Feasibility and Acceptability of a Mobile App for Prolonged Grief Disorder Symptoms.

Background: Mobile apps provide a unique platform for mental health assessment and monitoring. They can provide real time, accessible data on symptoms of mental disorders that may yield rich data for detailed clinical assessment and help individuals gain insight into their current mental state. We developed one of the first apps for tracking symptoms of prolonged grief disorder. Method: In this pilot feasibility study, we assess the feasibility and acceptability of a new mobile app mGAGE for use once a day for 3 weeks. 27 participants completed mental health assessments at t1 and t2. Results: Adherence to the app protocol was very high with 100% for the first two weeks of use. A surprising finding was the improvement of grief symptoms at t2. Debriefing interviews revealed general qualitative categories including positive feedback, negative feedback and specific recommendations. Overall, the app was found to be feasible for use for the first two weeks and acceptable for bereaved individuals. Conclusions: This app could provide valuable data for in depth clinical assessment, may support individuals to gain greater insight into their symptoms and may have a therapeutic effect in terms of improved grief symptoms. Implications for future studies including use in larger intervention studies are discussed.

Effectiveness of a facilitation programme using a mobile application for initiating advance care planning discussions between patients with advanced cancer and healthcare providers: protocol for a randomised controlled trial (J-SUPPORT 2104).

INTRODUCTION: Timely implementation of the discussion process of advance care planning (ACP) is recommended. The communication attitude of healthcare providers is critical in ACP facilitation; thus, improving their communication attitudes may reduce patient distress and unnecessary aggressive treatment while enhancing care satisfaction. Digital mobile devices are being developed for behavioural interventions owing to their low space and time restrictions and ease of information sharing. This study aims to evaluate the effectiveness of an intervention programme using an application intended to facilitate patient questioning behaviour on improving communication related to ACP between patients with advanced cancer and healthcare providers. METHODS AND ANALYSIS: This study uses a parallel-group, evaluator-blind, randomised controlled trial design. We plan to recruit 264 adult patients with incurable advanced cancer at the National Cancer Centre in Tokyo, Japan. Intervention group participants use a mobile application ACP programme and undergo a 30 min interview with a trained intervention provider for discussions with the oncologist at the next patient visit, while control group participants continue their usual treatment. The primary outcome is the oncologist's communication behaviour score assessed using audiorecordings of the consultation. Secondary outcomes include communication between patients and oncologists and the patients' distress, quality of life, care goals and preferences, and medical care utilisation. We will use a full analysis set including the registered participant population who receive at least a part of the intervention. ETHICS AND DISSEMINATION: The study protocol was reviewed and approved by the Scientific Advisory Board of the Japan Supportive, Palliative and Psychosocial Oncology Group (Registration No. 2104) and the Institutional Review Board of the National Cancer Centre Hospital (registration No. 2020-500). Written informed consent is obtained from the patients. The results of the trial will be published in peer-reviewed scientific journals and presented at scientific meetings. TRIAL REGISTRATION NUMBERS: UMIN000045305, NCT05045040.

Effect of smartphone-based cognitive behavioral therapy app on insomnia: a randomized, double-blind study.

STUDY OBJECTIVES: This study assessed the effects and safety of the smartphone-based cognitive behavioral therapy for insomnia (CBT-I) app compared with the sham app. METHODS: In this multicenter, double-blind, and parallel-group study, 175 patients with insomnia were randomized to a smartphone-based CBT-I app (Active, n = 87) or a sham app (Sham, n = 88) group. The primary endpoint was the change in Athens Insomnia Score (AIS) from baseline after 8 weeks of treatment. RESULTS: The change in AIS (mean ± standard deviation) from baseline, assessed using a modified-intent-to-treat analysis, was -6.7 ± 4.4 in the Active group and -3.3 ± 4.0 in the Sham group. The difference in the mean change between the groups was -3.4 (p < .001), indicating a greater change in the Active group. The change in CGI-I from the baseline was 1.3 ± 0.8 in the Active group and 0.7 ± 0.8 in the Sham group (p < .001). The proportion of patients with an AIS less than 6 was 37.9% in the Active group and 10.2% in the Sham group (p < .001). As for the safety assessment, no adverse reactions or device failures were detected in the Active group. CONCLUSIONS: This study demonstrated the effectiveness of a smartphone-based CBT-I system for treating insomnia. CLINICAL TRIAL REGISTRATION: ID: jRCT2032210071; trial name: Sham (software)-controlled, multicenter, dynamic allocation, double-blinded study of non-medication therapy with a software Yukumi in patients with insomnia disorders (verification study); URL: https://jrct.niph.go.jp/en-latest-detail/jRCT2032210071.

