Feasibility of a novel wearable thermal device for management of bothersome hot flashes in patients with prostate cancer.

BACKGROUND: This single-arm prospective study evaluated the feasibility of a novel wrist-worn thermal device that applies cooling to the inside of the wrist for management of bothersome hot flashes in prostate cancer survivors. METHODS: 57 individuals were enrolled and instructed to use the thermal device as needed for management of hot flashes for 4 weeks. The primary outcome was thermal device usage (hours and sessions per day). Additional outcomes included the change in Hot Flash Related Daily Interference Scale (HFRDIS, range 0-10) and Patient Reported Outcomes Measurement Information System Sleep Disturbance 4a (PROMIS SD T-score, range 0-100) and Sleep-Related Impairment 8a (PROMIS SRI T-score, range 0-100). Study procedures were conducted remotely from May to Dec 2021 in the US. RESULTS: 44 participants completed the study and 39 had retrievable usage data. The mean ± SD age was 67 ± 6 years and 5 ± 5 years since cancer diagnosis. The baseline mean ± SD HFRDIS score of 4.3 ± 2.0 indicated moderate hot flash interference in this population. During the study, participants used the thermal device (mean ± SD) 3.2 ± 2.5 hours/day and 7.6 ± 3.6 sessions/day. Most (67%) participants reported using the device 7 days and 7 nights each week. Statistically significant improvements from baseline at Week 4 were observed for HFRDIS (mean ± SE change: -1.1 ± 0.3), PROMIS SD (-6.0 ± 1.0), and PROMIS SRI (-5.5 ± 1.2) scores (all p < 0.001). The majority (69%) of participants reported that the thermal device was effective at helping them manage hot flashes. No adverse events were reported. CONCLUSIONS: Results support the feasibility of using the thermal device for management of bothersome hot flashes in prostate cancer survivors. Future randomized controlled studies are warranted to evaluate the impact of the thermal device on frequency and severity of hot flashes, sleep quality, fatigue, and overall quality of life.

Developing OCHROdb, a comprehensive quality checked database of open chromatin regions from sequencing data.

International consortia, including ENCODE, Roadmap Epigenomics, Genomics of Gene Regulation and Blueprint Epigenome have made large-scale datasets of open chromatin regions publicly available. While these datasets are extremely useful for studying mechanisms of gene regulation in disease and cell development, they only identify open chromatin regions in individual samples. A uniform comparison of accessibility of the same regulatory sites across multiple samples is necessary to correlate open chromatin accessibility and expression of target genes across matched cell types. Additionally, although replicate samples are available for majority of cell types, a comprehensive replication-based quality checking of individual regulatory sites is still lacking. We have integrated 828 DNase-I hypersensitive sequencing samples, which we have uniformly processed and then clustered their regulatory regions across all samples. We checked the quality of open-chromatin regions using our replication test. This has resulted in a comprehensive, quality-checked database of Open CHROmatin (OCHROdb) regions for 194 unique human cell types and cell lines which can serve as a reference for gene regulatory studies involving open chromatin. We have made this resource publicly available: users can download the whole database, or query it for their genomic regions of interest and visualize the results in an interactive genome browser.

Single-Cell Chromatin Accessibility Data Combined with GWAS Improves Detection of Relevant Cell Types in 59 Complex Phenotypes.

Several disease risk variants reside on non-coding regions of DNA, particularly in open chromatin regions of specific cell types. Identifying the cell types relevant to complex traits through the integration of chromatin accessibility data and genome-wide association studies (GWAS) data can help to elucidate the mechanisms of these traits. In this study, we created a collection of associations between the combinations of chromatin accessibility data (bulk and single-cell) with an array of 201 complex phenotypes. We integrated the GWAS data of these 201 phenotypes with bulk chromatin accessibility data from 137 cell types measured by DNase-I hypersensitive sequencing and found significant results (FDR adjusted p-value ≤ 0.05) for at least one cell type in 21 complex phenotypes, such as atopic dermatitis, Graves' disease, and body mass index. With the integration of single-cell chromatin accessibility data measured by an assay for transposase-accessible chromatin with high-throughput sequencing (scATAC-seq), taken from 111 adult and 111 fetal cell types, the resolution of association was magnified, enabling the identification of further cell types. This resulted in the identification of significant correlations (FDR adjusted p-value ≤ 0.05) between 15 categories of single-cell subtypes and 59 phenotypes ranging from autoimmune diseases like Graves' disease to cardiovascular traits like diastolic/systolic blood pressure.

Towards Real-Time Prediction of Freezing of Gait in Patients With Parkinson's Disease: A Novel Deep One-Class Classifier.

