Development and Evaluation of a Miniaturized Taste Sensor Chip

A miniaturized taste sensor chip was designed for use in a portable-type taste sensing system. The fabricated sensor chip (40 mm × 26 mm × 2.2 mm) has multiple taste-sensing sites consisting of a poly(hydroxyethyl methacrylate) hydrogel with KCl as the electrolyte layer for stability of the membrane potential and artificial lipid membranes as the taste sensing elements. The sensor responses to the standard taste substances showed high accuracy and good reproducibility, which is comparable with the performance of the sensor probe of the commercialized taste sensing system. Thus, the fabricated taste sensor chip could be used as a key element for the realization of a portable-type taste sensing system

Advanced Taste Sensors Based on Artificial Lipids with Global Selectivity to Basic Taste Qualities and High Correlation to Sensory Scores

Effective R&D and strict quality control of a broad range of foods, beverages, and pharmaceutical products require objective taste evaluation. Advanced taste sensors using artificial-lipid membranes have been developed based on concepts of global selectivity and high correlation with human sensory score. These sensors respond similarly to similar basic tastes, which they quantify with high correlations to sensory score. Using these unique properties, these sensors can quantify the basic tastes of saltiness, sourness, bitterness, umami, astringency and richness without multivariate analysis or artificial neural networks. This review describes all aspects of these taste sensors based on artificial lipid, ranging from the response principle and optimal design methods to applications in the food, beverage, and pharmaceutical markets

Modeling Low Muscle Mass Screening in Hemodialysis Patients

Introduction: Computed tomography (CT) can accurately measure muscle mass, which is necessary for diagnosing sarcopenia, even in dialysis patients. However, CT-based screening for such patients is challenging, especially considering the availability of equipment within dialysis facilities. We therefore aimed to develop a bedside prediction model for low muscle mass, defined by the psoas muscle mass index (PMI) from CT measurement. Methods: Hemodialysis patients (n = 619) who had undergone abdominal CT screening were divided into the development (n = 441) and validation (n = 178) groups. PMI was manually measured using abdominal CT images to diagnose low muscle mass by two independent investigators. The development group’s data were used to create a logistic regression model using 42 items extracted from clinical information as predictive variables; variables were selected using the stepwise method. External validity was examined using the validation group’s data, and the area under the curve (AUC), sensitivity, and specificity were calculated. Results: Of all subjects, 226 (37%) were diagnosed with low muscle mass using PMI. A predictive model for low muscle mass was calculated using ten variables: each grip strength, sex, height, dry weight, primary cause of end-stage renal disease, diastolic blood pressure at start of session, pre-dialysis potassium and albumin level, and dialysis water removal in a session. The development group’s adjusted AUC, sensitivity, and specificity were 0.81, 60%, and 87%, respectively. The validation group’s adjusted AUC, sensitivity, and specificity were 0.73, 64%, and 82%, respectively. Discussion/Conclusion: Our results facilitate skeletal muscle screening in hemodialysis patients, assisting in sarcopenia prophylaxis and intervention decisions