Elucidation of the working principle of a gene-directed caged HDAC inhibitor with cell-type selectivity.

Histone deacetylases (HDACs) play crucial roles in the epigenetic regulation of gene expression. Here, we report CM-Bhc-SAHA, a novel caged HDAC inhibitor, genetically targeting cells of interest. Mammalian cells expressing porcine liver esterase led to the optochemical inhibition of endogenous HDAC activity when treated with CM-Bhc-SAHA and irradiated with 405 nm light.

Cardiorespiratory fitness in breast cancer survivors: a randomised controlled trial of home-based smartphone supported high intensity interval training.

BACKGROUND: A strategy for maintaining and/or improving cardiorespiratory fitness (CRF) in the growing population of cancer survivors is of major clinical importance, especially in the COVID-19 era. The effect of unsupervised high-intensity interval training (HIIT) on increasing CRF in breast cancer survivors is unknown. PURPOSE: The purpose of this study was to determine whether the newly developed habit-B programme, which involves home-based smartphone-supported HIIT using body weight exercises, improves CRF in early-stage breast cancer survivors. METHODS: This single-centre, 12-week, parallel-group, single-blind, randomised controlled trial involved 50 women with stage I-IIa breast cancer, aged 20-59 years, who had completed initial treatment except for hormone therapy. Participants were randomised to either the exercise or control group. The primary outcome was the 12-week change in peak oxygen uptake [Formula: see text]. Other outcomes included muscle strength, 6 min walk test, resting heart rate, physical activity, fatigue, safety and quality of life. RESULTS: The change in [Formula: see text] and leg strength increased significantly in the exercise group compared with the control group (p<0.01). Changes in other outcomes were not significantly different between the groups. CONCLUSION: A home-based HIIT intervention can lead to improve CRF and muscle strength in early-stage breast cancer survivors.

Comorbid insomnia among breast cancer survivors and its prediction using machine learning: a nationwide study in Japan.

OBJECTIVE: Insomnia is an increasingly recognized major symptom of breast cancer which can seriously disrupt the quality of life during and many years after treatment. Sleep problems have also been linked with survival in women with breast cancer. The aims of this study were to estimate the prevalence of insomnia in breast cancers survivors, clarify the clinical characteristics of their sleep difficulties and use machine learning techniques to explore clinical insights. METHODS: Our analysis of data, obtained in a nationwide questionnaire survey of breast cancer survivors in Japan, revealed a prevalence of suspected insomnia of 37.5%. With the clinical data obtained, we then used machine learning algorithms to develop a classifier that predicts comorbid insomnia. The performance of the prediction model was evaluated using 8-fold cross-validation. RESULTS: When using optimal hyperparameters, the L2 penalized logistic regression model and the XGBoost model provided predictive accuracy of 71.5 and 70.6% for the presence of suspected insomnia, with areas under the curve of 0.76 and 0.75, respectively. Population segments with high risk of insomnia were also extracted using the RuleFit algorithm. We found that cancer-related fatigue is a predictor of insomnia in breast cancer survivors. CONCLUSIONS: The high prevalence of sleep problems and its link with mortality warrants routine screening. Our novel predictive model using a machine learning approach offers clinically important insights for the early detection of comorbid insomnia and intervention in breast cancer survivors.

Data Validation and Verification Using Blockchain in a Clinical Trial for Breast Cancer: Regulatory Sandbox.

