial cells, immune cells, and mesenchymal stem cells. In particular, innate immune cells such as macrophages, dendritic cells, myeloid-derived suppressor cells, neutrophils, innate lymphoid cells, γδT lymphocytes, and natural killer cells can either promote or suppress tumor progression when present in the TME. An increase in research on the cross-talk between the TME and innate immune cells will lead to new approaches for anti-tumoral therapeutic interventions. This review primarily focuses on the biology of innate immune cells and their main functions in the TME. In addition, it summarizes several innate immune-based immunotherapies that are currently tested in clinical trials.

Relationship between pain, fatigue, and physical activity levels during a technology-based physical activity intervention.

Objective: The majority of individuals with spinal cord injury (SCI) experience chronic pain. Chronic pain can be difficult to manage because of variability in the underlying pain mechanisms. More insight regarding the relationship between pain and physical activity (PA) is necessary to understand pain responses during PA. The objective of this study is to explore possible relationships between PA levels and secondary conditions including pain and fatigue.Design: Prospective cohort analysis of a pilot study.Setting: Community.Participants: Twenty individuals with SCI took part in the study, and sixteen completed the study.Interventions: Mobile-health (mHealth) based PA intervention for two-months during the three-month study.Outcome measures: Chronic Pain Grade Scale (CPGS) questionnaire, The Wheelchair User's Shoulder Pain Index (WUSPI), Fatigue Severity Scale (FSS), and PA levels measured by the mHealth system.Results: A positive linear relationship was found between light-intensity PA and task-specific pain. However, the relationship between moderate-intensity PA and pain interference was best represented by a curvilinear relationship (polynomial regression of second order). Light-intensity PA showed positive, linear correlation with fatigue at baseline. Moderate-intensity PA was not associated with fatigue during any phase of the study.Conclusion: Our results indicated that PA was associated with chronic pain, and the relationship differed based on intensity and amount of PA performed. Further research is necessary to refine PA recommendations for individuals with SCI who experience chronic pain.Trial registration: ClinicalTrials.gov identifier: NCT03773692.

Correction: Mobile health-based physical activity intervention for individuals with spinal cord injury in the community: A pilot study.

[This corrects the article DOI: 10.1371/journal.pone.0223762.].

Mobile health-based physical activity intervention for individuals with spinal cord injury in the community: A pilot study.

Low levels of physical activity (PA) and high levels of sedentary behavior in individuals with spinal cord injury (SCI) have been associated with secondary conditions such as pain, fatigue, weight gain, and deconditioning. One strategy for promoting regular PA is to provide people with an accurate estimate of everyday PA level. The objective of this research was to use a mobile health-based PA measurement system to track PA levels of individuals with SCI in the community and provide them with a behavior-sensitive, just-in-time-adaptive intervention (JITAI) to improve their PA levels. The first, second, and third phases of the study, each with a duration of one month, involved collecting baseline PA levels, providing near-real-time feedback on PA level (PA Feedback), and providing PA Feedback with JITAI, respectively. PA levels in terms of energy expenditure in kilocalories, and minutes of light- and moderate- or vigorous-intensity PA were assessed by an activity monitor during the study. Twenty participants with SCI took part in this research study with a mean (SD) age of 39.4 (12.8) years and 12.4 (12.5) years since injury. Sixteen participants completed the study. Sixteen were male, 16 had paraplegia, and 12 had complete injury. Within-participant comparisons indicated that only two participants had higher energy expenditure (>10%) or lower energy expenditure (<-10%) during PA Feedback with JITAI compared to the baseline. However, eleven participants (69.0%) had higher light- and/or moderate-intensity PA during PA Feedback with JITAI compared to the baseline. To our knowledge, this is the first study to test a PA JITAI for individuals with SCI that responds automatically to monitored PA levels. The results of this pilot study suggest that a sensor-enabled mobile JITAI has potential to improve PA levels of individuals with SCI. Future research should investigate the efficacy of JITAI through a clinical trial.

Hand Motion Detection in fNIRS Neuroimaging Data.

