<p>Size complementarity of X@C₈₂ endohedral fullerenes with [11]CPP allows their selective complexation from a mixture also containing smaller fullerenes.</p>

<jats:p>Massive Open Online Courses (MOOCs) have experienced in recent years a significant growth in courses'offer and the number of enrolled students. Nevertheless, the controversy regarding if its quality is reliable, namely in student evaluation and assessment, has not found closure. In this study, we aim at establishing an initial prospection of the academic teaching professionals' perspective regarding the quality of the most common/usual evaluation methods and tools used in MOOCs. After the elaboration of a questionnaire and its implementation to an international sample of academic professors, the analysis of the answers allows perceiving which MOOC grading methods are acceptable in presential Higher Education courses and its eventual acceptable weight in the final grade. Further, within certain constraints, a large percentage of the inquired academics presented no problem with the inclusion of MOOC grading methods on their non-online courses. Overall, within those constraints, the academics felt the quality of the academic orthodox courses was maintained, a perspective that can contribute to change eventual suspicious attitudes regarding  MOOCs evaluation methodologies and their student assessment. </jats:p>

Parkinson's Disease (PD) is a long-term neurodegenerative brain disorder that mainly affects the motor system. The causes are still unknown, and even though currently there is no cure, several therapeutic options are available to manage its symptoms. The development of novel anti-parkinsonian agents and an understanding of their proper and optimal use are, indeed, highly demanding. For the last decades, L-3,4-DihydrOxyPhenylAlanine or levodopa (L-DOPA) has been the gold-standard therapy for the symptomatic treatment of motor dysfunctions associated to PD. However, the development of dyskinesias and motor fluctuations (wearing-off and on-off phenomena) associated with long-term L-DOPA replacement therapy have limited its antiparkinsonian efficacy. The investigation for non-dopaminergic therapies has been largely explored as an attempt to counteract the motor side effects associated with dopamine replacement therapy. Being one of the largest cell membrane protein families, G-Protein-Coupled Receptors (GPCRs) have become a relevant target for drug discovery focused on a wide range of therapeutic areas, including Central Nervous System (CNS) diseases. The modulation of specific GPCRs potentially implicated in PD, excluding dopamine receptors, may provide promising non-dopaminergic therapeutic alternatives for symptomatic treatment of PD. In this review, we focused on the impact of specific GPCR subclasses, including dopamine receptors, adenosine receptors, muscarinic acetylcholine receptors, metabotropic glutamate receptors, and 5-hydroxytryptamine receptors, on the pathophysiology of PD and the importance of structure- and ligand-based in silico approaches for the development of small molecules to target these receptors.

Mixing of ionic liquids provides new opportunities for their tuning, enabling the applications of ionic liquid mixtures to expand. At the same time, the genesis of the fundamental properties of ionic liquid mixtures is still poorly understood. In this study we carried out a molecular dynamics simulation of binary mixtures of 1-buthyl-3-methylimidazolium hexafluorophosphate, 1-buthyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide, and 1-buthyl-3-methylimidazolium tris(perfluoroethyl)trifluorophosphate ([C(4)mim][PF6] + [C(4)mim][NTf2], [C(4)mim][PF6] + [C(4)mim][FAP], [C(4)mim][FAP] + [C(4)mim][NTf2]) in a wide concentration range at 303.15 K and complemented it with quantum mechanical calculations. Three pure ionic liquids underwent the same kind of analysis for comparison purposes. We found that the addition of the [FAP](-)-anion to a mixture enhances the segregation of non-polar domains and weakens the hydrogen-bond network. The H-bonds in the studied mixtures are rather weak, as follows from QTAIM analysis, with the rarest occurrence for the [FAP](-)-anion. The competition of two anions in the mixtures for the most acidic hydrogen of the 1-butyl-3-methylimidazolium cation is reported. In most of the cases, the smaller anion ([PF6](-) or [NTf2](-)) with stronger charge concentration displaces the bigger one ([NTf2](-) or [FAP](-)) from the preferred coordination site. The existing nano-segregation in some mixtures notably slows down ion diffusion. Our results show that the differences in anion size, shape and nature are the main reasons for nano-segregation and the non-ideal behavior of ionic liquid mixtures.

Epidermal Growth Factor Receptor (EGFR) is still the main target of the Head and Neck Squamous Cell Cancer (HNSCC) because its overexpression has been detected in more than 90% of this type of cancer. This overexpression is usually linked with more aggressive disease, increased resistance to chemotherapy and radiotherapy, increased metastasis, inhibition of apoptosis, promotion of neoplastic angiogenesis, and, finally, poor prognosis and decreased survival. Due to this reason, the main target in the search of new drugs and inhibitors candidates is to downturn this overexpression. Quantitative Structure-Activity Relationship (QSAR) is one of the most widely used approaches while looking for new and more active inhibitors drugs. In this contest, a lot of authors used this technique, combined with others, to find new drugs or enhance the activity of well-known inhibitors. In this paper, on one hand, we will review the most important QSAR approaches developed in the last fifteen years, spacing from classical 1D approaches until more sophisticated 3D; the first paper is dated 2003 while the last one is from 2017. On the other hand, we will present a completely new QSAR approach aimed at the prediction of new EGFR inhibitors drugs. The model presented here has been developed over a dataset consisting of more than 1000 compounds using various molecular descriptors calculated with the DRAGON 7.0 (c) software.