Medicinal Chemistry approach, pharmacology and neuroprotective benefits of CB2R modulators in neurodegenerative diseases

In the last decades, cannabinoid receptor 2 (CB2R) has continued to receive attention as a key therapeutic target in neuroprotection. Indeed, several findings highlight the neuroprotective effects of CB2R through suppression of both neuronal excitability and reactive microglia. Additionally, CB2R seems to be a more promising target than cannabinoid receptor 1 (CB1R) thanks to the lack of central side effects, its lower expression levels in the central nervous system (CNS), and its inducibility, since its expression enhances quickly in the brain following pathological conditions. This review aims to provide a thorough overview of the main natural and synthetic selective CB2R modulators, their chemical classification and their potential therapeutic usefulness in neuroprotection, a crucial aspect for the treatment of neurodegenerative diseases

When Do People Trust Their Social Groups?

Trust facilitates cooperation and supports positive outcomes in social groups, including member satisfaction, information sharing, and task performance. Extensive prior research has examined individuals' general propensity to trust, as well as the factors that contribute to their trust in specific groups. Here, we build on past work to present a comprehensive framework for predicting trust in groups. By surveying 6,383 Facebook Groups users about their trust attitudes and examining aggregated behavioral and demographic data for these individuals, we show that (1) an individual's propensity to trust is associated with how they trust their groups, (2) smaller, closed, older, more exclusive, or more homogeneous groups are trusted more, and (3) a group's overall friendship-network structure and an individual's position within that structure can also predict trust. Last, we demonstrate how group trust predicts outcomes at both individual and group level such as the formation of new friendship ties.Comment: CHI 201

Epigenetic mechanisms of endocrine-disrupting chemicals in obesity

The incidence of obesity has dramatically increased over the last decades. Recently, there has been a growing interest in the possible association between the pandemics of obesity and some endocrine-disrupting chemicals (EDCs), termed “obesogens”. These are a heterogeneous group of exogenous compounds that can interfere in the endocrine regulation of energy metabolism and adipose tissue structure. Oral intake, inhalation, and dermal absorption represent the major sources of human exposure to these EDCs. Recently, epigenetic changes such as the methylation of cytosine residues on DNA, post-translational modification of histones, and microRNA expression have been considered to act as an intermediary between deleterious effects of EDCs and obesity development in susceptible individuals. Specifically, EDCs exposure during early-life development can detrimentally affect individuals via inducing epigenetic modifications that can permanently change the epigenome in the germline, enabling changes to be transmitted to the next generations and predisposing them to a multitude of diseases. The purpose of this review is to analyze the epigenetic alterations putatively induced by chemical exposures and their ability to interfere with the control of energy metabolism and adipose tissue regulation, resulting in imbalances in the control of body weight, which can lead to obesity

Allosteric modulators targeting cannabinoid cb1 and cb2 receptors: Implications for drug discovery

Allosteric modulators of cannabinoid receptors hold great therapeutic potential, as they do not possess intrinsic efficacy, but instead enhance or diminish the receptor's response of orthosteric ligands allowing for the tempering of cannabinoid receptor signaling without the desensitization, tolerance and dependence. Allosteric modulators of cannabinoid receptors have numerous advantages over the orthosteric ligands such as higher receptor type selectivity, probe dependence and biased signaling, so they have a great potential to separate the therapeutic benefits from side effects own of orthosteric ligands. This review aims to give an overview of the CB1 and CB2 receptor allosteric modulators highlighting the structure-activity relationship and pharmacological profile of each classes, and their future promise

Predicting facebook users online privacy protection: Risk, trust, norm focus theory, and the theory of planned behavior

Journal ArticleThis is an Accepted Manuscript of an article published by Taylor & Francis in The Journal of Social Psychology on 21 April 2014, available online: http://wwww.tandfonline.com/10.1080/00224545.2014.914881The present research adopts an extended theory of the planned behavior model that included descriptive norms, risk, and trust to investigate online privacy protection in Facebook users. Facebook users (N = 119) completed a questionnaire assessing their attitude, subjective injunctive norm, subjective descriptive norm, perceived behavioral control, implicit perceived risk, trust of other Facebook users, and intentions toward protecting their privacy online. Behavior was measured indirectly 2 weeks after the study. The data show partial support for the theory of planned behavior and strong support for the independence of subjective injunctive and descriptive norms. Risk also uniquely predicted intentions over and above the theory of planned behavior, but there were no unique effects of trust on intentions, nor of risk or trust on behavior. Implications are discussed. © Copyright © Taylor & Francis Group, LLC

Thyroid nodules treated with percutaneous radiofrequency thermal ablation: a comparative study

Percutaneous radiofrequency thermal ablation (RTA) was reported as an effective tool for the management of thyroid nodules (TNs). The aim of this study was to investigate the effects of RTA and to establish whether they were treatment-related by comparison with a matched, untreated control group

Effectiveness of Lactobacillus-containing vaginal tablets in the treatment of symptomatic bacterial vaginosis

AbstractThe purpose of this study was to determine the effectiveness of Lactobacillus-containing vaginal tablets in the treatment of bacterial vaginosis (BV) and in the restoration of a healthy vaginal flora. Thirty-nine women with BV were enrolled in a double-blind, placebo-controlled clinical trial. Patients received either one Lactobacillus-containing tablet or placebo daily for 7 days. Clinical criteria, vaginal Gram stain scores and symptoms were compared with those at the initial visit and those at completion of therapy and 2 weeks later. After completion of therapy, all of the patients in the Lactobacillus-treated group (n = 18) were free of BV, showing a normal (83%) or intermediate (17%) vaginal flora, as compared with only two patients free of BV with intermediate flora (12%) from among the 16 placebo-treated women (p <0.001). Two weeks after completion of therapy, treatment was successful (score <7) in 61% of Lactobacillus-treated patients as compared with 19% of those in the placebo group (p <0.05). In the treatment group, the total number of symptomatic patients and the intensity of their symptoms, in particular vaginal malodour, were significantly reduced at both follow-up visits. The data indicate that intravaginal administration of exogenous selected strains of lactobacilli can restore a normal vaginal microbiota and be used in treating bacterial vaginosis

Thyroid disease treatment prediction with machine learning approaches

The thyroid is an endocrine gland located in the anterior region of the neck: its main task is to produce thyroid hormones, which are functional to our entire body. Its possible dysfunction can lead to the production of an insufficient or excessive amount of thyroid hormone. Therefore, the thyroid can become inflamed or swollen due to one or more swellings forming inside it. Some of these nodules can be the site of malignant tumors. One of the most used treatments is sodium levothyroxine, also known as LT4, a synthetic thyroid hormone used in the treatment of thyroid disorders and diseases. Predictions about the treatment can be important for supporting endocrinologists' activities and improve the quality of the patients' life. To date, there are numerous studies in the literature that focus on the prediction of thyroid diseases on the trend of the hormonal parameters of people. This work, differently, aims to predict the LT4 treatment trend for patients suffering from hypothyroidism. To this end, a dedicated dataset was built that includes medical information related to patients being treated in the”AOU Federico II” hospital of Naples. For each patient, the clinical history is available over time, and therefore on the basis of the trend of the hormonal parameters and other attributes considered it was possible to predict the course of each patient's treatment in order to understand if this should be increased or decreased. To conduct this study, we used different machine learning algorithms. In particular, we compared the results of 10 different classifiers. The performances of the different algorithms show good results, especially in the case of the Extra-Tree Classifier, where the accuracy reaches 84%