Comparative Studies of the Structural and Physicochemical Properties of the First Fullerene Derivative FD-C (Fullerenol) and Second Fullerene Derivate SD-C (3HFWC).

In order to maximally reduce the toxicity of fullerenol (the first derivative of C, FD-C), and increase its biomedical efficiency, the second derivative SD-C (3HFWC, Hyper-Harmonized Hydroxylated Fullerene Water Complex) was created. Several different methods were applied in the comparative characterization of FD-C and SD-C with the same OH groups in their core. FD-C as an individual structure was about 1.

The Second Derivative of Fullerene C (SD-C) and Biomolecular Machinery of Hydrogen Bonds: Water-Based Nanomedicine.

The human body contains 60-70% water, depending on age. As a body fluid, it is not only a medium in which physical and chemical processes take place, but it is also one of the active mediators. Water is the richest substance with non-covalent hydrogen bonds.

Physicochemical Properties of Gold Nanoparticles for Skin Care Creams.

Gold nanoparticles (AuNPs) have now been used in skin care creams for several years, with marketed anti-aging, moisturizing, and regenerative properties. Information on the harmful effects of these nanoparticles is lacking, a concern for the use of AuNPs as cosmetic ingredients. Testing AuNPs without the medium of a cosmetic product is a typical method for obtaining this information, which is mainly dependent on their size, shape, surface charge, and dose.

The presymptomatic treatment with 3HFWC nanosubstance decreased plaque load in 5XFAD mouse model of Alzheimer's disease.

Introduction: In the present study, we assessed the effects of the hyper-harmonized-hydroxylated fullerene-water complex (3HFWC) on Alzheimer's disease (AD) neuropathological hallmarks in 5XFAD mice, an AD animal model.

Methods: The 3-week-old 5XFAD mice were exposed to 3HFWC water solution ad libitum for 3 months in the presymptomatic phase of pathology. The functional effects of the treatment were confirmed through near-infrared spectroscopy (NIRS) analysis through machine learning (ML) using artificial neural networks (ANNs) to classify the control and 3HFWC-treated brain tissue samples.