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Glucocorticoids (GC) are a controversial yet commonly used intervention in the clinical management of acute inflammatory conditions, including sepsis or traumatic injury. In the context of major trauma such as surgery, concerns have been raised regarding adverse effects from GC, thereby necessitating a better understanding of how GCs modulate the immune response. Here we report the results of a randomized controlled trial (NCT02542592) in which we employ a high-dimensional mass cytometry approach to characterize innate and adaptive cell signaling dynamics after a major surgery (primary outcome) in patients treated with placebo or methylprednisolone (MP). A robust, unsupervised bootstrap clustering of immune cell subsets coupled with random forest analysis shows profound (AUC = 0.92, p-value = 3.16E-8) MP-induced alterations of immune cell signaling trajectories, particularly in the adaptive compartments. By contrast, key innate signaling responses previously associated with pain and functional recovery after surgery, including STAT3 and CREB phosphorylation, are not affected by MP. These results imply cell-specific and pathway-specific effects of GCs, and also prompt future studies to examine GCs’ effects on clinical outcomes likely dependent on functional adaptive immune responses., Erratum published online 03 September 2020 DOI: 10.1038/s41467-020-18410-y

Stroke is a leading cause of cognitive impairment and dementia, but the mechanisms that underlie post-stroke cognitive decline are not well understood. Stroke produces profound local and systemic immune responses that engage all major innate and adaptive immune compartments. However, whether the systemic immune response to stroke contributes to long-term disability remains ill-defined. We used a single-cell mass cytometry approach to comprehensively and functionally characterize the systemic immune response to stroke in longitudinal blood samples from 24 patients over the course of 1 year and correlated the immune response with changes in cognitive functioning between 90 and 365 days post-stroke. Using elastic net regularized regression modelling, we identified key elements of a robust and prolonged systemic immune response to ischaemic stroke that occurs in three phases: an acute phase (Day 2) characterized by increased signal transducer and activator of transcription 3 (STAT3) signalling responses in innate immune cell types, an intermediate phase (Day 5) characterized by increased cAMP response element-binding protein (CREB) signalling responses in adaptive immune cell types, and a late phase (Day 90) by persistent elevation of neutrophils, and immunoglobulin M(+) (IgM(+)) B cells. By Day 365 there was no detectable difference between these samples and those from an age- and gender-matched patient cohort without stroke. When regressed against the change in the Montreal Cognitive Assessment scores between Days 90 and 365 after stroke, the acute inflammatory phase Elastic Net model correlated with post-stroke cognitive trajectories (r = −0.692, Bonferroni-corrected P = 0.039). The results demonstrate the utility of a deep immune profiling approach with mass cytometry for the identification of clinically relevant immune correlates of long-term cognitive trajectories.

