Bimodal imaging probes for combined PET and OI: recent developments and future directions for hybrid agent development.

Biomed Res Int

Biomedical Chemistry, Department of Clinical Radiology and Nuclear Medicine, Medical Faculty Mannheim of Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany.

Published: January 2015

Molecular imaging--and especially positron emission tomography (PET)--has gained increasing importance for diagnosis of various diseases and thus experiences an increasing dissemination. Therefore, there is also a growing demand for highly affine PET tracers specifically accumulating and visualizing target structures in the human body. Beyond the development of agents suitable for PET alone, recent tendencies aim at the synthesis of bimodal imaging probes applicable in PET as well as optical imaging (OI), as this combination of modalities can provide clinical advantages. PET, due to the high tissue penetration of the γ-radiation emitted by PET nuclides, allows a quantitative imaging able to identify and visualize tumors and metastases in the whole body. OI on the contrary visualizes photons exhibiting only a limited tissue penetration but enables the identification of tumor margins and infected lymph nodes during surgery without bearing a radiation burden for the surgeon. Thus, there is an emerging interest in bimodal agents for PET and OI in order to exploit the potential of both imaging techniques for the imaging and treatment of tumor diseases. This short review summarizes the available hybrid probes developed for dual PET and OI and discusses future directions for hybrid agent development.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4009187PMC
http://dx.doi.org/10.1155/2014/153741DOI Listing

Publication Analysis

Top Keywords

bimodal imaging
8
imaging probes
8
pet
8
future directions
8
directions hybrid
8
hybrid agent
8
agent development
8
tissue penetration
8
imaging
5
probes combined
4

Similar Publications

The local pulse wave velocity (PWV) from large elastic arteries and its pressure-dependent changes within a cardiac cycle are potential biomarkers for cardiovascular risk stratification. However, pulse wave reflections can impair the accuracy of local PWV measurements. We propose a method to measure pressure-dependent variations in local PWV while minimizing the influence of pulse wave reflections.

View Article and Find Full Text PDF

Neoadjuvant chemotherapy (NAC) is a systemic and systematic chemotherapy regimen for breast cancer patients before surgery. However, NAC is not effective for everyone, and the process is excruciating. Therefore, accurate early prediction of the efficacy of NAC is essential for the clinical diagnosis and treatment of patients.

View Article and Find Full Text PDF

The repercussions of hormone replacement therapy (HRT) and bisphosphonates pose serious clinical challenges and warrant novel therapies for osteoporosis in menopausal women. To confront this issue, the present research aimed to design and fabricate daidzein (DZ); a phytoestrogen-loaded hydroxyapatite nanoparticles to mimic and compensate for synthetic estrogens and biomineralization. Hypothesizing this bimodal approach, hydroxyapatite nanoparticles (HAPNPs) were synthesized using the chemical-precipitation method followed by drug loading (DZHAPNPs) via sorption.

View Article and Find Full Text PDF

The assessment of early atherosclerosis (AS) via fluorescence imaging is crucial for advancing early diagnosis research. Abnormal inflammation biomarkers, including hypochlorous acid (HClO) and viscosity within mitochondria, have been closely linked to the pathogenesis of AS. However, current fluorescent probes predominantly rely on unimodal imaging that targets a single biomarker and lacks mitochondrial specificity, which can result in potential false signal readouts due to the complex intracellular environment.

View Article and Find Full Text PDF

PIDGN: An explainable multimodal deep learning framework for early prediction of Parkinson's disease.

J Neurosci Methods

January 2025

School of Mathematics and Statistics, Ludong University, Yantai 264025, China. Electronic address:

Background: Parkinson's disease (PD), the second most common neurodegenerative disease in the world, is usually not diagnosed until the later stages of the disease, when patients might have already missed the best treatment period. Therefore, more effective prediction methods based on artificial intelligence (AI) are needed to assist physicians in timely diagnosis.

New Methods: An explainable deep learning-based early Parkinson's disease diagnostic model, Parkinson's Integrative Diagnostic Gated Network (PIDGN), was designed by fusing Single Nucleotide Polymorphism (SNP) and brain sMRI data.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!