On the nutritional and therapeutic effects of ketone body D-ß-hydroxybutyrate.

D-ß-hydroxybutyrate (D-3HB), a monomer of microbial polyhydroxybutyrate (PHB), is also a natural ketone body produced during carbohydrate deprivation to provide energy to the body cells, heart, and brain. In recent years, increasing evidence demonstrates that D-3HB can induce pleiotropic effects on the human body which are highly beneficial for improving physical and metabolic health. Conventional ketogenic diet (KD) or exogenous ketone salts (KS) and esters (KE) have been used to increase serum D-3HB level. However, strict adaptation to the KD was often associated with poor patient compliance, while the ingestion of KS caused gastrointestinal distresses due to excessive consumption of minerals. As for ingestion of KE, subsequent degradation is required before releasing D-3HB for absorption, making these methods somewhat inferior. This review provides novel insights into a biologically synthesized D-3HB (D-3-hydroxybutyric acid) which can induce a faster increase in plasma D-3HB compared to the use of KD, KS, or KE. It also emphasizes on the most recent applications of D-3HB in different fields, including its use in improving exercise performance and in treating metabolic or age-related diseases. Ketones may become a fourth micro-nutrient that is necessary to the human body along with carbohydrates, proteins, and fats. Indeed, D-3HB being a small molecule with multiple signaling pathways within the body exhibits paramount importance in mitigating metabolic and age-related diseases. Nevertheless, specific dose-response relationships and safety margins of using D-3HB remain to be elucidated with more research. KEY POINTS: • D-3HB induces pleiotropic effects on physical and metabolic health. • Exogenous ketone supplements are more effective than ketogenic diet. • d-3HB as a ketone supplement has long-term healthy impact.

Machine Learning Approaches for Exercise Exertion Level Classification Using Data from Wearable Physiologic Monitors.

This research aimed to develop a model for real-time prediction of aerobic exercise exertion levels. ECG signals were registered during 16-minute cycling exercises. Perceived ratings of exertion (RPE) were collected each minute from the study participants. Based on the reported RPE, each consecutive minute of the exercise was assigned to the "high exertion" or "low exertion" class. The characteristics of heart rate variability (HRV) in time and frequency domains were used as predictive features. The top ten ranked predictive features were selected using the minimum redundancy maximum relevance (mRMR) algorithm. The support vector machine demonstrated the highest accuracy with an F1 score of 82%.

Use of Artificial Intelligence for Predicting COVID-19 Outcomes: A Scoping Review.

During the COVID-19 pandemic, artificial intelligence has played an essential role in healthcare analytics. Scoping reviews have been shown to be instrumental for analyzing recent trends in specific research areas. This paper aimed at applying the scoping review methodology to analyze the papers that used artificial intelligence (AI) models to forecast COVID-19 outcomes. From the initial 1,057 articles on COVID-19, 19 articles satisfied inclusion/exclusion criteria. We found that the tree-based models were the most frequently used for extracting information from COVID-19 datasets. 25% of the papers used time series to transform and analyze their data. The largest number of articles were from the United States and China. The reviewed artificial intelligence methods were able to predict cases, death, mortality, and severity. AI tools can serve as powerful means for building predictive analytics during pandemics.

Using Big Data to Identify Impact of Asthma on Mortality in Patients with COVID-19.

The goal of this paper was to assess if mortality in COVID-19 positive patients is affected by a history of asthma in anamnesis. A total of 48,640 COVID-19 positive patients were included in our analysis. A propensity score matching was carried out to match each asthma patient with two patients without history of chronic respiratory diseases in one stratum. Matching was based on age, comorbidity score, and gender. Conditional logistics regression was used to compute within each strata. There were 5,557 strata in this model. We included asthma, ethnicity, race, and BMI as risk factors. The results showed that the presence of asthma in anamnesis is a statistically significant protective factor from mortality in COVID-19 positive patients.

Assessing Disparities in COVID-19 Testing Using National COVID Cohort Collaborative.

With NCATS National COVID Cohort Collaborative (N3C) dataset, we evaluated 14 billion medical records and identified more than 12 million patients tested for COVID-19 across the US. To assess potential disparities in COVID-19 testing, we chose ten US states and then compared each state's population distribution characteristics with distribution of corresponding characteristics from N3C. Minority racial groups were more prevalent in the N3C dataset as compared to census data. The proportion of Hispanics and Latinos in N3C was slightly lower than in the state census. Patients over 65 years old had higher representation in the N3C dataset and patients under 18 were underrepresented. Proportion of females in the N3C was higher compared with the state data. All ten states in N3C showed a higher representation of urban population versus rural population compared to census data.

Telemonitoring of Home-Based Biking Exercise: Assessment of Wireless Interfaces.

BACKGROUND: Telerehabiliation has been shown to have great potential in expanding access to rehabilitation services, enhancing patients' quality of life, and improving clinical outcomes. Stationary biking exercise can serve as an effective aerobic component of home-based physical rehabilitation programs. Remote monitoring of biking exercise provides necessary safeguards to ensure exercise adherence and safety in patients' homes. The scalability of the current remote monitoring of biking exercise solutions is impeded by the high cost that limits patient access to these services, especially among older adults with chronic health conditions. OBJECTIVE: The aim of this project was to design and test two low-cost wireless interfaces for the telemonitoring of home-based biking exercise. METHODS: We designed an interactive biking system (iBikE) that comprises a tablet PC and a low-cost bike. Two wireless interfaces to monitor the revolutions per minute (RPM) were built and tested. The first version of the iBikE system uses Bluetooth Low Energy (BLE) to send information from the iBikE to the PC tablet, and the second version uses a Wi-Fi network for communication. Both systems provide patients and their clinical teams the capability to monitor exercise progress in real time using a simple graphical representation. The bike can be used for upper or lower limb rehabilitation. We developed two tablet applications with the same graphical user interfaces between the application and the bike sensors but with different communication protocols (BLE and Wi-Fi). For testing purposes, healthy adults were asked to use an arm bike for three separate subsessions (1 minute each at a slow, medium, and fast pace) with a 1-minute resting gap. While collecting speed values from the iBikE application, we used a tachometer to continuously measure the speed of the bikes during each subsession. Collected data were later used to assess the accuracy of the measured data from the iBikE system. RESULTS: Collected RPM data in each subsession (slow, medium, and fast) from the iBikE and tachometer were further divided into 4 categories, including RPM in every 10-second bin (6 bins), RPM in every 20-second bin (3 bins), RPM in every 30-second bin (2 bins), and RPM in each 1-minute subsession (60 seconds, 1 bin). For each bin, the mean difference (iBikE and tachometer) was then calculated and averaged for all bins in each subsession. We saw a decreasing trend in the mean RPM difference from the 10-second to the 1-minute measurement. For the 10-second measurements during the slow and fast cycling, the mean discrepancy between the wireless interface and tachometer was 0.67 (SD 0.24) and 1.22 (SD 0.67) for the BLE iBike, and 0.66 (SD 0.48) and 0.87 (SD 0.91) for the Wi-Fi iBike system, respectively. For the 1-minute measurements during the slow and fast cycling, the mean discrepancy between the wireless interface and tachometer was 0.32 (SD 0.26) and 0.66 (SD 0.83) for the BLE iBike, and 0.21 (SD 0.21) and 0.47 (SD 0.52) for the Wi-Fi iBike system, respectively. CONCLUSIONS: We concluded that a low-cost wireless interface provides the necessary accuracy for the telemonitoring of home-based biking exercise.