Early identification of pregnant women at risk for preeclampsia (PE) is important, as it will enable targeted interventions ahead of clinical manifestations. The quantitative analyses of plasma proteins feature prominently among molecular approaches used for risk prediction. However, derivation of protein signatures of sufficient predictive power has been challenging. The recent availability of platforms simultaneously assessing over 1000 plasma proteins offers broad examinations of the plasma proteome, which may enable the extraction of proteomic signatures with improved prognostic performance in prenatal care. The primary aim of this study was to examine the generalizability of proteomic signatures predictive of PE in two cohorts of pregnant women whose plasma proteome was interrogated with the same highly multiplexed platform. Establishing generalizability, or lack thereof, is critical to devise strategies facilitating the development of clinically useful predictive tests. A second aim was to examine the generalizability of protein signatures predictive of gestational age (GA) in uncomplicated pregnancies in the same cohorts to contrast physiological and pathological pregnancy outcomes. Serial blood samples were collected during the first, second, and third trimesters in 18 women who developed PE and 18 women with uncomplicated pregnancies (Stanford cohort). The second cohort (Detroit), used for comparative analysis, consisted of 76 women with PE and 90 women with uncomplicated pregnancies. Multivariate analyses were applied to infer predictive and cohort-specific proteomic models, which were then tested in the alternate cohort. Gene ontology (GO) analysis was performed to identify biological processes that were over-represented among top-ranked proteins associated with PE. The model derived in the Stanford cohort was highly significant (p = 3.9E–15) and predictive (AUC = 0.96), but failed validation in the Detroit cohort (p = 9.7E–01, AUC = 0.50). Similarly, the model derived in the Detroit cohort was highly significant (p = 1.0E–21, AUC = 0.73), but failed validation in the Stanford cohort (p = 7.3E–02, AUC = 0.60). By contrast, proteomic models predicting GA were readily validated across the Stanford (p = 1.1E–454, R = 0.92) and Detroit cohorts (p = 1.1.E–92, R = 0.92) indicating that the proteomic assay performed well enough to infer a generalizable model across studied cohorts, which makes it less likely that technical aspects of the assay, including batch effects, accounted for observed differences. Results point to a broader issue relevant for proteomic and other omic discovery studies in patient cohorts suffering from a clinical syndrome, such as PE, driven by heterogeneous pathophysiologies. While novel technologies including highly multiplex proteomic arrays and adapted computational algorithms allow for novel discoveries for a particular study cohort, they may not readily generalize across cohorts. A likely reason is that the prevalence of pathophysiologic processes leading up to the “same” clinical syndrome can be distributed differently in different and smaller-sized cohorts. Signatures derived in individual cohorts may simply capture different facets of the spectrum of pathophysiologic processes driving a syndrome. Our findings have important implications for the design of omic studies of a syndrome like PE. They highlight the need for performing such studies in diverse and well-phenotyped patient populations that are large enough to characterize subsets of patients with shared pathophysiologies to then derive subset-specific signatures of sufficient predictive power.

The dense network of interconnected cellular signalling responses that are quantifiable in peripheral immune cells provides a wealth of actionable immunological insights. Although high-throughput single-cell profiling techniques, including polychromatic flow and mass cytometry, have matured to a point that enables detailed immune profiling of patients in numerous clinical settings, the limited cohort size and high dimensionality of data increase the possibility of false-positive discoveries and model overfitting. We introduce a generalizable machine learning platform, the immunological Elastic-Net (iEN), which incorporates immunological knowledge directly into the predictive models. Importantly, the algorithm maintains the exploratory nature of the high-dimensional dataset, allowing for the inclusion of immune features with strong predictive capabilities even if not consistent with prior knowledge. In three independent studies our method demonstrates improved predictions for clinically relevant outcomes from mass cytometry data generated from whole blood, as well as a large simulated dataset. The iEN is available under an open-source licence.

High-throughput single-cell analysis technologies produce an abundance of data that is critical for profiling the heterogeneity of cellular systems. We introduce VoPo (https://github.com/stanleyn/VoPo), a machine learning algorithm for predictive modeling and comprehensive visualization of the heterogeneity captured in large single-cell datasets. In three mass cytometry datasets, with the largest measuring hundreds of millions of cells over hundreds of samples, VoPo defines phenotypically and functionally homogeneous cell populations. VoPo further outperforms state-of-the-art machine learning algorithms in classification tasks, and identified immune-correlates of clinically-relevant parameters., Single-cell technologies are increasingly prominent in clinical applications, but predictive modelling with such data in large cohorts has remained computationally challenging. We developed a new algorithm, ‘VoPo’, for predictive modelling and visualization of single cell data for translational applications.

Designing remote health monitoring systems requires a multi-faceted perspective that takes into account requirements and contexts imposed by the medical application, technology, and end-user. We study such a design perspective in the context of remote and real-time edema monitoring. Edema (accumulation of fluid in certain soft-tissues) is regarded as one of the most important symptoms for systematic diseases such as heart failure. Monitoring edema allows patients and caregivers to understand the state of sickness and effectiveness of the treatments. This article proposes a novel low-power context-aware and real-time wearable platform capable of continuous assessment of ankle edema in remote settings. Our system keeps track of changes in subject’s ankle circumference as well as current body posture. An examination of our system with 15 subjects demonstrates the effectiveness and reliability of the proposed force-sensitive-resistor-based edema sensor (with an $R^2$ of 0.87 for our regression model and intraclass correlation of 0.97) as well as an over 96 percent accuracy in activity monitoring that provide the means to perform reliable data validation on ankle circumference measurements in a continuous manner. Furthermore, we devise a novel derivative-free power optimization approach to maximize the battery lifetime resulting in improvement in battery lifetime by a factor of 2.13.

