Functionalized fullerenes and carbon nanotubes were investigated as regards in vitro cytotoxicity and hemolytic properties. Pristine carbon nanotubes (CNTs) were first shortened to make them compatible with cellular dimensions before functional groups were appended to their surface either covalently (e.g. amine, alcohol, carboxylate) or non-covalently (adsorption and polymerization of different polyethylene glycol-based amphiphiles). C60 fullerenes were covalently functionalized with polyethylene glycols of various sizes. Cell viability was measured 24 h after exposure to the nanomaterials using MTT and LDH assays which were adapted to avoid nanomaterial interference. In vitro analysis of hemolytic properties was also performed to assess acute damage to red blood cells. While all the tested nanomaterials were found to reduce, to some extent, the cellular metabolic activity, two only affected the plasma membrane integrity, and none induced hemolysis.

Nuclear Magnetic Resonance Imaging (MRI) is a very useful tool in modern medical diagnostics, especially when gadolinium(III)-based contrast agents are administered to the patient with the aim of increasing the image contrast between normal and diseased tissues. The main purpose of this review is to show that a new generation of these contrast agents could be developed by making greater use of soft modelling techniques such as QSAR / QSPR after a suitable description of their molecular structure.

Prostate cancer (PCa) is one of the most common tumors diagnosed in men worldwide, with approximately 1.7 million new cases expected by 2030. Most cancerous lesions in PCa are located in the peripheral zone (PZ); therefore, accurate identification of the location of the lesion is essential for effective diagnosis and treatment. Zonal segmentation in magnetic resonance imaging (MRI) scans is critical and plays a key role in pinpointing cancerous regions and treatment strategies. In this work, we report on the development of three advanced neural network-based models: one based on ensemble learning, one on Meta-Net, and one on YOLO-V8. They were tailored for the segmentation of the central gland (CG) and PZ using a small dataset of 90 MRI scans for training, 25 MRIs for validation, and 24 scans for testing. The ensemble learning method, combining U-Net-based models (Attention-Res-U-Net, Vanilla-Net, and V-Net), achieved an IoU of 79.3% and DSC of 88.4% for CG and an IoU of 54.5% and DSC of 70.5% for PZ on the test set. Meta-Net, used for the first time in segmentation, demonstrated an IoU of 78% and DSC of 88% for CG, while YOLO-V8 outperformed both models with an IoU of 80% and DSC of 89% for CG and an IoU of 58% and DSC of 73% for PZ.