In this paper, the washability of wearable textronic (textile-electronic) devices has been studied. Two different approaches aiming at designing, producing, and testing robust washable and reliable smart textile systems are presented. The common point of the two approaches is the use of flexible conductive PCB in order to interface the miniaturized rigid (traditional) electronic devices to conductive threads and tracks within the textile flexible fabric and to connect them to antenna, textile electrodes, sensors, actuators, etc. The first approach consists in the use of TPU films (thermoplastic polyurethane) that are deposited by the press under controlled temperature and pressure parameters in order to protect the conductive thread and electrical contacts. The washability of conductive threads and contact resistances between flexible PCB and conductive threads are tested. The second approach is focused on the protection of the whole system—composed of a rigid electronic device, flexible PCB, and textile substrate—by a barrier made of latex. Three types of prototypes were realized and washed. Their reliabilities are studied.

The needs for light-weight and soft smart clothing in homecare have been rising since the past decade. Many smart textile sensors have been developed and applied to automatic physiological and user-centered environmental status recognition. In the present study, we propose wearable multi-sensor smart clothing for homecare monitoring based on an economic fabric electrode with high elasticity and low resistance. The wearable smart clothing integrated with heterogeneous sensors is capable to measure multiple human biosignals (ECG and respiration), acceleration, and gyro information. Five independent respiratory signals (electric impedance plethysmography, respiratory induced frequency variation, respiratory induced amplitude variation, respiratory induced intensity variation, and respiratory induced movement variation) are obtained. The smart clothing can provide accurate respiratory rate estimation by using three different techniques (Naïve Bayes inference, static Kalman filter, and dynamic Kalman filter). During the static sitting experiments, respiratory induced frequency variation has the best performance; whereas during the running experiments, respiratory induced amplitude variation has the best performance. The Naïve Bayes inference and dynamic Kalman filter have shown good results. The novel smart clothing is soft, elastic, and washable and it is suitable for long-term monitoring in homecare medical service and healthcare industry.

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Background: The factors affecting hypertrophy of the future liver remnant (FLR) after portal vein embolization (PVE) remain unclear. The aim of this study was to clarify the clinical factors affecting the hypertrophy rate after PVE and to develop a scoring system predicting insufficient liver hypertrophy. Methods: A total of 152 patients who underwent PVE of the right portal branch between 2006 and 2016 were reviewed retrospectively. The score to predict insufficient (<25%) hypertrophy was established based on logistic regression analyses of the clinical parameters before PVE. Results: After PVE, the FLR volume significantly increased from 364 (151e801) ml, 33.3% (17.9%e 53.8%) to 451 (242e866) ml, 41.9% (26.1%e65.1%). The median hypertrophy rate was 23.9% (À5.1%e 95.5%). A preoperative predictive scoring system for insufficient liver hypertrophy was constructed using the following 4 factors; an initial FLR volume !35% (2 points), alkaline phosphatase !450 IU/dL (1 point), cholinesterase <220 mg/dL (1 point), and the indocyanine green disappearance rate <0.16 (1 point). The constructed scoring system showed the proportion of patients with insufficient liver hypertrophy (<25%) to be 1/22 (4.5%) in the low-score group (0 point), 33/75 (44.0%) in the medium-score group (1e2 points), and 46/ 55 (83.6%) in the high-score group (3e5 points). The hypertrophy rate of FLR was significantly different among the 3 groups (low-score group, 40.1% [2.1e 73.2%]; medium-score group, 26.5% [À5.1e81.4%]; high-score group, 20.1% [1.1e49.8%]) (P < 0.001). Conclusion: The constructed scoring system was able to stratify patients before PVE according to the possibility of developing insufficient liver hypertrophy. P 5.

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