Relaxometric properties and biocompatibility of a novel nanostructured fluorinated gadolinium metal-organic framework.
Letizia TrovarelliAlessandra MirarchiCataldo ArcuriStefano BruscoliOxana BereshchenkoMarta FeboFabio CarniatoFerdinando CostantinoPublished in: Dalton transactions (Cambridge, England : 2003) (2024)
A novel Gd-MOF based on tetrafluoro-terephthalic acid has been synthesized and its structure has been solved using X-ray single crystal diffraction data. The compound, with the formula [Gd 2 (F 4 BDC) 3 ·H 2 O]·DMF, is isostructural with other Ln-MOFs based on the same ligand and has been recently reported. Its crystals were also reduced to nanometer size by employing acetic acid or cetyltrimethylammonium bromide (CTAB) as a modulator. The relaxometric properties of the nanoparticles were evaluated in solution by measuring 1 H T 1 and T 2 as a function of the applied magnetic field and temperature. The biocompatibility of Gd-MOFs was evaluated on murine microglial BV-2 and human glioblastoma U251 cell lines. In both cell lines, Gd-MOFs do not modify the cell cycle profile or the activation levels of ERK1/2 and Akt, which are protein-serine/threonine kinases that participate in many signal transduction pathways. These pathways are fundamental in the regulation of a large variety of processes such as cell migration, cell cycle progression, differentiation, cell survival, metabolism, transcription, tumour progression and others. These data indicate that Gd-MOF nanoparticles exhibit high biocompatibility, making them potentially valuable for diagnostic and biomedical applications.
Keyphrases
- metal organic framework
- cell cycle
- cell proliferation
- cell migration
- signaling pathway
- endothelial cells
- electronic health record
- lipopolysaccharide induced
- big data
- pi k akt
- lps induced
- protein kinase
- inflammatory response
- high resolution
- machine learning
- computed tomography
- preterm infants
- spinal cord injury
- mass spectrometry
- small molecule
- tissue engineering
- protein protein
- contrast enhanced
- amino acid
- walled carbon nanotubes
- deep learning