The dense network of interconnected cellular signaling responses quantifiable in peripheral immune cells provide a wealth of actionable immunological insights. While high-throughput single-cell profiling techniques, including polychromatic flow and mass cytometry, have matured to a point that enables detailed immune profiling of patients in numerous clinical settings, limited cohort size together with the high dimensionality of data increases the possibility of false positive discoveries and model overfitting. We introduce a machine learning platform, the immunological Elastic-Net (iEN), which incorporates immunological knowledge directly into the predictive models. Importantly, the algorithm maintains the exploratory nature of the high-dimensional dataset, allowing for the inclusion of immune features with strong predictive power even if not consistent with prior knowledge. In three independent studies our method demonstrates improved predictive power for clinically-relevant outcomes from mass cytometry data generated from whole blood, as well as a large simulated dataset.

The sulfur contents of fossil fuels have negative impacts on the environment and human health. The bio-catalytic desulfurization strategies and the biological refinement of fossil fuels are a cost-effective process compared to classical chemistry desulfurization. Rhodococcus erythropolis IGTS8 is able to metabolize the organic sulfur compound by the unique genes cluster (i.e. DszA, B, C and D genes) in the 4S metabolic pathway. The dszD gene codes a key enzyme for sulfur reduction in the gene cluster. In this study, the structure of the DszD enzyme was predicted and then the key residues toward FMN binding were identified which were Thr62, Ser63, Asn77, and Ala79. To investigate the effect of manipulation in key residues on the enzymatic activity of the DszD, different mutations were performed on key residues. The molecular docking simulation showed that A79I and A79N mutants have the lowest binding free energies compared to the wild-type enzyme in binding with FMN substrate. A 50 ns molecular dynamics (MD) simulation performed using GROMACS software. The RMSD and RMSF analysis showed that two mutants are more stable than the wild-type enzyme during MD simulation. The binding free energies between FMN substrate and complexes were calculated and analyzed by the Molecular Mechanics/Poisson-Boltzmann Surface Area (MM-PBSA) method. The experimental results showed that the enzyme activity for the oxidoreductase process toward biodesulfurization increased 1.9 and 2.3 fold for A79I and A79N mutants, respectively.

The sulfur contents of fossil fuels has negative impacts on the environment and human health. The bio-catalytic desulfurization strategies and the biological refinement of the fossil fuels are a cost-effective process compared to chemical desulfurization. Rhodococcus erythropolis IGTS8 is able to extract the organic sulfur of oil as mineral salts by 4S metabolic pathway (i.e. DszA,B,C and D genes). dszD gene codes a NADH:FMN oxidoreductase delivering reducing equivalent to DszA, DszB, and DszC to remove sulfur from heterocyclic molecules. In this study, we sought to improve DszD specificity by side-direct mutagenesis based on prediction of the DszD structure, molecular docking, and molecular dynamic simulation. Accordingly, the cloning, expression and activity assay of best candidates (A79I and A79N) were performed. Our results demonstrated the important role of position 79 in enzyme activity, the A79I and A79N mutants are able to increase the enzyme activity 3.4 and 5.2 fold compared to wild-type.

