Problematic pornography use and mental health: a systematic review

The present review aimed to synthesize the empirical evidence regarding the association between problematic pornography use (PPU) and mental health. A comprehensive literature search using keywords and subject headings was performed with three electronic databases, resulting in 20 studies that met the inclusion criteria. The patterns of association between PPU and mental health were examined, and the limitations of these studies were discussed. The overall findings suggest the relationship between PPU and mental health outcomes is not clear-cut, and it is often mediated by other factors such as loneliness, anxiety, and self-esteem. Further studies are required to evaluate the prevalence of PPU and both risk and protective factors which are associated with exposure to online pornography. Most studies relied on homogenous samples which have limited the generalizability of findings. The use of representative samples, including both males and females with different sexual orientations and from diverse cultural and ethnic backgrounds, would strengthen our understanding of PPU and go further to expound on its controversies. Clinical recommendations and future directions are also discussed

Early maladaptive schemas and behavioural addictions: a systematic literature review

As observed in other mental health difficulties, behavioural addiction is a complex construct with several potential predisposing factors, which include biological factors (e.g., genetic predispositions), psychological factors (e.g., personality traits), and social factors (e.g., family, and social history). One factor that may play a significant role in both developing and perpetuating behavioural addiction is the activation of early maladaptive schemas (EMSs). The aim of the present review was to synthesize the evidence concerning the relationship between behavioural addiction and EMSs. A comprehensive literature search using keywords and subject headings was performed with three electronic databases, resulting in 20 studies that met the inclusion criteria. In relation to specific behavioural addiction, the 20 studies examined: binge-eating/food addiction (n = 6), sexual addiction/compulsive sexual behaviours (n = 3), multiple addictive behaviours (n = 2), internet addiction (n = 2), smartphone addiction (n = 2), social networking/Facebook addiction (n = 2), exercise dependence (n = 1), gambling (n = 1), and videogame addiction (n = 1). The patterns of association between EMS and behavioural addiction were examined in both clinical and non-clinical population. The ‘Disconnection and Rejection’ domain was the most strongly related schema domain across all addictive behaviours, followed by ‘Impaired Limits’. The present review suggests a positive relationship between schema activation and several addictive behaviours, including addictions to gambling, gaming, social media use sex, exercise, and food. The clinical implications of the findings are discussed, but further research is needed to inform treatment plans and interventions for those who struggle with behavioural addictions

Classifying Soil Type Using Radar Satellite Images

The growth of the crop is dependent on soil type, apart from atmospheric and geo-location characteristics. As of now, there is no direct and cost free method to measure soil property or to classify soil type. In this work, we proposed a machine learning model to classify soil type using Sentinel-1 satellite radar images. Further, the developed classifier achieved 72.17% F1-score classifying sandy, free and clayish on a set of 65003 data points collected over one year (from Oct 2018 to Sep 2019) over 14 corn parcels near Ourique, Portugal

Testing the robustness of attribution methods for convolutional neural networks in MRI-based Alzheimer's disease classification

Attribution methods are an easy to use tool for investigating and validating machine learning models. Multiple methods have been suggested in the literature and it is not yet clear which method is most suitable for a given task. In this study, we tested the robustness of four attribution methods, namely gradient*input, guided backpropagation, layer-wise relevance propagation and occlusion, for the task of Alzheimer's disease classification. We have repeatedly trained a convolutional neural network (CNN) with identical training settings in order to separate structural MRI data of patients with Alzheimer's disease and healthy controls. Afterwards, we produced attribution maps for each subject in the test data and quantitatively compared them across models and attribution methods. We show that visual comparison is not sufficient and that some widely used attribution methods produce highly inconsistent outcomes

Classifying Soil Type Using Radar Satellite Images

The growth of the crop is dependent on soil type, apart from atmospheric and geo-location characteristics. As of now, there is no direct and costfree method to measure soil property or to classify soil type. In this work, we proposed a machine learning model to classify soil type using Sentinel-1 satellite radar images. Further, the developed classifier achieved 72.17% F1-score classifying sandy, free and clayish on a set of 65003 data points collected over one year (from Oct 2018 to Sep 2019) over 14 corn parcels near Ourique, Portugal

Synchronization modulation increases transepithelial potentials in MDCK monolayers through Na/K pumps

Peer reviewedPublisher PD

Clinical oxidative stress during leprosy multidrug therapy:impact of dapsone oxidation

This study aims to assess the oxidative stress in leprosy patients under multidrug therapy (MDT; dapsone, clofazimine and rifampicin), evaluating the nitric oxide (NO) concentration, catalase (CAT) and superoxide dismutase (SOD) activities, glutathione (GSH) levels, total antioxidant capacity, lipid peroxidation, and methemoglobin formation. For this, we analyzed 23 leprosy patients and 20 healthy individuals from the Amazon region, Brazil, aged between 20 and 45 years. Blood sampling enabled the evaluation of leprosy patients prior to starting multidrug therapy (called MDT 0) and until the third month of multidrug therapy (MDT 3). With regard to dapsone (DDS) plasma levels, we showed that there was no statistical difference in drug plasma levels between multibacillary (0.518±0.029 μg/mL) and paucibacillary (0.662±0.123 μg/mL) patients. The methemoglobin levels and numbers of Heinz bodies were significantly enhanced after the third MDTsupervised dose, but this treatment did not significantly change the lipid peroxidation and NO levels in these leprosy patients. In addition, CAT activity was significantly reduced in MDT-treated leprosy patients, while GSH content was increased in these patients. However, SOD and Trolox equivalent antioxidant capacity levels were similar in patients with and without treatment. These data suggest that MDT can reduce the activity of some antioxidant enzyme and influence ROS accumulation, which may induce hematological changes, such as methemoglobinemia in patients with leprosy. We also explored some redox mechanisms associated with DDS and its main oxidative metabolite DDS-NHOH and we explored the possible binding of DDS to the active site of CYP2C19 with the aid of molecular modeling software