ABSTRACT
The first magnetic ligand-based electrochemical assay aimed at the determination of BRD4 was developed and validated. BRD4 is an epigenetic regulator of great interest in oncology in relation to its overexpression observed in the pathogenesis of several cancer diseases. BRD4 also represents a major target for the development of innovative treatments aimed at protein inhibition or degradation. Despite the relevance of BRD4 both for diagnostics and therapeutic purposes, current methodologies for its determination are limited to commercial ELISA kits. We present a novel magnetic ligand-based assay for the electrochemical determination of BRD4. The developed assay is based on the use of a small synthetic fragment of the natural protein ligand for BRD4 as receptor, thus exploiting the intrinsic biological protein-protein recognition mechanism. In addition, the assay features the use of magnetic beads as immobilization platforms and peroxidase-conjugated monoclonal anti-BRD4 antibody for the generation of the electrochemical signal. The ligand-based assay shows outstanding performance in terms of rapidity, with results achievable in less than 20 min, no matrix effect when applied to human plasma or cell lysate samples, and excellent specificity. The proposed method exhibits a limit of detection of 2.66 nM and a response range tunable as a function of the amount of immobilized receptor. The developed ligand-based assay was successfully applied to the accurate determination of BRD4 in untreated cell lysates, as proven by the ELISA reference method. The good performance of the proposed bioassay for determination of BRD4 showed potential application of this strategy in convenient point-of-care testing.
ABSTRACT
Artificial intelligence (AI) is one of the most promising fields of research in medical imaging so far. By means of specific algorithms, it can be used to help radiologists in their routine workflow. There are several papers that describe AI approaches to solve different problems in liver and pancreatic imaging. These problems may be summarized in four different categories: segmentation, quantification, characterization and image quality improvement. Segmentation is usually the first step of successive elaborations. If done manually, it is a time-consuming process. Therefore, the semi-automatic and automatic creation of a liver or a pancreatic mask may save time for other evaluations, such as quantification of various parameters, from organs volume to their textural features. The alterations of normal liver and pancreas structure may give a clue to the presence of a diffuse or focal pathology. AI can be trained to recognize these alterations and propose a diagnosis, which may then be confirmed or not by radiologists. Finally, AI may be applied in medical image reconstruction in order to increase image quality, decrease dose administration (referring to computed tomography) and reduce scan times. In this article, we report the state of the art of AI applications in these four main categories.
