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Real-time monitoring and precise diagnosis of the severity of Dengue infection is needed for better decisions in disease management. The aim of this study is to use the Bioimpedance Vector Analysis (BIVA) method to differentiate between healthy subjects and severe and non-severe Dengue-infected patients. Bioimpedance was measured using a 50 KHz single-frequency bioimpedance analyzer. Data from 299 healthy subjects (124 males and 175 females) and 205 serologically confirmed Dengue patients (123 males and 82 females) were analyzed in this study. The obtained results show that the BIVA method was able to assess and classify the body fluid and cell mass condition between the healthy subjects and the Dengue-infected patients. The bioimpedance mean vectors (95% confidence ellipse) for healthy subjects, severe and non-severe Dengue-infected patients were illustrated. The vector is significantly shortened from healthy subjects to Dengue patients; for both genders the p-value is less than 0.0001. The mean vector of severe Dengue patients is significantly shortened compare to non-severe patients with a p-value of 0.0037 and 0.0023 for males and females, respectively. This study confirms that the BIVA method is a valid method in differentiating the healthy, severe and non-severe Dengue-infected subjects. All tests performed had a significance level with a p-value less than 0.05. [ABSTRACT FROM AUTHOR]

In recent years, many improvements have been made in foodborne pathogen detection methods to reduce the impact of food contamination. Several rapid methods have been developed with biosensor devices to improve the way of performing pathogen detection. This paper presents an automated endpoint detection system for amplicons generated by loop mediated isothermal amplification (LAMP) on a microfluidic compact disk platform. The developed detection system utilizes a monochromatic ultraviolet (UV) emitter for excitation of fluorescent labeled LAMP amplicons and a color sensor to detect the emitted florescence from target. Then it processes the sensor output and displays the detection results on liquid crystal display (LCD). The sensitivity test has been performed with detection limit up to 2.5 x 10-3 ng/µL with different DNA concentrations of Salmonella bacteria. This system allows a rapid and automatic endpoint detection which could lead to the development of a point-of-care diagnosis device for foodborne pathogens detection in a resource-limited environment. [ABSTRACT FROM AUTHOR]

Bioimpedance analysis is a noninvasive, low cost and a commonly used approach for body composition measurements and assessment of clinical condition. There are a variety of methods applied for interpretation of measured bioimpedance data and a wide range of utilizations of bioimpedance in body composition estimation and evaluation of clinical status. This paper reviews the main concepts of bioimpedance measurement techniques including the frequency based, the allocation based, bioimpedance vector analysis and the real time bioimpedance analysis systems. Commonly used prediction equations for body composition assessment and influence of anthropometric measurements, gender, ethnic groups, postures, measurements protocols and electrode artifacts in estimated values are also discussed. In addition, this paper also contributes to the deliberations of bioimpedance analysis assessment of abnormal loss in lean body mass and unbalanced shift in body fluids and to the summary of diagnostic usage in different kinds of conditions such as cardiac, pulmonary, renal, and neural and infection diseases. [ABSTRACT FROM AUTHOR]

This paper presents a state-of-the-art survey of smartphone (SP)-based solutions for fall detection and prevention. Falls are considered as major health hazards for both the elderly and people with neurodegenerative diseases. To mitigate the adverse consequences of falling, a great deal of research has been conducted, mainly focused on two different approaches, namely, fall detection and fall prevention. Required hardware for both fall detection and prevention are also available in SPs. Consequently, researchers' interest in finding SP-based solutions has increased dramatically over recent years. To the best of our knowledge, there has been no published review on SP-based fall detection and prevention. Thus in this paper, we present the taxonomy for SP-based fall detection and prevention solutions and systematic comparisons of existing studies. We have also identified three challenges and three open issues for future research, after reviewing the existing articles. Our time series analysis demonstrates a trend towards the integration of external sensing units with SPs for improvement in usability of the systems. [ABSTRACT FROM AUTHOR]

Centrifugal microfluidic platforms are widely used in various advanced processes such as biomedical diagnostics, chemical analysis and drug screening. This paper investigates the effect of liquid density on the burst frequency of the centrifugal microfluidic platform. This effect is experimentally investigated and compared to theoretical values. It is found that increasing the liquid density results in lower burst frequency and it is in agreement with theoretical calculations. Moreover, in this study we proposed the use of the microfluidic CD platform as an inexpensive and simple sensor for liquid density measurements. The proposed liquid sensor requires much less liquid volume (in the range of microliters) compared to conventional density meters. This study presents fundamental work which allows for future advance studies with the aim of designing and fabricating centrifugal microfluidic platforms for more complex tasks such as blood analysis.

Chronic obstructive pulmonary disease (COPD) is responsible for significant morbidity and mortality worldwide. Recent clinical research has indicated a strong association between physiological homeostasis and the onset of COPD exacerbation. Thus the analysis of these variables may yield a means of predicting a COPD exacerbation in the near future. However, the accuracy of existing prediction methods based on statistical analysis of periodic snapshots of physiological variables is still far from satisfactory, due to lack of integration of long-term and interactive effects of the physiological variables. Therefore, developing a relatively accurate method for predicting COPD exacerbation is an outstanding challenge. In this paper, a regression-based machine learning technique was developed, using trend pattern variables extracted from COPD patients' longitudinal physiological records, to classify subjects into "low-risk" and "high-risk" categories, indicating their risk of suffering a COPD exacerbation event. Experimental results from cross validation assessment of the classifier model show an average accuracy of 79.27% using this method.

The objectives of this paper are to present a guideline-based decision support system (GBDSS) design for supporting patient telehealth management of chronic disease and to test its performance in correctly making referral recommendations using routinely recorded measurement data from home telehealth recordings. The GBDSS has been developed to manage lung disease patients in a home telehealth environment. The system operates by checking the availability of home telehealth measurement data on a daily basis, interprets these data using a rule-based decision tree classification, and ultimately generates referral recommendations based on these measured data. The system has demonstrated discriminative power when applied in the analysis of retrospective telehealth data, as a surrogate for realtime referral generation. To this end a telehealth dataset comprising 16 chronic obstructive pulmonary disease (COPD) patients monitored over a 12 month period was used. It was shown that GBDSS referral recommendations could help reduce the number of cases that required a carer's urgent attention by 72.1%, with 81.9% accuracy, 80.8% specificity and 90.4% sensitivity.