Freezing of gait (FoG) is a common motor dysfunction in individuals with Parkinson's disease. FoG impairs walking and is associated with increased fall risk. On-demand external cueing systems can detect FoG and provide stimuli to help individuals overcome freezing. Predicting FoG before onset enables preemptive cueing and may prevent FoG. However, detection and prediction remain challenging. If FoG data are not available for an individual, patient-independent models have been used to detect FoG, which have shown great sensitivity and poor specificity, or vice versa. In this study, we introduce a Deep Gait Anomaly Detector (DGAD) using a transfer learning-based approach to improve FoG detection accuracy. We also evaluate the effect of data augmentation and additional pre-FoG segments on prediction rate. Seven individuals with PD performed a series of daily walking tasks wearing inertial measurement units on their ankles. The DGAD algorithm demonstrated average sensitivity and specificity of 63.0% and 98.6% (3.2% improvement compared with the highest specificity in the literature). The target models identified 87.4% of FoG onsets, with 21.9% predicted. This study demonstrates our algorithm's potential for accurate identification of FoG and delivery of cues for patients for whom no FoG data is available for model training.

Towards Real-Time Prediction of Freezing of Gait in Patients With Parkinson's Disease: Addressing the Class Imbalance Problem.

Freezing of gait (FoG) is a common motor symptom in patients with Parkinson's disease (PD). FoG impairs gait initiation and walking and increases fall risk. Intelligent external cueing systems implementing FoG detection algorithms have been developed to help patients recover gait after freezing. However, predicting FoG before its occurrence enables preemptive cueing and may prevent FoG. Such prediction remains challenging given the relative infrequency of freezing compared to non-freezing events. In this study, we investigated the ability of individual and ensemble classifiers to predict FoG. We also studied the effect of the ADAptive SYNthetic (ADASYN) sampling algorithm and classification cost on classifier performance. Eighteen PD patients performed a series of daily walking tasks wearing accelerometers on their ankles, with nine experiencing FoG. The ensemble classifier formed by Support Vector Machines, K-Nearest Neighbors, and Multi-Layer Perceptron using bagging techniques demonstrated highest performance (F1 = 90.7) when synthetic FoG samples were added to the training set and class cost was set as twice that of normal gait. The model identified 97.4% of the events, with 66.7% being predicted. This study demonstrates our algorithm's potential for accurate prediction of gait events and the provision of preventive cueing in spite of limited event frequency.

Prediction of Freezing of Gait in Parkinson's Disease Using Statistical Inference and Lower-Limb Acceleration Data.

The freezing of gait (FoG) is a common type of motor dysfunction in advanced Parkinson's disease (PD) associated with falls. Over the last decade, a significant amount of studies has been focused on detecting FoG episodes in clinical and home environments. Yet, there remains a paucity of techniques regarding real-time prediction of FoG before its occurrence. In this paper, a new algorithm was employed to define the best combination of sensor position, axis, sampling window length, and features to predict FoG. We hypothesized that gait deterioration before FoG onsets can be discriminated from normal gait using statistical analysis of features from successive windows of data collected from lower-limb accelerometers. We defined a new performance measure, "predictivity", to compare the number of correctly predicted FoG events among different combinations. We characterized the system performance using data from 10 PD patients, who experienced FoG while performing several walking tasks in a lab environment. The analysis of 120 different combinations revealed that prediction of FoG can be realized by using an individual shank sensor and sample entropy calculated from the horizontal forward axis with window length of 2 s (88.8%, 92.5%, and 89.0% for average predictivity, sensitivity, and specificity, respectively).

Design and control of an active 1-DoF mechanism for knee rehabilitation.

A 1-DoF robot is designed and fabricated to be used for knee rehabilitation training. The mechanism (robot) is designed to perform specific set of exercises while the patient is sitting on a chair. The therapy process for patients has different stages; each stage consists of specific exercises to recover the knee to its condition before injury. The maximum torque of healthy joint during the extension/flexion exercise is evaluated by simulation and suitable actuator is selected based on the results. A prototype is then fabricated as a platform to evaluate the design and control concepts. The experiment procedure consisting of three stages of therapy indicates good tracking performance and safe operation of the system. Implication for Rehabilitation A 1-DoF mechanism for knee rehabilitation has been designed to perform three stages of therapy: passive, active assist and active resist. The assistive and resistive torque, during active assist and active resist stages, can be set according to the progress in therapy. The results of this study suggest the system has the potential to result in various benefits including reduction of physical workload of physiotherapists and improved repeatability.