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A force plate is mainly used in biomechanics; it aims to measure the ground reaction force in a persons walking or standing position. In this study, a large-area force mat of the piezoresistance sensing type was developed, and a deep-learning-based weight measurement calibration method was applied to solve the problem in which measurements are not normalized because of physical limitations in hardware and signal processing. The test set was composed of the values measured at each point by weight and the value of the center of the pressure variable, and the measured value was predicted using a deep neural network (DNN) regression model. The calibration verification results show that the average weight errors range from a minimum of 0.06% to a maximum of 3.334%. This is simpler than the previous method, which directly measures the ratio of the resistance value to the measured weight of each sensor and derives an equation.

(1) Background: Various machine learning techniques were used to predict hypertension in Korean adults aged 20 and above, using a range of body composition indicators. Muscle and fat components of body composition are closely related to hypertension. The aim was to identify which body composition indicators are significant predictors of hypertension for each gender; (2) Methods: A model was developed to classify hypertension using six different machine learning techniques, utilizing age, BMI, and body composition indicators such as body fat mass, lean mass, and body water of 2906 Korean men and women; (3) Results: The elastic-net technique demonstrated the highest classification accuracy. In the hypertension prediction model, the most important variables for men were age, skeletal muscle mass (SMM), and body fat mass (BFM), in that order. For women, the significant variables were age and BFM. However, there was no difference between soft lean mass and SMM; (4) Conclusions: Hypertension affects not only BFM but also SMM in men, whereas in women, BFM has a stronger effect than SMM. [ABSTRACT FROM AUTHOR]

The purpose of this study was to investigate the protective effects of 7S,15R-dihydroxy-16S,17S-epoxy-docosapentaenoic acid (diHEP-DPA) in retinal pigment epithelial (RPE) cell damage. ARPE-19 cells, a human RPE cell line, were cultured with diHEP-DPA and Bis-retinoid N-retinyl-N-retinylidene ethanolamine (A2E), followed by exposure to BL. Cell viability and cell death rates were determined. Western blotting was performed to determine changes in apoptotic factors, mitogen-activated protein kinase (MAPK) family proteins, inflammatory proteins, and oxidative and carbonyl stresses. The levels of pro-inflammatory cytokines in the culture medium supernatants were also measured. Exposure to A2E and BL increased the ARPE-19 cell death rate, which was alleviated by diHEP-DPA in a concentration-dependent manner. A2E and BL treatments induced apoptosis in ARPE-19 cells, which was also alleviated by diHEP-DPA. Analysis of the relationship with MAPK proteins revealed that the expression of p-JNK and p-P38 increased after A2E and BL treatments and decreased with exposure to diHEP-DPA in a concentration-dependent manner. DiHEP-DPA also affected the inflammatory response by suppressing the expression of inflammatory proteins and the production of pro-inflammatory cytokines. Furthermore, it was shown that diHEP-DPA regulated the proteins related to oxidative and carbonyl stresses. Taken together, our results provide evidence that diHEP-DPA can inhibit cell damage caused by A2E and BL exposure at the cellular level by controlling various pathways involved in apoptosis and inflammatory responses. [ABSTRACT FROM AUTHOR]

The purpose of this study is to more quantitatively identify changes in body function through various bio-signal parameters. (1) Background: Forest therapy is effective in stabilizing cognitive, emotional, cardiovascular, and autonomic nervous systems. In particular, it is necessary to more quantitatively confirm changes in body functions through various bio signals. (2) Methods: As a forest therapy program (FTP) for the elderly, it consisted of strength training in the forest, respiratory aerobic exercises, and cognitive function training, and a total of 19 sessions were performed for 12 weeks. The electroencephalography (EEG) and Photoplethysmography (PPG) before and after the program were measured and compared between program participants (FTP group) and non-participants (control group). (3) Results: the FTP group showed increase in the alpha band power in EEG and a decrease in the PRV index, Tad, and Tae after the program compared to the control group; (4) Conclusions: Significant differences occurred in the physiological functioning of the elderly participants after the program. This is a result that can confirm the effectiveness of forest therapy more quantitatively. Forest therapy has a positive effect on mental stress reduction and cardiovascular function. [ABSTRACT FROM AUTHOR]