BACKGROUND: The integrity of data in a clinical trial is essential, but the current data management process is too complex and highly labor-intensive. As a result, clinical trials are prone to consuming a lot of budget and time, and there is a risk for human-induced error and data falsification. Blockchain technology has the potential to address some of these challenges. OBJECTIVE: The aim of the study was to validate a system that enables the security of medical data in a clinical trial using blockchain technology. METHODS: We have developed a blockchain-based data management system for clinical trials and tested the system through a clinical trial for breast cancer. The project was conducted to demonstrate clinical data management using blockchain technology under the regulatory sandbox enabled by the Japanese Cabinet Office. RESULTS: We verified and validated the data in the clinical trial using the validation protocol and tested its resilience to data tampering. The robustness of the system was also proven by survival with zero downtime for clinical data registration during a Amazon Web Services disruption event in the Tokyo region on August 23, 2019. CONCLUSIONS: We show that our system can improve clinical trial data management, enhance trust in the clinical research process, and ease regulator burden. The system will contribute to the sustainability of health care services through the optimization of cost for clinical trials.

Study protocol for a nationwide questionnaire survey of physical activity among breast cancer survivors in Japan.

INTRODUCTION: A major concern is that few cancer survivors meet the guidelines for recommended levels of physical activity. No studies have investigated physical activity among breast cancer survivors nationwide in Japan. Therefore, the aims of this study are to identify the levels of physical activity among breast cancer survivors, to examine factors-related physical activity among breast cancer survivors and to identify breast cancer survivors' preferences for and interest in exercise programmes in order to inform the future programme development. METHODS AND ANALYSIS: We will administer a cross-sectional survey using a self-report questionnaire to breast cancer survivors. At each of 50 facilities selected to include a variety of institutional backgrounds according to the population distribution of different regions throughout Japan, we will consecutively distribute the questionnaire to 30 outpatients who have completed initial treatments, except for hormone therapy. The target sample size is 1500 survivors. We will calculate descriptive statistics for each measurement item and perform univariate and multivariate analyses using outcome measures (eg, physical activity and quality of life) related to physical, psychological, social and environmental factors. DISCUSSION: This is the first nationwide survey of physical activity levels among breast cancer survivors in Japan. Identifying the factors associated with physical activity will help us to develop, disseminate and implement programmes that encourage more survivors to adhere to physical activity guidelines. ETHICS AND DISSEMINATION: The protocol was approved by the Institutional Review Board (IRB) of the National Cancer Center on 11 January 2019 (ID: 2018-295). In addition, many of the participating facilities required ethical approval from their local IRBs, while others did not. Accordingly, approval from the local IRBs of individual facilities was obtained when required. The findings will be disseminated through peer-reviewed publications and conference presentations.

XGBoost, a Machine Learning Method, Predicts Neurological Recovery in Patients with Cervical Spinal Cord Injury.

The accurate prediction of neurological outcomes in patients with cervical spinal cord injury (SCI) is difficult because of heterogeneity in patient characteristics, treatment strategies, and radiographic findings. Although machine learning algorithms may increase the accuracy of outcome predictions in various fields, limited information is available on their efficacy in the management of SCI. We analyzed data from 165 patients with cervical SCI, and extracted important factors for predicting prognoses. Extreme gradient boosting (XGBoost) as a machine learning model was applied to assess the reliability of a machine learning algorithm to predict neurological outcomes compared with that of conventional methodology, such as a logistic regression or decision tree. We used regularly obtainable data as predictors, such as demographics, magnetic resonance variables, and treatment strategies. Predictive tools, including XGBoost, a logistic regression, and a decision tree, were applied to predict neurological improvements in the functional motor status (ASIA [American Spinal Injury Association] Impairment Scale [AIS] D and E) 6 months after injury. We evaluated predictive performance, including accuracy and the area under the receiver operating characteristic curve (AUC). Regarding predictions of neurological improvements in patients with cervical SCI, XGBoost had the highest accuracy (81.1%), followed by the logistic regression (80.6%) and the decision tree (78.8%). Regarding AUC, the logistic regression showed 0.877, followed by XGBoost (0.867) and the decision tree (0.753). XGBoost reliably predicted neurological alterations in patients with cervical SCI. The utilization of predictive machine learning algorithms may enhance personalized management choices through pre-treatment categorization of patients.

Dopamine modulates the optomotor response to unreliable visual stimuli in Drosophila melanogaster.

