This study has implications for the development of intervention programs aiming at increasing occupational well-being in educational settings.Sustainable oral care of the elderly requires a holistic view of aging, which must extend far beyond the narrow field of dental expertise to help reduce the effects of sociobiological changes on oral health in good time. Digital technologies now extend into all aspects of daily life. This review summarizes the diverse digital opportunities that may help address the complex challenges in Gerodontology. Systemic patient management is at the center of these descriptions, while the application of digital tools for purely dental treatment protocols is deliberately avoided.The present study aimed to evaluate the effect of the most common antidepressants on aquatic protozoa. Spirostomum ambiguum was used as the model protozoan. The biological activity of four antidepressants, namely fluoxetine, sertraline, paroxetine, and mianserin, toward S. ambiguum was evaluated. Sertraline was found to be the most toxic drug with EC50 values of 0.2 to 0.7 mg/L. The toxicity of the antidepressants depended on the pH of the medium and was the highest in alkaline conditions. Sertraline was also the most bioaccumulating compound tested, followed by mianserin. Slow depuration was observed after transferring the protozoa from the drug solutions to a fresh medium, which indicated possible lysosomotropism of the tested antidepressants in the protozoa.  https://www.selleckchem.com/products/PD-0332991.html  The biotransformation products were identified using a high-resolution mass spectrometer after two days of incubation of the protozoa with the tested antidepressants. Four to six potential biotransformation products were observed in the aqueous phase, while no metabolites were detected in the protozoan cells. Because of the low abundance of metabolites in the medium, their structure was not determined.Raman mapping is becoming a very useful tool in investigating cells and cellular components, as well as bioactive molecules intracellularly. In this study, we have encapsulated beta-carotene using a layer-by-layer technique, as a way to enhance its stability and bioavailability. Further, we have used Raman mapping to characterize the as-obtained capsules and monitor their uptake by the human retinal epithelial D407 cells. We were able to successfully map the beta-carotene distribution inside the capsules, to localize the capsules intracellularly, and distinguish between capsules and other cellular components.Diabetic nephropathy (DN) is a major microvascular complication of diabetes. Obesity and hyperlipidemia, fueled by unhealthy food habits, are risk factors to glomerular filtration rate (GFR) decline and DN progression. Several studies recommend that diabetic patients should be screened early (in prediabetes) for kidney disease, in order to prevent advanced stages, for whom the current interventions are clearly inefficient. This ambition greatly depends on the existence of accurate early biomarkers and novel molecular targets, which only may arise with a more thorough knowledge of disease pathophysiology. We used a rat model of prediabetes induced by 23 weeks of high-sugar/high-fat (HSuHF) diet to characterize the phenotype of early renal dysfunction and injury. When compared with the control animals, HSuHF-treated rats displayed a metabolic phenotype compatible with obese prediabetes, displaying impaired glucose tolerance and insulin sensitivity, along with hypertriglyceridemia, and lipid peroxidation. Despite unchanged creatinine levels, the prediabetic animals presented glomerular crescent-like lesions, accompanied by increased kidney Oil-Red-O staining, triglycerides content and mRNA expression of IL-6 and iNOS. This model of HSuHF-induced prediabetes can be a useful tool to study early features of DN, namely crescent-like lesions, an early signature that deserves in-depth elucidation.The importance of the monitoring of thickness and rate deposition is indispensable for the fabrication of thin film sensors, such as SPR sensors. The sensitivity of SPR responses varies with the thickness of the film, as well as the linear range. Thus, in the present work, we presented an experimental study of the plasmonic response of Cr/Au thin films deposited onto glass slides by evaporation, based on both a rotation and no-rotation system. The results show that the thickness of the gold film varies from 240 to 620 Å, depending on the glass slide position. The SPR response curves obtained experimentally were compared with simulated plasmonic responses and different parameters such as resonance angle, and the depth, slope and half-width of the SPR curve were analysed.Global Navigation Satellite System (GNSS) meaconing and spoofing are being considered as the key threats to the Safety-of-Life (SoL) applications that mostly rely upon the use of open service (OS) signals without signal or data-level protection. While a number of pre and post correlation techniques have been proposed so far, possible utilization of the supervised machine learning algorithms to detect GNSS meaconing and spoofing is currently being examined. One of the supervised machine learning algorithms, the Support Vector Machine classification (C-SVM), is proposed for utilization at the GNSS receiver level due to fact that at that stage of signal processing, a number of measurements and observables exists. It is possible to establish the correlation pattern among those GNSS measurements and observables and monitor it with use of the C-SVM classification, the results of which we present in this paper. By adding the real-world spoofing and meaconing datasets to the laboratory-generated spoofing datasets at the training stage of the C-SVM, we complement the experiments and results obtained in Part I of this paper, where the training was conducted solely with the use of laboratory-generated spoofing datasets. In two experiments presented in this paper, the C-SVM algorithm was cross-fed with the real-world meaconing and spoofing datasets, such that the meaconing addition to the training was validated by the spoofing dataset, and vice versa. The comparative analysis of all four experiments presented in this paper shows promising results in two aspects (i) the added value of the training dataset enrichment seems to be relevant for real-world GNSS signal manipulation attempt detection and (ii) the C-SVM-based approach seems to be promising for GNSS signal manipulation attempt detection, as well as in the context of potential federated learning applications.