Cannabinoids in the treatment of epilepsy: current status and future prospects

Cannabidiol (CBD) is one of the prominent phytocannabinoids found in Cannabis sativa, differentiating from Δ9-tetrahydrocannabinol (THC) for its non-intoxicating profile and its antianxiety/antipsychotic effects. CBD is a multi-target drug whose anti-convulsant properties are supposed to be independent of endocannabinoid receptor CB1 and might be related to several underlying mechanisms, such as antagonism on the orphan GPR55 receptor, regulation of adenosine tone, activation of 5HT1A receptors and modulation of calcium intracellular levels. CBD is a lipophilic compound with low oral bioavailability (6%) due to poor intestinal absorption and high first-pass metabolism. Its exposure parameters are greatly influenced by feeding status (ie, high fatcontaining meals). It is mainly metabolized by cytochrome P 450 (CYP) 3A4 and 2C19, which it strongly inhibits. A proprietary formulation of highly purified, plant-derived CBD has been recently licensed as an adjunctive treatment for Dravet syndrome (DS) and Lennox-Gastaut syndrome (LGS), while it is being currently investigated in tuberous sclerosis complex. The regulatory agencies’ approval was granted based on four pivotal double-blind, placebocontrolled, randomized clinical trials (RCTs) on overall 154 DS patients and 396 LGS ones, receiving CBD 10 or 20 mg/kg/day BID as active treatment. The primary endpoint (reduction in monthly seizure frequency) was met by both CBD doses. Most patients reported adverse events (AEs), generally from mild to moderate and transient, which mainly consisted of somnolence, sedation, decreased appetite, diarrhea and elevation in aminotransferase levels, the last being documented only in subjects on concomitant valproate therapy. The interaction between CBD and clobazam, likely due to CYP2C19 inhibition, might contribute to some AEs, especially somnolence, but also to CBD clinical effectiveness. Cannabidivarin (CBDV), the propyl analogue of CBD, showed anti-convulsant properties in pre-clinical studies, but a plant-derived, purified proprietary formulation of CBDV recently failed the Phase II RCT in patients with uncontrolled focal seizures

Achieving descriptive accuracy in explanations via argumentation: the case of probabilistic classifiers

The pursuit of trust in and fairness of AI systems in order to enable human-centric goals has been gathering pace of late, often supported by the use of explanations for the outputs of these systems. Several properties of explanations have been highlighted as critical for achieving trustworthy and fair AI systems, but one that has thus far been overlooked is that of descriptive accuracy (DA), i.e., that the explanation contents are in correspondence with the internal working of the explained system. Indeed, the violation of this core property would lead to the paradoxical situation of systems producing explanations which are not suitably related to how the system actually works: clearly this may hinder user trust. Further, if explanations violate DA then they can be deceitful, resulting in an unfair behavior toward the users. Crucial as the DA property appears to be, it has been somehow overlooked in the XAI literature to date. To address this problem, we consider the questions of formalizing DA and of analyzing its satisfaction by explanation methods. We provide formal definitions of naive, structural and dialectical DA, using the family of probabilistic classifiers as the context for our analysis. We evaluate the satisfaction of our given notions of DA by several explanation methods, amounting to two popular feature-attribution methods from the literature, variants thereof and a novel form of explanation that we propose. We conduct experiments with a varied selection of concrete probabilistic classifiers and highlight the importance, with a user study, of our most demanding notion of dialectical DA, which our novel method satisfies by design and others may violate. We thus demonstrate how DA could be a critical component in achieving trustworthy and fair systems, in line with the principles of human-centric AI

Reference Profile Correlation Reveals Estrogen-like Trancriptional Activity of Curcumin

Background: Several secondary metabolites from herbal nutrient products act as weak estrogens (phytoestrogens), competing with endogenous estrogen for binding to the estrogen receptors and inhibiting steroid converting enzymes. However, it is still unclear whether these compounds elicit estrogen dependent transcription of genes at physiological concentrations. Methods: We compare the effects of physiological concentrations (100 nM) of the two phytoestrogens Enterolactone and Quercetin and the suspected phytoestrogen Curcumin on gene expression in the breast cancer cell line MCF7 with the effects elicited by 17-beta-estradiol (E2). Results: All three phytocompounds have weak effects on gene transcription; most of the E2 genes respond to the phytoestrogens in the same direction though to a much lesser extent and in the order Curcumin > Quercetin > Enterolactone. Gene regulation induced by these compounds was low for genes strongly induced by E2 and similar to the latter for genes only weakly regulated by the classic estrogen. Of interest with regard to the treatment of menopausal symptoms, the survival factor Birc5/survivin and the oncogene MYBL1 are strongly induced by E2 but only marginally by phytoestrogens. Conclusion: This approach demonstrates estrogenic effects of putative phytoestrogens at physiological concentrations and shows, for the first time, estrogenic effects of Curcumin. Copyright (C) 2010 S. Karger AG, Base

