The FIB-4 scores in the emergency department to predict the outcomes of COVID-19 patients in taiwan.

Objective: We aimed to determine the reliability of using the Fibrosis-4 (FIB-4) index in COVID-19 patients without underlying liver illness. Method: We employed multivariate logistic regression to identify variables that exhibited statistically significant influence on the ultimate outcome. Multilayer perceptron analysis was employed to develop a prediction model for the FIB-4 index concerning ICU admission and intubation rates. However, the scarcity of cases rendered the assessment of the mortality rate unfeasible. We plotted ROC curves to analyze the predictive strength of the FIB-4 index across various age groups. Result: In univariate logistic regression, only the FIB-4 index and respiratory rate demonstrated statistical significance on all poor outcomes. The FIB-4 index for mortality prediction had an ROC and AUC of 0.863 (95% CI: 0.781-0.9444). It demonstrates predictive power across age groups, particularly for age ≥65 (AUC: 0.812, 95% CI: 0.6571-0.9673) and age <65 (AUC: 0.878, 95% CI: 0.8012-0.9558). Its sensitivity for intubation and ICU admission prediction is suboptimal. Conclusion: FIB-4 index had promising power in prediction of mortality rate in all age groups.

Resource use and outcomes in patients with dialysis-dependent chronic kidney disease admitted to intensive care.

BACKGROUND: Central Australia (CA) has a high prevalence of haemodialysis-dependent chronic kidney disease (CKD5D). CKD5D is associated with an increased need for critical care services. AIMS: To describe the demographic features, critical care resource use and outcomes of patients with CKD5D requiring intensive care admission in CA. METHODS: Retrospective matched cohort database study. Patients with CKD5D who required admission for critical illness between 1 July 2015 and 30 June 2016 were identified using the Centre for Outcome and Resource Evaluation Outcome Measurement and Evaluation Tool (CORE COMET) and matched with patients without CKD5D. The primary outcome was all cause mortality. Secondary outcomes explored use of critical care and other ongoing healthcare use. RESULTS: There were 621 critical care admissions during the study period. Of these, CKD5D patients comprised 88 admissions (14%), representing 63 patients. Compared to matched controls, these patients had a similar mortality at a median follow up of 463 days (17% vs 22%, P = 0.50) which did not change when patients with an intensive care unit length of stay (ICU LoS) less than 4 days were excluded. CKD5D patients had a shorter median ICU LoS (1.3 vs 2.9). Although those with CKD5D had higher healthcare resource use, the rate of utilisation remained unchanged by their ICU admission. CONCLUSIONS: This retrospective observational matched cohort study examining the burden of disease amongst CKD5D patients in CA suggests that there is no additional mortality burden in this group, nor do they require significantly higher critical care resources compared to a matched cohort.

Depth-Camera-Based System for Estimating Energy Expenditure of Physical Activities in Gyms.

Energy expenditure (EE) monitoring is crucial to tracking physical activity (PA). Accurate EE monitoring may help people engage in adequate activity and therefore avoid obesity and reduce the risk of chronic diseases. This study proposes a depth-camera-based system for EE estimation of PA in gyms. Most previous studies have used inertial measurement units for EE estimation. By contrast, the proposed system can be used to conveniently monitor subjects' treadmill workouts in gyms without requiring them to wear any devices. A total of 21 subjects were recruited for the experiment. Subjects' skeletal data acquired using the depth camera and oxygen consumption data simultaneously obtained using the K4b2 device were used to establish an EE predictive model. To obtain a robust EE estimation model, depth cameras were placed in the side view, rear side view, and rear view. A comparison of five different predictive models and these three camera locations showed that the multilayer perceptron model was the best predictive model and that placing the camera in the rear view provided the best EE estimation performance. The measured and predicted metabolic equivalents of task exhibited a strong positive correlation, with r = 0.94 and coefficient of determination r2 = 0.89. Furthermore, the mean absolute error was 0.61 MET, mean squared error was 0.67 MET, and root mean squared error was 0.76 MET. These results indicate that the proposed system is handy and reliable for monitoring user's EE when performing treadmill workouts.

PotteryGo: A Virtual Pottery Making Training System.

This article presents a pottery-making training system with a focus on teaching fundamental knowledge and practical techniques in a virtual-reality environment. Gesture analysis makes it possible to correct the learners actions via visual feedback. Our results demonstrate the efficacy in assisting beginners with learning the gestures used in pottery-making.

Preserving privacy of online digital physiological signals using blind and reversible steganography.

BACKGROUND AND OBJECTIVE: Physiological signals such as electrocardiograms (ECG) and electromyograms (EMG) are widely used to diagnose diseases. Presently, the Internet offers numerous cloud storage services which enable digital physiological signals to be uploaded for convenient access and use. Numerous online databases of medical signals have been built. The data in them must be processed in a manner that preserves patients' confidentiality. METHODS: A reversible error-correcting-coding strategy will be adopted to transform digital physiological signals into a new bit-stream that uses a matrix in which is embedded the Hamming code to pass secret messages or private information. The shared keys are the matrix and the version of the Hamming code. RESULTS: An online open database, the MIT-BIH arrhythmia database, was used to test the proposed algorithms. The time-complexity, capacity and robustness are evaluated. Comparisons of several evaluations subject to related work are also proposed. CONCLUSIONS: This work proposes a reversible, low-payload steganographic scheme for preserving the privacy of physiological signals. An (n,  m)-hamming code is used to insert (n - m) secret bits into n bits of a cover signal. The number of embedded bits per modification is higher than in comparable methods, and the computational power is efficient and the scheme is secure. Unlike other Hamming-code based schemes, the proposed scheme is both reversible and blind.

Simple Smartphone-Based Guiding System for Visually Impaired People.

Visually impaired people are often unaware of dangers in front of them, even in familiar environments. Furthermore, in unfamiliar environments, such people require guidance to reduce the risk of colliding with obstacles. This study proposes a simple smartphone-based guiding system for solving the navigation problems for visually impaired people and achieving obstacle avoidance to enable visually impaired people to travel smoothly from a beginning point to a destination with greater awareness of their surroundings. In this study, a computer image recognition system and smartphone application were integrated to form a simple assisted guiding system. Two operating modes, online mode and offline mode, can be chosen depending on network availability. When the system begins to operate, the smartphone captures the scene in front of the user and sends the captured images to the backend server to be processed. The backend server uses the faster region convolutional neural network algorithm or the you only look once algorithm to recognize multiple obstacles in every image, and it subsequently sends the results back to the smartphone. The results of obstacle recognition in this study reached 60%, which is sufficient for assisting visually impaired people in realizing the types and locations of obstacles around them.