Gormanharder3025

The possibility of measuring in real time the different types of analytes present in food is becoming a requirement in food industry. In this context, biosensors are presented as an alternative to traditional analytical methodologies due to their specificity, high sensitivity and ability to work in real time. It has been observed that the behavior of the analysis curves of the biosensors follow a trend that is reproducible among all the measurements and that is specific to the reaction that occurs in the electrochemical cell and the analyte being analyzed. Kinetic reaction modeling is a widely used method to model processes that occur within the sensors, and this leads to the idea that a mathematical approximation can mimic the electrochemical reaction that takes place while the analysis of the sample is ongoing. For this purpose, a novel mathematical model is proposed to approximate the enzymatic reaction within the biosensor in real time, so the output of the measurement can be estimated in advance. The proposed model is based on adjusting an exponential decay model to the response of the biosensors using a nonlinear least-square method to minimize the error. The obtained results show that our proposed approach is capable of reducing about 40% the required measurement time in the sample analysis phase, while keeping the error rate low enough to meet the accuracy standards of the food industry.The emergence of highly effective CFTR modulator therapy has led to significant improvements in health care for most patients with cystic fibrosis (CF). For some, however, these therapies remain inaccessible due to the rarity of their individual CFTR variants, or due to a lack of biologic activity of the available therapies for certain variants. One proposed method of addressing this gap is the use of primary human cell-based models, which allow preclinical therapeutic testing and physiologic assessment of relevant tissue at the individual level. Nasal cells represent one such tissue source and have emerged as a powerful model for individual disease study. The ex vivo culture of nasal cells has evolved over time, and modern nasal cell models are beginning to be utilized to predict patient outcomes. This review will discuss both historical and current state-of-the art use of nasal cells for study in CF, with a particular focus on the use of such models to inform personalized patient care.The notion of corporate social responsibility (CSR) has been around for many decades. However, even in 2021, its spectrum is still evolving. Several studies addressed CSR for realizing different organizational outcomes. However, its significance in achieving employee-related consequences is relatively new to the literature. In the same manner, it is not clear from existing literature how ethical leaders can impact their followers' CSR-related behavior, for example, employee pro-environmental behavior (EPB). With this background, the current study aims to explore the relationship of CSR at the employee level (CSR-E) with EPB through the mediating effect of ethical leadership (ELS) in the healthcare sector of a developing economy. This study also proposes a conditional indirect effect of quality of work-life (QWL) in this relationship. The data for the current study were obtained from different hospitals located in a large city through a self-administered questionnaire. The data were examined through the structural equation modeling (SEM) technique. The results validated that CSR-E positively influences EPB, and ELS partially mediates this relationship. Furthermore, the results also confirmed the presence of the conditional indirect effect of QWL in the proposed relationship of the current study. These findings will be helpful for healthcare policymakers to enhance the pro-environmental behavior of employees at the workplace through CSR-E and ELS. These results will also be helpful in reducing the overall environmental footprint of a hospital.The development of drug resistance is one of the main causes of cancer treatment failure. This phenomenon occurs very frequently in different types of cancer, including colon and pancreatic cancers. However, the underlying molecular mechanisms are not fully understood. In recent years, nanomedicine has improved the delivery and efficacy of drugs, and has decreased their side effects. In addition, it has allowed to design drugs capable of avoiding certain resistance mechanisms of tumors. In this article, we review the main resistance mechanisms in colon and pancreatic cancers, along with the most relevant strategies offered by nanodrugs to overcome this obstacle. These strategies include the inhibition of efflux pumps, the use of specific targets, the development of nanomedicines affecting the environment of cancer-specific tissues, the modulation of DNA repair mechanisms or RNA (miRNA), and specific approaches to damage cancer stem cells, among others. This review aims to illustrate how advanced nanoformulations, including polymeric conjugates, micelles, dendrimers, liposomes, metallic and carbon-based nanoparticles, are allowing to overcome one of the main limitations in the treatment of colon and pancreatic cancers. The future development of nanomedicine opens new horizons for cancer treatment.The decomposition effect of variational mode decomposition (VMD) mainly depends on the choice of decomposition number K and penalty factor α. For the selection of two parameters, the empirical method and single objective optimization method are usually used, but the aforementioned methods often have limitations and cannot achieve the optimal effects. see more Therefore, a multi-objective multi-island genetic algorithm (MIGA) is proposed to optimize the parameters of VMD and apply it to feature extraction of bearing fault. First, the envelope entropy (Ee) can reflect the sparsity of the signal, and Renyi entropy (Re) can reflect the energy aggregation degree of the time-frequency distribution of the signal. Therefore, Ee and Re are selected as fitness functions, and the optimal solution of VMD parameters is obtained by the MIGA algorithm. Second, the improved VMD algorithm is used to decompose the bearing fault signal, and then two intrinsic mode functions (IMF) with the most fault information are selected by improved kurtosis and Holder coefficient for reconstruction.