Psychomotor symptoms, cognitive impairments and suicidal thoughts after COVID-19 infection: a case report and the possible allostatic mechanism.

Although neuropsychiatric manifestations are common in survivors of coronavirus disease 2019 (COVID-19), the pathophysiology is not yet elucidated. Here we describe the case of a geriatric inpatient who developed postCOVID depression with psychomotor retardation, anxiety, hopelessness, executive function problems, and suicidal ideations. The language problems and cognitive impairments coemerged with the motor problems. We propose a mechanism associated with problems in energy prediction and regulation in which the coronavirus infection, which causes neuroinflammation and viral activity in the nervous system, interferes with the reward pathway and sensory prediction process. Sigma-1 receptor agonists such as sertraline may regulate energy expenditure and, thus, be beneficial to the process. The treatment improvements in our patient included those in the autonomic nervous system, activity, and circadian rhythm.

Neurobiological mechanisms of dialectical behavior therapy and Morita therapy, two psychotherapies inspired by Zen.

Psychotherapy is a learning process. Updating the prediction models of the brain may be the mechanism underlying psychotherapeutic changes. Although developed in different eras and cultures, dialectical behavior therapy (DBT) and Morita therapy are influenced by Zen principles, and both emphasize the acceptance of reality and suffering. This article reviews these two treatments, their common and distinct therapeutic factors, and their neuroscientific implications. Additionally, it proposes a framework that includes the predictive function of the mind, constructed emotions, mindfulness, therapeutic relationship, and changes enabled via reward predictions. Brain networks, including the Default Mode Network (DMN), amygdala, fear circuitry, and reward pathways, contribute to the constructive process of brain predictions. Both treatments target the assimilation of prediction errors, gradual reorganization of predictive models, and creation of a life with step-by-step constructive rewards. By elucidating the possible neurobiological mechanisms of these psychotherapeutic techniques, this article is expected to serve as the first step towards filling the cultural gap and creating more teaching methods based on these concepts.

Novel Lagrange interpolation polynomials for dynamic access control in a healthcare cloud system.

The authority of user personal health records (PHRs) is usually determined by the owner of a cloud computing system. When a PHR file is accessed, a dynamic access control algorithm must be used to authenticate the users. The proposed dynamic access control algorithm is based on a novel Lagrange interpolation polynomial with timestamps, mainly functioning to authenticate the users with key information. Moreover, the inclusion of timestamps allows user access within an approved time slot to enhance the security of the healthcare cloud system. According to the security analysis results, this healthcare cloud system can effectively resist common attacks, including external attacks, internal attacks, collaborative attacks and equation-based attacks. Furthermore, the overall computational complexity of establishing and updating the polynomials is O(n*m* (log m)2), which is a promising result, where m denotes the degree of $ polynomial~G\left(x, y\right) $ and n denotes the number of secure users in the hierarchy.

Panic Attack Prediction Using Wearable Devices and Machine Learning: Development and Cohort Study.

BACKGROUND: A panic attack (PA) is an intense form of anxiety accompanied by multiple somatic presentations, leading to frequent emergency department visits and impairing the quality of life. A prediction model for PAs could help clinicians and patients monitor, control, and carry out early intervention for recurrent PAs, enabling more personalized treatment for panic disorder (PD). OBJECTIVE: This study aims to provide a 7-day PA prediction model and determine the relationship between a future PA and various features, including physiological factors, anxiety and depressive factors, and the air quality index (AQI). METHODS: We enrolled 59 participants with PD (Diagnostic and Statistical Manual of Mental Disorders, 5th edition, and the Mini International Neuropsychiatric Interview). Participants used smartwatches (Garmin Vívosmart 4) and mobile apps to collect their sleep, heart rate (HR), activity level, anxiety, and depression scores (Beck Depression Inventory [BDI], Beck Anxiety Inventory [BAI], State-Trait Anxiety Inventory state anxiety [STAI-S], State-Trait Anxiety Inventory trait anxiety [STAI-T], and Panic Disorder Severity Scale Self-Report) in their real life for a duration of 1 year. We also included AQIs from open data. To analyze these data, our team used 6 machine learning methods: random forests, decision trees, linear discriminant analysis, adaptive boosting, extreme gradient boosting, and regularized greedy forests. RESULTS: For 7-day PA predictions, the random forest produced the best prediction rate. Overall, the accuracy of the test set was 67.4%-81.3% for different machine learning algorithms. The most critical variables in the model were questionnaire and physiological features, such as the BAI, BDI, STAI, MINI, average HR, resting HR, and deep sleep duration. CONCLUSIONS: It is possible to predict PAs using a combination of data from questionnaires and physiological and environmental data.

Confidentiality Protection of Digital Health Records in Cloud Computing.

Electronic medical records containing confidential information were uploaded to the cloud. The cloud allows medical crews to access and manage the data and integration of medical records easily. This data system provides relevant information to medical personnel and facilitates and improve electronic medical record management and data transmission. A structure of cloud-based and patient-centered personal health record (PHR) is proposed in this study. This technique helps patients to manage their health information, such as appointment date with doctor, health reports, and a completed understanding of their own health conditions. It will create patients a positive attitudes to maintain the health. The patients make decision on their own for those whom has access to their records over a specific span of time specified by the patients. Storing data in the cloud environment can reduce costs and enhance the share of information, but the potential threat of information security should be taken into consideration. This study is proposing the cloud-based secure transmission mechanism is suitable for multiple users (like nurse aides, patients, and family members).