State-dependent modulation of sensory systems has been studied in many organisms and is possibly mediated through neuromodulators such as monoamine neurotransmitters. Among these, dopamine is involved in many aspects of animal behaviour, including movement control, attention, motivation and cognition. However, the precise neural mechanism underlying dopaminergic modulation of behaviour induced by sensory stimuli remains poorly understood. Here, we used Drosophila melanogaster to show that dopamine can modulate the optomotor response to moving visual stimuli including noise. The optomotor response is the head-turning response to moving objects, which is observed in most sight-reliant animals including mammals and insects. First, the effects of the dopamine system on the optomotor response were investigated in mutant flies deficient in dopamine receptors D1R1 or D1R2, which are involved in the modulation of sleep-arousal in flies. We examined the optomotor response in D1R1 knockout (D1R1 KO) and D1R2 knockout (D1R2 KO) flies and found that it was not affected in D1R1 KO flies; however, it was significantly reduced in D1R2 KO flies compared with the wild type. Using cell-type-specific expression of an RNA interference construct of D1R2, we identified the fan-shaped body, a part of the central complex, responsible for dopamine-mediated modulation of the optomotor response. In particular, pontine cells in the fan-shaped body seemed important in the modulation of the optomotor response, and their neural activity was required for the optomotor response. These results suggest a novel role of the central complex in the modulation of a behaviour based on the processing of sensory stimulations.

Extracellular ADP augments microglial inflammasome and NF-κB activation via the P2Y12 receptor.

The NLRP3 inflammasome is a molecular complex that translates signals from pathogens and tissue damage into inflammatory responses, and plays crucial roles in numerous neurological diseases. Activation of the NLRP3 inflammasome leads to caspase-1 dependent cleavage of pro-IL-1ß to form mature IL-1ß. By acting on the P2X7 purinergic receptor, extracellular ATP is one of the major stimuli that activates the NLRP3 inflammasome. Although microglia express multiple purinergic receptors, their roles in inflammasome-mediated inflammation are largely unknown. We studied the role of the P2Y12 receptor, a metabotropic P2Y receptor enriched in microglia, on inflammation in vitro. Inhibition of the microglial P2Y12 receptor by PSB0739 or siRNA knockdown suppressed IL-1ß release. P2Y12 receptor-deficient microglia displayed markedly attenuated IL-1ß mRNA expression and release. P2Y12 receptor blockade also suppressed IL-6 production. Both IL-1ß and IL-6 responses were augmented by extracellular ADP or ADP-ßS and were abrogated by PSB0739. Mechanistically, ADP-ßS potentiated NF-κB activation. In addition, ADP altered mitochondrial membrane potential in combination with ATP and increased the number of caspase-1 positive cells through the P2Y12 receptor. These results elucidate a novel inflammatory mechanism by which extracellular ADP acts on the P2Y12 receptor to activate NF-κB and the NLRP3 inflammasome to enhance microglial inflammation.

Effect of home-based high-intensity interval training and behavioural modification using information and communication technology on cardiorespiratory fitness and exercise habits among sedentary breast cancer survivors: habit-B study protocol for a randomised controlled trial.

INTRODUCTION: Maintaining high levels of physical activity helps to maintain and improve physical health and quality of life, and plays a role in reducing adverse effects due to cancer treatments. Moreover, a greater degree of cardiorespiratory fitness is associated with reduced risk of all-cause mortality. However, there are no home-based programme for improving cardiorespiratory fitness using body weight exercises for breast cancer survivors. This study will assess the efficacy of the newly developed habit-B programme on maximum oxygen uptake compared with treatment as usual with wearable device. The effects of this programme on exercise habits, level of physical activity, physical fitness and subjective indices will also be investigated. METHODS AND ANALYSIS: This is a 12-week, parallel-group, single-blind, randomised controlled trial. Allocation will be managed by a central server using a computer-generated random allocation sequence provided by an independent data centre. Participants will be assigned to the habit-B programme (high-intensity interval training, exercise counselling + guidance, home-based exercise support using information and communication technology, and a wearable device) or treatment as usual with a wearable device. Subjects will be sedentary women aged 20-59 years who have received breast surgery in the past 2-13 months after the diagnosis of invasive breast cancer (stages I-IIa) and have never received chemotherapy except for hormone therapy. The primary endpoint is the change in peak oxygen uptake (VO2peak; mL/kg/min) between the groups after 12 weeks of intervention. ETHICS AND DISSEMINATION: The study protocol was approved by the Institutional Review Board of the National Cancer Center Japan on 28 February 2019 (ID: 2018-347). The findings will be disseminated through peer-reviewed publications and conference presentations. TRIAL REGISTRATION NUMBER: UMIN000036400.

