Abstract

Abstract
This study aimed to assess the effectiveness of a structured intervention on the frequency of self-care behaviors with arteriovenous fistula (AVF) by patients on hemodialysis. This is a quasi-experimental study with pre- and post-measurements. Participants were assigned to an intervention group (IG) (n = 48) or to a control group (CG) (n = 41). IG patients were subject to a structured intervention on self-care with AVF (SISC-AVF) consisting of both a theoretical and a practical part. After SISC-AVF application, patients in the IG showed better overall self-care behaviors with AVF than patients in the CG (79.2% and 91.4%, respectively, p < .001) as well as better self-care concerning both the management of signs and symptoms (90.1% and 94.4% respectively, p = .004) and the prevention of complications (72.7% and 89.5%, respectively, p < .001). The study results suggest that the SISC-AVF had positive effects on patients in the IG.

Abstract

Abstract
One of the Industry 4.0 landmarks, concerns the optimization of manufacturing processes by increasing the operator's productivity. But productivity is highly affected by the operator's emotions. Positive emotions (e.g. happiness) are positively related to productivity, in contrast negative emotions (e.g. frustration) are negative related to productivity and positive related to misconducts and misbehaviors on the workplace. Thus perhaps, automatic recommendation systems can suggest actions or instructions to eliminate or attenuate undesired negative emotions on the workplace. These systems might support their actions based on the reliability of emotion detectors. In this paper, emotions are detected thought a speech system. Our solution was built over deep speech recognition layers, namely the first two convolutional layers of the pre-trained 2015 Baidu's speech recognition model. In re-utilizing these first two convolutional layers, robust meta-features are expected to be extracted. Our deep learning model attempts to predict the seven primary emotions on the MELD test set.Furthermore, our solution did not use any contextual data and yet it achieved robust results. The proposed weighted TrBaidu algorithm achieved state-of-art results on the detection of joy and surprise emotions, a F1-score rate of 23 % for both emotions.

Abstract

Abstract
This work reports the development of a simple and rapid electrochemical immunosensor for the determination of breast cancer biomarker Cancer Antigen 15-3 (CA 15-3). Disposable and cost-effective chips, consisting of gold screen-printed electrodes (AuSPEs), were used to develop the portable electrochemical devices for monitoring the biomarker in point-of-care (PoC), under clinical context. The biosensor preparation consisted of two simple steps. First, a self-assembled monolayer (SAM) of mercaptosuccinic acid (MSA) was formed at the AuSPE surface. Then, the CA 15-3 antibody was covalently bound to the carboxylic groups standing at the electrode surface using EDC/NHS chemistry. The performance of the developed immunosensor was evaluated by assessing the sensor sensitivity, linear response interval, selectivity and detection limit (LOD). The developed immunosensor provided a wide linear concentration range (from 1.0 to 1000 U mL(-1)) and low detection levels were achieved (LOD of 0.95 U mL(-1)), enabling the sensitive detection of the cancer biomarker at clinically relevant levels, using square wave voltammetry (SWV) as electroanalytical technique. Moreover, selectivity studies performed against other cancer biomarkers (CA 125 and CA 19-9) revealed that the antibody has high selectivity for CA 15-3 antigen. The immunosensor was applied to the quantification of CA 15-3 in artificial serum samples with satisfactory results.