Many studies have reported that glaucoma patients experience mobility issues, such as walking slowly and bumping into obstacles frequently. However, little is known to date about how a person’s gait is impacted due to glaucoma. This paper presents design and development of a gait analysis approach using a shoe-integrated sensing system and accompanying machine learning techniques to quantitatively examine gait patterns in glaucoma patients. The customized sensor platform is utilized in a clinical trial conducted with nine glaucoma patients and ten age-matched healthy participants. The signal processing and machine learning algorithms automatically detect effective gait cycles and extract both steady-state and spatio-temporal gait features from the signal segments. We perform machine learning algorithms to distinguish glaucoma patients from healthy controls, and identify several prominent features with high discriminability between the two groups. The results demonstrate that classification algorithms can be used to identify the gait patterns of glaucoma patients with an accuracy higher than 94% in a 10-m-walk test. It is also demonstrated that gait features such as evenness of the sway speed along medio-lateral direction between the two feet are significantly different (p-value < 0.001) between older adults with and without glaucoma. These results suggest that emerging solutions, such as wearable sensing technologies, can be used for continuous and real-time assessment of gait and mobility problems in individuals with low vision, and may open new avenues for using changes in gait patterns for preventing life threatening situations such as falls.

In this review, we describe state-of-the-art digital health solutions for geriatric oncology and explore the potential application of emerging remote health-monitoring technologies in the context of cancer care. We also discuss the benefits and motivations behind adopting technology for symptom monitoring of older adults with cancer. We provide an overview of common symptoms and of the digital solutions–designed remote symptom assessment. We describe state-of-the-art systems for this purpose and highlight the limitations and challenges for the full-scale adoption of such solutions in geriatric oncology. With rapid advances in Internet-of-things technologies, many remote assessment systems have been developed in recent years. Despite showing potential in several health care domains and reliable functionality, few of these solutions have been designed for or tested in older patients with cancer. As a result, the geriatric oncology community lacks a consensus understanding of a possible correlation between remote digital assessments and health-related outcomes. Although the recent development of digital health solutions has been shown to be reliable and effective in many health-related applications, there exists an unmet need for development of systems and clinical trials specifically designed for remote cancer management of older adults with cancer, including developing advanced remote technologies for cancer-related symptom assessment and psychological behavior monitoring at home and developing outcome-oriented study protocols for accurate evaluation of existing or emerging systems. We conclude that perhaps the clearest path to future large-scale use of remote digital health technologies in cancer research is designing and conducting collaborative studies involving computer scientists, oncologists, and patient advocates.

PURPOSE OF REVIEW: To explore state-of-the-art remote monitoring and emerging new sensing technologies for in-home physical assessment and their application/potential in cancer-care. In addition, we discuss the main functional and non-functional requirements and research challenges of employing such technologies in real-world settings. RECENT FINDINGS: With rapid growth in aging population, effective and efficient patient care has become an important topic. Advances in remote monitoring and in its forefront in-home physical assessment technologies play a fundamental role in reducing the cost and improving the quality of care by complementing the traditional in-clinic health-care. However, there is a gap in medical research community regarding the applicability and potential outcomes of such systems. SUMMARY: While some studies reported positive outcomes using remote assessment technologies such as web/smart phone based self-reports and wearable sensors, the cancer research community is still lacking far behind. Thorough investigation of more advanced technologies in cancer-care is warranted.

One of major challenges in chronic disease self-management is the lack of medication adherence. Despite the proliferation of mobile technologies, the potential of using pervasive computing solutions for improved medication management has remained almost unexplored. In this paper, we present a smart-phone based system capable of delivering adaptive activity-aware medication reminders by learning the user’s activity of daily living and detecting the most appropriate and effective timing for medication reminders centered around the initial user-specified schedule.

Ankle edema an important symptom for monitoring patients with chronic systematic diseases. It is an important indicator of onset or exacerbation of a variety of diseases that disturb cardiovascular, renal, or hepatic system such as heart, liver, and kidney failure, diabetes, etc. The current approaches toward edema assessment are conducted during clinical visits. In-clinic assessments, in addition to being burdensome and expensive, are sometimes not reliable and neglect important contextual factors such as patient's physical activity level and body posture. A novel wearable sensor, namely SmartSock, equipped with accelerometer and flexible stretch sensor embedded in clothing is presented. SmartSock is powered by advanced machine learning, signal processing, and correlation techniques to provide real-time, reliable, and context-rich information in remote settings. Our experiments on human subjects indicate high confidence in activity and posture recognition (with an accuracy of > 96%) as well as reliable edema quantification with intra-class correlation and Pearson correlation of 0.97.