Explaining classifiers’ outputs with causal models and argumentation

We introduce a conceptualisation for generating argumentation frameworks (AFs) from causal models for the purpose of forging explanations for mod-els’ outputs. The conceptualisation is based on reinterpreting properties of semantics of AFs as explanation moulds, which are means for characterising argumentative relations. We demonstrate our methodology by reinterpreting the property of bi-variate reinforcement in bipolar AFs, showing how the ex-tracted bipolar AFs may be used as relation-based explanations for the outputs of causal models. We then evaluate our method empirically when the causal models represent (Bayesian and neural network) machine learning models for classification. The results show advantages over a popular approach from the literature, both in highlighting specific relationships between feature and classification variables and in generating counterfactual explanations with respect to a commonly used metric

Kinetics of the thermal degradation of Erica arborea by DSC: Hybrid kinetic method

The scope of this work was the determination of kinetic parameters of the thermal oxidative degradation of a Mediterranean scrub using a hybrid method developed at the laboratory. DSC and TGA were used in this study under air sweeping to record oxidative reactions. Two dominating and overlapped exothermic peaks were recorded in DSC and individualized using an experimental and numerical separation. This first stage allowed obtaining the enthalpy variation of each exothermic phenomenon. In a second time, a model free method was applied on each isolated curve to determine the apparent activation energies. A reactional kinetic scheme was proposed for the global exotherm composed of two independent and consecutive reactions. In fine mean values of enthalpy variation and apparent activation energy previously determined were injected in a model fitting method to obtain the reaction order and the preexponential factor of each oxidative reaction. We plan to use these data in a sub-model to be integrated in a wildland fire spread model

Argflow: a toolkit for deep argumentative explanations for neural networks

In recent years, machine learning (ML) models have been successfully applied in a variety of real-world applications. However, they are often complex and incomprehensible to human users. This can decrease trust in their outputs and render their usage in critical settings ethically problematic. As a result, several methods for explaining such ML models have been proposed recently, in particular for black-box models such as deep neural networks (NNs). Nevertheless, these methods predominantly explain outputs in terms of inputs, disregarding the inner workings of the ML model computing those outputs. We present Argflow, a toolkit enabling the generation of a variety of ‘deep’ argumentative explanations (DAXs) for outputs of NNs on classification tasks

evaluation of liver fibrosis concordance analysis between noninvasive scores apri and fib 4 evolution and predictors in a cohort of hiv infected patients without hepatitis c and b infection

Background. There is lack of data on the incidence of liver fibrosis (LF) progression in patients with human immunodeficiency virus (HIV) monoinfection and risk factors for LF. Methods. We performed an observational prospective study in a cohort of HIV-infected patients who had initiated highly active antiretroviral therapy (HAART). FIB-4 and aspartate aminotransferase (AST)-to-platelet ratio index (APRI) were assessed. The concordance between the 2 scores was assessed by weighted kappa coefficient. Kaplan-Meier analysis was used to estimate the incidence of LF. Cox regression analysis was used to assess the predictors of transition. Results. A total of 1112 patients were observed for a mean of 2249 days of follow-up. The concordance between FIB-4 and APRI was moderate (kappa = .573). The incidence of transition to higher FIB-4 classes was 0.064 (95% confidence interval [CI], 0.056―0.072) per person-year of follow-up (PYFU), whereas the incidence of transition to higher APRI classes was 0.099 (95% CI, 0.089-0.110) per PYFU. The incidence of transition to FIB-4 >3.25 was 0.013 per PYFU (95% CI, 0.010-0.017) and 0.018 per PYFU (95% CI, 0.014―0.022) for APRI >1.5. In multivariate analyses, for transition to higher classes, HIV RNA level 3.25 and APRI> 1.5 as study outcomes. Conclusions. Overall, our results suggest a possible benefit associated with earlier HAART initiation, provided that the effectiveness of HAART is sustained and treatment with DDX is avoided