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al cardiopulmonary circulation impairment.

Adrenal insufficiency (AI) is associated with increased cardiovascular morbidity and mortality and reduced quality of life (QoL). Optimum glucocorticoid (GC) dosing and timing are crucial in the treatment of AI, yet the natural circadian secretion of cortisol is difficult to mimic. The once-daily dual-release hydrocortisone (DR-HC) preparation (Plenadren®), offers a more physiological cortisol profile and may address unmet needs.

An investigator-initiated, prospective, cross-over study in patients with AI. Following baseline assessment of cardiometabolic risk factors and QoL, patients switched from their usual hydrocortisone regimen to a once-daily dose equivalent of DR-HC and were reassessed after 12 weeks of treatment.

Fifty-one patients (21 PAI/30 SAI) completed the study. Mean age was 41.6 years (s.d. 13), and 58% (n = 30) were male. The median daily HC dose before study entry was 20 mg (IQR 15-20 mg). After 3 months on DR-HC, the mean SBP decreased by 5.7 mmHg, P = 0.0019 and DBP decreased by 4.5 mference for DR-HC.

Individuals can experience different manifestations of the same psychological disorder. This underscores the need for a personalized model approach in the study of psychopathology. Emerging adulthood is a developmental phase wherein individuals are especially vulnerable to psychopathology. Given their exposure to repeated stressors and disruptions in routine, the emerging adult population is worthy of investigation.

In our prospective study, we aim to conduct multimodal assessments to determine the feasibility of an individualized approach for understanding the contextual factors of changes in daily affect, sleep, physiology, and activity. In other words, we aim to use event mining to predict changes in mental health.

We expect to have a final sample size of 20 participants. Recruited participants will be monitored for a period of time (ie, between 3 and 12 months). Participants will download the Personicle app on their smartphone to track their activities (eg, home events and cycling). They will also be given wearable sensor devices (ie, devices that monitor sleep, physiology, and physical activity), which are to be worn continuously. Participants will be asked to report on their daily moods and provide open-ended text responses on a weekly basis. Sabutoclax Participants will be given a battery of questionnaires every 3 months.

Our study has been approved by an institutional review board. The study is currently in the data collection phase. Due to the COVID-19 pandemic, the study was adjusted to allow for remote data collection and COVID-19-related stress assessments.

Our study will help advance research on individualized approaches to understanding health and well-being through multimodal systems. Our study will also demonstrate the benefit of using individualized approaches to study interrelations among stress, social relationships, technology, and mental health.

DERR1-10.2196/25775.

DERR1-10.2196/25775.This article addresses the design issue of fuzzy asynchronous fault detection filter (FAFDF) for a class of nonlinear Markov jump systems by an event-triggered (ET) scheme. The ET scheme can be applied to cut down the transmission times from the system to FAFDF. It is assumed that the system modes cannot be obtained synchronously by the filter, and instead, there is a detector that can measure the estimated modes of the system. The asynchronous phenomenon between the system and the filter is characterized via a hidden Markov model with partly accessible mode detection probabilities. Applying the Lyapunov function methods, sufficient conditions for the presence of FAFDF are obtained. Finally, an application of a wheeled mobile manipulator with hybrid joints is employed to clarify that the devised FAFDF can detect the faults without any incorrect alarm.The optimal strategy estimation of random evolutionary Boolean games (REBGs) is discussed in this article. First, using the minimum mean square error criterion, the optimal strategy estimator is proposed for REBGs. Then, a matrix approach is developed to calculate the optimal strategy estimator by the aid of a semitensor product of matrices, which includes the prediction matrix, updating distribution, and strategy iterative formula. Finally, an elucidative example is included to show the obtained results are valid.Capillary blood pressure (CBP) is the primary driving force for fluid exchange across microvessels. Subclinical systemic venous congestion prior to overt peripheral edema can directly result in elevated peripheral CBP. Therefore, CBP measurements can enable timely edema control in a variety of clinical cases including venous insufficiency, heart failure and so on. However, currently CBP measurements can be only done invasively and with a complicated experimental setup. In this work, we proposed an opto-mechanical system to achieve non-invasive and automatic CBP measurements through modifying the widely implemented oscillometric technique in home-use arterial blood pressure monitors. The proposed CBP system is featured with a blue light photoplethysmography sensor embedded in finger/toe cuffs to probe skin capillary pulsations. The experimental results demonstrated the proposed CBP system can track local CBP changes induced by different levels of venous congestion. Leveraging the decision tree technique, we demonstrate the use of a multi-site CBP measurement at fingertips and toes to classify four categories of subjects (total N = 40) including patients with peripheral arterial disease, varicose veins and heart failure. Our work demonstrates the promising non-invasive CBP measurement as well as its great potential in realizing point-of-care systems for the management of cardiovascular diseases.Accurately diagnosing and describing the severity of vitiligo is crucial for prognostication, treatment selection and comparison. Currently, disease severity scores require dermatologists to estimate percentage area of involvement, which is subjected to inter and intra-assessor variability. Previous studies focus on pure skin but vitiligo on the face, which has a more serious impact on patients' quality of life, was completely neglected. Convolutional neural networks (CNNs) have good performance on many segmentation tasks. However, due to data privacy, it is hard to have a large clinical vitiligo face image dataset to train a CNN. To address this challenge, images from two different sources, the Internet and the proposed vitiligo face synthesis algorithm, are employed in training. 843 vitiligo images taken from different viewpoints were collected from the Internet. These images are hugely different from the target clinical images collected according to a newly established international standard. To have more vitiligo face images similar to the target clinical images to enhance segmentation performance, an image synthesis algorithm is proposed.