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.

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.

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.