Secure and Scalable mHealth Data Management Using Blockchain Combined With Client Hashchain: System Design and Validation.

BACKGROUND: Blockchain is emerging as an innovative technology for secure data management in many areas, including medical practice. A distributed blockchain network is tolerant against network fault, and the registered data are resistant to tampering and revision. The technology has a high affinity with digital medicine like mobile health (mHealth) and provides reliability to the medical data without labor-intensive third-party contributions. On the other hand, the reliability of the medical data is not insured before registration to the blockchain network. Furthermore, there are issues with regard to how the clients' mobile devices should be dealt with and authenticated in the blockchain network in order to avoid impersonation. OBJECTIVE: The aim of the study was to design and validate an mHealth system that enables the compatibility of the security and scalability of the medical data using blockchain technology. METHODS: We designed an mHealth system that sends medical data to the blockchain network via relay servers. The architecture provides scalability and convenience of operation of the system. In order to ensure the reliability of the data from clients' mobile devices, hash values with chain structure (client hashchain) were calculated in the clients' devices and the results were registered on the blockchain network. RESULTS: The system was applied and deployed in mHealth for insomnia treatment. Clinical trials for mHealth were conducted with insomnia patients. Medical data of the recruited patients were successfully registered with the blockchain network via relay servers along with the hashchain calculated on the clients' mobile devices. The correctness of the data was validated by identifying illegal data, which were made by simulating fraudulent access. CONCLUSIONS: Our proposed mHealth system, blockchain combined with client hashchain, ensures compatibility of security and scalability in the data management of mHealth medical practice. TRIAL REGISTRATION: UMIN Clinical Trials Registry UMIN000032951; https://upload.umin.ac.jp/cgi-open- bin/ctr_e/ctr_view.cgi?recptno=R000037564 (Archived by WebCite at http://www.webcitation.org/78HP5iFIw).

Sweetness induces sleep through gustatory signalling independent of nutritional value in a starved fruit fly.

Starvation reduces sleep in various animal species including humans and fruit flies. Immediate hunger and the following insufficient nutritional status resulting from starvation may affect sleep and arousal differently. In order to clarify the mechanism underlying the relationship between diet and sleep, we analysed the sleep behaviour of Drosophila melanogaster that were either starved or fed with different types of sugars. Starved flies showed longer activity bouts, short sleep bouts and a decreased arousal threshold. Non-nutritive sweeteners such as sucralose and arabinose, which are sweet but not nutritive, induced sleep in starved flies, but sleep bout length and the arousal threshold was short and decreased, respectively. On the other hand, sorbitol, which is not sweet but nutritive, did not induce sleep, but slightly increased the lowered arousal threshold. Activation of sweetness receptor expressing neurons induced sleep in starved flies. These results suggest that sweetness alone is sufficient to induce sleep in starved flies and that the nutritional status affects sleep homeostasis by decreasing the arousal threshold, which resulted in short sleep bouts in Drosophila.

Tamper-Resistant Mobile Health Using Blockchain Technology.

