The mechanisms of action of silver nanoparticles (AgNPs) in monogenean parasites of the genus Cichlidogyrus were investigated through a microarray hybridization approach using genomic information from the nematode Caenorhabditis elegans. The effects of two concentrations of AgNPs were explored, low (6 µg/L Ag) and high (36 µg/L Ag). Microarray analysis revealed that both concentrations of AgNPs activated similar biological processes, although by different mechanisms. Expression profiles included genes involved in detoxification, neurotoxicity, modulation of cell signaling, reproduction, embryonic development, and tegument organization as the main biological processes dysregulated by AgNPs. Two important processes (DNA damage and cell death) were mostly activated in parasites exposed to the lower concentration of AgNPs. To our knowledge, this is the first study providing information on the sub-cellular and molecular effects of exposure to AgNPs in metazoan parasites of fish.The prevalence of childhood and adolescence overweight an obesity is raising at an alarming rate in many countries. This poses a serious threat to the current and near-future health systems, given the association of these conditions with different comorbidities (cardiovascular diseases, type II diabetes, and metabolic syndrome) and even death. In order to design appropriate strategies for its prevention, as well as understand its origins, the development of predictive models for childhood/adolescent overweight/obesity and related outcomes is of extreme value. Obesity has a complex etiology, and in the case of childhood and adolescence obesity, this etiology includes also specific factors like (pre)-gestational ones; weaning; and the huge anthropometric, metabolic, and hormonal changes that during this period the body suffers. In this way, Machine Learning models are becoming extremely useful tools in this area, given their excellent predictive power; ability to model complex, nonlinear relationships between variables; and capacity to deal with high-dimensional data typical in this area. This is especially important given the recent appearance of large repositories of Electronic Health Records (EHR) that allow the development of models using datasets with many instances and predictor variables, from which Deep Learning variants can generate extremely accurate predictions. In the current work, the area of Machine Learning models to predict childhood and adolescent obesity and related outcomes is comprehensively and critically reviewed, including the latest ones using Deep Learning with EHR. These models are compared with the traditional statistical ones that used mainly logistic regression. The main features and applications appearing from these models are described, and the future opportunities are discussed.Chrysin is a bioflavonoid that can be found in natural products such as honey and propolis, and it possesses several biological effects such as antioxidant, anti-inflammatory, and anti-cancer activity. However, it is poorly soluble in water, and its bioavailability is limited. The aim of this research is to investigate the chrysin solubilization capacity of different β-cylcodextrin derivatives and compare their biological activities. Chrysin was complexed with β-cyclodextrin (βCD), hydroxypropyl-β-, (HPBCD) sulfobutylether-β-, (SBECD), and randomly-methylated-β-cyclodextrin (RAMEB) by the lyophilization method in 11 and 12 molar ratios. The solubilities of the chrysin-cyclodextrin complexes were tested, and the solubilization abilities of cyclodextrins were studied by phase solubility experiments. The cytotoxicity of the complexes was measured by the MTT method, and the permeability enhancement was tested on Caco-2 monolayers. The solubility study showed that the complexes formed with RAMEB had the highest solubility in water. The phase solubility experiments confirmed the strongest interaction between RAMEB and chrysin. In the viability test, none of the complexes showed cytotoxicity up to 100 µM concentration. The permeability study revealed that both at 11 and 12 ratios, the RAMEB complexes were the most effective to enhance chrysin permeability through the Caco-2 monolayers. In conclusion, cyclodextrins, especially RAMEB, are suitable for improving chrysin solubility and absorption.Extramammary Paget’s disease (EMPD) is a rare skin cancer arising in the anogenital area. Most EMPD tumors remain dormant as in situ lesions, but the outcomes of patients with metastatic EMPD are poor because of the lack of effective systemic therapies. Nectin cell adhesion molecule 4 (NECTIN4) has attracted attention as a potential therapeutic target for some cancers. Urothelial cancer is one such cancer, and clinical trials of enfortumab vedotin, a drug-conjugated anti-NECTIN4 antibody, are ongoing. However, little is known regarding the role of NECTIN4 in EMPD. In this study, we conducted immunohistochemical analysis of NECTIN4 expression in 110 clinical EMPD samples and normal skin tissue. In normal skin, positive signals were observed in epidermal keratinocytes (particularly in the lower part of the epidermis), eccrine and apocrine sweat glands, inner and outer root sheaths, and matrix of the hair follicles. The most EMPD lesions exhibited strong NECTIN4 expression, and high NECTIN4 expression was significantly associated with increased tumor thickness, advanced TNM stage, and worse disease-specific survival. These results support the potential use of NECTIN4-targeted therapy for EMPD. Our report contributes to the better understanding of the pathobiology of NECTIN4 in the skin and the skin-related adverse effects of NECTIN4-targeted therapy.This paper describes a virtual instrument capable of the automatic and quasi-instantaneous classification of a vehicle according to category when it is driving along the road. The vehicle’s classification is based on accurate measurements of both the vehicle’s speed and its wheelbase. GS-0976 inhibitor Our research is focused on achieving accurate speed and wheelbase measurements and then determining the category of the vehicle through the developed software. The vehicle categorization is based on the wheelbase measurements and the number of axles and metal masses of the vehicle. The system has a complementary magnetic sensor, which helps in classifying the vehicle when the wheelbase measurement could be representative of different categories, and a camera to confirm the results of the experiment. The proposed measurement system presents a novel method for classifying vehicles according to type using piezoelectric transducers (piezo sensors). In addition, no system references have been found that encompass the functionalities of the presented system based on the information of only two piezoelectric transducers.