A New Interleukin-13 Amino-Coated Gadolinium Metallofullerene Nanoparticle for Targeted MRI Detection of Glioblastoma Tumor Cells.

The development of new nanoparticles as next-generation diagnostic and therapeutic ("theranostic") drug platforms is an active area of both chemistry and cancer research. Although numerous gadolinium (Gd) containing metallofullerenes as diagnostic magnetic resonance imaging (MRI) contrast agents have been reported, the metallofullerene cage surface, in most cases, consists of negatively charged carboxyl or hydroxyl groups that limit attractive forces with the cellular surface. It has been reported that nanoparticles with a positive charge will bind more efficiently to negatively charged phospholipid bilayer cellular surfaces, and will more readily undergo endocytosis. In this paper, we report the preparation of a new functionalized trimetallic nitride template endohedral metallofullerene (TNT EMF), Gd3N@C80(OH)x(NH2)y, with a cage surface bearing positively charged amino groups (-NH3(+)) and directly compare it with a similar carboxyl and hydroxyl functionalized derivative. This new nanoparticle was characterized by X-ray photoelectron spectroscopy (XPS), dynamic light scattering (DLS), and infrared spectroscopy. It exhibits excellent (1)H MR relaxivity. Previous studies have clearly demonstrated that the cytokine interleukin-13 (IL-13) effectively targets glioblastoma multiforme (GBM) cells, which are known to overexpress IL-13Rα2. We also report that this amino-coated Gd-nanoplatform, when subsequently conjugated with interleukin-13 peptide IL-13-Gd3N@C80(OH)x(NH2)y, exhibits enhanced targeting of U-251 GBM cell lines and can be effectively delivered intravenously in an orthotopic GBM mouse model.

Predicting verbal reasoning from virtual community membership in a sample of Russian young adults.

Predicting personality traits from social networking site profiles can help to assess individual differences in verbal reasoning without using long questionnaires. Inspired by earlier studies, which investigated whether abstract-thinking ability are predictable by social networking sites data, we used supervised machine learning to predict verbal-reasoning ability based on a proposed set of features extracted from virtual community membership. A large sample (N = 3,646) of Russian young adults aged 18-22 years approved access to the data from their social networking accounts and completed an online test on verbal reasoning. We experimented with binary classification machine-learning models for verbal-reasoning prediction. Prediction performance was tested on isolated control subsamples for men and women. The results of prediction on AUC-ROC metrics for control subsamples over 0.7 indicated reasonably good performance on predicting verbal-reasoning level. We also investigated the contribution of virtual community's genres to verbal reasoning level prediction for male and female participants. Theoretical interpretations of results stemming from both Vygotsky's sociocultural theory and behavioural genomics are discussed, including the implication that virtual communities make up a non-shared environment that can cause variance in verbal reasoning. We intend to conduct studies to explore the implications of the results further.

Corrigendum to "Predicting verbal reasoning from virtual community membership in a sample of Russian young adults" [Heliyon 8 (6) (2022) e09664].

[This corrects the article DOI: 10.1016/j.heliyon.2022.e09664.].