Author(s): Evangelista, Lorraine S; Ghasemzadeh, Hassan; Lee, Jung-Ah; Fallahzadeh, Ramin; Sarrafzadeh, Majid; Moser, Debra K | Abstract: BackgroundIt is unclear whether subgroups of patients may benefit from remote monitoring systems (RMS) and what user characteristics and contextual factors determine effective use of RMS in patients with heart failure (HF).ObjectiveThe study was conducted to determine whether certain user characteristics (i.e. personal and clinical variables) predict use of RMS using advanced machine learning software algorithms in patients with HF.MethodsThis pilot study was a single-arm experimental study with a pre- (baseline) and post- (3 months) design; data from the baseline measures were used for the current data analyses. Sixteen patients provided consent; only 7 patients (mean age 65.8 ± 6.1, range 58-83) accessed the RMS and transmitted daily data (e.g. weight, blood pressure) as instructed during the 12 week study duration.ResultsBaseline demographic and clinical characteristics of users and non-users were comparable for a majority of factors. However, users were more likely to have no HF specialty based care or an automatic internal cardioverter defibrillator. The precision accuracy of decision tree, multilayer perceptron (MLP) and k-Nearest Neighbor (k-NN) classifiers for predicting access to RMS was 87.5%, 90.3%, and 94.5% respectively.ConclusionOur preliminary data show that a small set of baseline attributes is sufficient to predict subgroups of patients who had a higher likelihood of using RMS. While our findings shed light on potential end-users more likely to benefit from RMS-based interventions, additional research in a larger sample is warranted to explicate the impact of user characteristics on actual use of these technologies.

One of the most important challenges in chronic disease self-management is medication non-adherence, which has irrevocable outcomes. Although many technologies have been developed for medication adherence monitoring, the reliability and cost-effectiveness of these approaches are not well understood to date. This paper presents a medication adherence monitoring system by user-activity tracking based on wrist-band wearable sensors. We develop machine learning algorithms that track wrist motions in real-time and identify medication intake activities. We propose a novel data analysis pipeline to reliably detect medication adherence by examining single-wrist motions. Our system achieves an accuracy of 78.3% in adherence detection without need for medication pillboxes and with only one sensor worn on either of the wrists. The accuracy of our algorithm is only 7.9% lower than a system with two sensors that track motions of both wrists.

Background: As commercially available activity trackers are being utilized in clinical trials, the research community remains uncertain about reliability of the trackers, particularly in studies that involve walking aids and low-intensity activities. While these trackers have been tested for reliability during walking and running activities, there has been limited research on validating them during low-intensity activities and walking with assistive tools. Objective: The aim of this study was to (1) determine the accuracy of 3 Fitbit devices (ie, Zip, One, and Flex) at different wearing positions (ie, pants pocket, chest, and wrist) during walking at 3 different speeds, 2.5, 5, and 8 km/h, performed by healthy adults on a treadmill; (2) determine the accuracy of the mentioned trackers worn at different sites during activities of daily living; and (3) examine whether intensity of physical activity (PA) impacts the choice of optimal wearing site of the tracker. Methods: We recruited 15 healthy young adults to perform 6 PAs while wearing 3 Fitbit devices (ie, Zip, One, and Flex) on their chest, pants pocket, and wrist. The activities include walking at 2.5, 5, and 8 km/h, pushing a shopping cart, walking with aid of a walker, and eating while sitting. We compared the number of steps counted by each tracker with gold standard numbers. We performed multiple statistical analyses to compute descriptive statistics (ie, ANOVA test), intraclass correlation coefficient (ICC), mean absolute error rate, and correlation by comparing the tracker-recorded data with that of the gold standard. Results: All the 3 trackers demonstrated good-to-excellent (ICC>0.75) correlation with the gold standard step counts during treadmill experiments. The correlation was poor (ICC