As the number of people diagnosed with movement disorders is increasing, it becomes vital to design techniques that allow the better understanding of human brain in naturalistic settings. There are many brain imaging methods such as fMRI, SPECT, and MEG that provide the functional information of the brain. However, these techniques have some limitations including immobility, cost, and motion artifacts. One of the most emerging portable brain scanners available today is functional near-infrared spectroscopy (fNIRS). In this study, we have conducted fNIRS neuroimaging of seven healthy subjects while they were performing wrist tasks such as flipping their hand with the periods of rest (no movement). Different models of support vector machine is applied to these fNIRS neuroimaging data and the results show that we could classify the action and rest periods with the accuracy of over 80% for the fNIRS data of individual participants. Our results are promising and suggest that the presented classification method for fNIRS could further be applied to real-time applications such as brain computer interfacing (BCI), and into the future steps of this research to record brain activity from fNIRS and EEG, and fuse them with the body motion sensors to correlate the activities.

WearSense: Detecting Autism Stereotypic Behaviors through Smartwatches.

Autism is a complex developmental disorder that affects approximately 1 in 68 children (according to the recent survey conducted by the Centers for Disease Control and Prevention-CDC) in the U.S., and has become the fastest growing category of special education. Each student with autism comes with her or his own unique needs and an array of behaviors and habits that can be severe and which interfere with everyday tasks. Autism is associated with intellectual disability, impairments in social skills, and physical health issues such as sleep and abdominal disturbances. We have designed an Internet-of-Things (IoT) framework named WearSense that leverages the sensing capabilities of modern smartwatches to detect stereotypic behaviors in children with autism. In this work, we present a study that used the inbuilt accelerometer of a smartwatch to detect three behaviors, including hand flapping, painting, and sibbing that are commonly observed in children with autism. In this feasibility study, we recruited 14 subjects to record the accelerometer data from the smartwatch worn on the wrist. The processing part extracts 34 different features in each dimension of the three-axis accelerometer, resulting in 102 features. Using and comparing various classification techniques revealed that an ensemble of 40 decision trees has the best accuracy of around 94.6%. This accuracy shows the quality of the data collected from the smartwatch and feature extraction methods used in this study. The recognition of these behaviors by using a smartwatch would be helpful in monitoring individuals with autistic behaviors, since the smartwatch can send the data to the cloud for comprehensive analysis and also to help parents, caregivers, and clinicians make informed decisions.

Mobile Phonocardiogram Diagnosis in Newborns Using Support Vector Machine.

Phonocardiogram (PCG) monitoring on newborns is one of the most important and challenging tasks in the heart assessment in the early ages of life. In this paper, we present a novel approach for cardiac monitoring applied in PCG data. This basic system coupled with denoising, segmentation, cardiac cycle selection and classification of heart sound can be used widely for a large number of the data. This paper describes the problems and additional advantages of the PCG method including the possibility of recording heart sound at home, removing unwanted noises and data reduction on a mobile device, and an intelligent system to diagnose heart diseases on the cloud server. A wide range of physiological features from various analysis domains, including modeling, time/frequency domain analysis, an algorithm, etc., is proposed in order to extract features which will be considered as inputs for the classifier. In order to record the PCG data set from multiple subjects over one year, an electronic stethoscope was used for collecting data that was connected to a mobile device. In this study, we used different types of classifiers in order to distinguish between healthy and pathological heart sounds, and a comparison on the performances revealed that support vector machine (SVM) provides 92.2% accuracy and AUC = 0.98 in a time of 1.14 seconds for training, on a dataset of 116 samples.

Bivariate autoregressive state-space modeling of psychophysiological time series data.

Heart rate (HR) and electrodermal activity (EDA) are often used as physiological measures of psychological arousal in various neuropsychology experiments. In this exploratory study, we analyze HR and EDA data collected from four participants, each with a history of suicidal tendencies, during a cognitive task known as the Paced Auditory Serial Addition Test (PASAT). A central aim of this investigation is to guide future research by assessing heterogeneity in the population of individuals with suicidal tendencies. Using a state-space modeling approach to time series analysis, we evaluate the effect of an exogenous input, i.e., the stimulus presentation rate which was increased systematically during the experimental task. Participants differed in several parameters characterizing the way in which psychological arousal was experienced during the task. Increasing the stimulus presentation rate was associated with an increase in EDA in participants 2 and 4. The effect on HR was positive for participant 2 and negative for participants 3 and 4. We discuss future directions in light of the heterogeneity in the population indicated by these findings.