BACKGROUND: Digital health technologies, including telemedicine, mobile health (mHealth), and remote monitoring, are playing a greater role in medical practice. Safe and accurate management of medical information leads to the advancement of digital health, which in turn results in a number of beneficial effects. Furthermore, mHealth can help lower costs by facilitating the delivery of care and connecting people to their health care providers. Mobile apps help empower patients and health care providers to proactively address medical conditions through near real-time monitoring and treatment, regardless of the location of the patient or the health care provider. Additionally, mHealth data are stored in servers, and consequently, data management that prevents all forms of manipulation is crucial for both medical practice and clinical trials. OBJECTIVE: The aim of this study was to develop and evaluate a tamper-resistant mHealth system using blockchain technology, which enables trusted and auditable computing using a decentralized network. METHODS: We developed an mHealth system for cognitive behavioral therapy for insomnia using a smartphone app. The volunteer data collected with the app were stored in JavaScript Object Notation format and sent to the blockchain network. Thereafter, we evaluated the tamper resistance of the data against the inconsistencies caused by artificial faults. RESULTS: Electronic medical records collected using smartphones were successfully sent to a private Hyperledger Fabric blockchain network. We verified the data update process under conditions where all the validating peers were running normally. The mHealth data were successfully updated under network faults. We further ensured that any electronic health record registered to the blockchain network was resistant to tampering and revision. The mHealth data update was compatible with tamper resistance in the blockchain network. CONCLUSIONS: Blockchain serves as a tamperproof system for mHealth. Combining mHealth with blockchain technology may provide a novel solution that enables both accessibility and data transparency without a third party such as a contract research organization.

Detection and Quantification of MicroRNAs by Ligase-Assisted Sandwich Hybridization on a Microarray.

Extracellular microRNAs (miRNAs) in body fluids have been identified as promising biomarkers for different human diseases. The high-throughput, multiplexed detection and quantification of these miRNAs are highly beneficial for the rapid and accurate diagnosis of diseases. Here, we developed a simple and convenient microarray-based technique, named ligase-assisted sandwich hybridization (LASH), for the detection and quantification of miRNAs. The LASH assay involves the hybridization of capture and detection probe pairs with the target miRNA to form a double-stranded structure which is then nick-sealed by T4 DNA ligase. Using this assay, we successfully demonstrated the multiplexed detection and quantification of different miRNAs in total RNA samples derived from blood obtained within 3 h. Here, we provide a detailed protocol for the LASH assay to detect a specific miRNA, as a model for the detection and quantification of extracellular miRNAs.

The NMDA Receptor Promotes Sleep in the Fruit Fly, Drosophila melanogaster.

Considerable evidence indicates that sleep is essential for learning and memory. Drosophila melanogaster has emerged as a novel model for studying sleep. We previously found a short sleeper mutant, fumin (fmn), and identified its mutation in the dopamine transporter gene. We reported similarities in the molecular basis of sleep and arousal regulation between mammals and Drosophila. In aversive olfactory learning tasks, fmn mutants demonstrate defective memory retention, which suggests an association between sleep and memory. In an attempt to discover additional sleep related genes in Drosophila, we carried out a microarray analysis comparing mRNA expression in heads of fmn and control flies and found that 563 genes are differentially expressed. Next, using the pan-neuronal Gal4 driver elav-Gal4 and UAS-RNA interference (RNAi) to knockdown individual genes, we performed a functional screen. We found that knockdown of the NMDA type glutamate receptor channel gene (Nmdar1) (also known as dNR1) reduced sleep. The NMDA receptor (NMDAR) plays an important role in learning and memory both in Drosophila and mammals. The application of the NMDAR antagonist, MK-801, reduced sleep in control flies, but not in fmn. These results suggest that NMDAR promotes sleep regulation in Drosophila.

Glial dysfunction causes age-related memory impairment in Drosophila.

Several aging phenotypes, including age-related memory impairment (AMI), are thought to be caused by cumulative oxidative damage. In Drosophila, age-related impairments in 1 hr memory can be suppressed by reducing activity of protein kinase A (PKA). However, the mechanism for this effect has been unclear. Here we show that decreasing PKA suppresses AMI by reducing activity of pyruvate carboxylase (PC), a glial metabolic enzyme whose amounts increase upon aging. Increased PC activity causes AMI through a mechanism independent of oxidative damage. Instead, increased PC activity is associated with decreases in D-serine, a glia-derived neuromodulator that regulates NMDA receptor activity. D-serine feeding suppresses both AMI and memory impairment caused by glial overexpression of dPC, indicating that an oxidative stress-independent dysregulation of glial modulation of neuronal activity contributes to AMI in Drosophila.