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Article Abstract

Molecular imaging probes hold great promise in disease diagnostics and their therapeutic interventions. However, a single imaging modality sometimes lacks efficiency in all kinds of diseases. Conditions such as cancer, cardiovascular disorders, and neurodegenerative diseases critically require multimodal imaging to characterize complex biological environments accurately. Efficient targeting further enhances probe performance, improving diagnostic precision. This study explores the design rationale and application of bimodal probes designed for Magnetic Resonance (MR) and fluorescence (FL) imaging for their complementary strengths. MRI enables deep tissue visualization, while fluorescence offers high sensitivity and cellular-level resolution. Meticulous attention has been devoted to presenting methodologies aimed at improving the targeting efficacy of these probes. This involves the methodology to enhance targeting efficacy, including ligand-based strategies, nanoparticle functionalization, and molecularly imprinted polymers. The probes combine fluorescence for precise cellular imaging with MRI for in-depth tissue visualization, providing synergistic benefits that elevate their diagnostic potential. Moreover, we offer recent developments in Machine Learning and Artificial Intelligence-based computational approaches for image analysis, enabling more precise diagnosis across a range of diseases that may propel their diagnostic abilities for better therapeutic outcomes. Through systematic analysis and in vitro and in vivo evaluations, we demonstrate the ability of the probes to achieve superior spatial and temporal resolution, facilitating the accurate delineation of biological targets. The integration of these bimodal probes holds great promise for advancing our understanding of complex biological processes, enabling more precise diagnostics, and paving the way for targeted therapeutic interventions.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12317703PMC
http://dx.doi.org/10.2147/IJN.S524454DOI Listing

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