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Nanoparticles (NPs) are a promising tool for in vivo multimodality imaging and theranostic applications. Hyaluronic acid (HA)-based NPs have numerous active groups that make them ideal as tumor-targeted carriers. The B-lymphoma neoplastic cells express on their surfaces a clone-specific immunoglobulin receptor (Ig-BCR). The peptide A20-36 (pA20-36) selectively binds to the Ig-BCR of A20 lymphoma cells. In this work, we demonstrated the ability of core-shell chitosan-HA-NPs decorated with pA20-36 to specifically target A20 cells and reduce the tumor burden in a murine xenograft model. We monitored tumor growth using high-frequency ultrasonography and demonstrated targeting specificity and kinetics of the NPs via in vivo fluorescent reflectance imaging. This result was also confirmed by ex vivo magnetic resonance imaging and confocal microscopy. In conclusion, we demonstrated the ability of NPs loaded with fluorescent and paramagnetic tracers to act as multimodal imaging contrast agents and hence as a non-toxic, highly specific theranostic system.
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http://dx.doi.org/10.1016/j.nano.2017.11.016 | DOI Listing |
Retin Cases Brief Rep
September 2025
Doheny Eye Institute, David Geffen School of Medicine, University of California, Los Angeles, California, USA.
Purpose: To report the examination and multimodal imaging findings of a patient with unilateral bull's eye maculopathy.
Methods: A retrospective chart review of a 77-year-old patient with unilateral bull's eye maculopathy who presented to a tertiary retinal practice was performed. The patient's history, visual acuity, examination and multimodal imaging findings over five years of follow-up were described.
J Vis Exp
August 2025
Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology.
We present multimodal confocal Raman micro-spectroscopy (RS) and tomographic phase microscopy (TPM) for quick morpho-chemical phenotyping of human breast cancer cells (MDA-MB-231). Leveraging the non-perturbative nature of these advanced microscopy techniques, we captured detailed morpho-molecular data from living, label-free cells in their native physiological environment. Human bias-free data processing pipelines were developed to analyze hyperspectral Raman images (spanning Raman modes from 600 cm to 1800 cm, which uniquely characterize a wide range of molecular bonds and subcellular structures), as well as morphological data from three-dimensional refractive index tomograms (providing measurements of cell volume, surface area, footprint, and sphericity at nanometer resolution, alongside dry mass and density).
View Article and Find Full Text PDFIEEE J Biomed Health Inform
September 2025
The Segment Anything Model (SAM) has attracted considerable attention due to its impressive performance and demonstrates potential in medical image segmentation. Compared to SAM's native point and bounding box prompts, text prompts offer a simpler and more efficient alternative in the medical field, yet this approach remains relatively underexplored. In this paper, we propose a SAM-based framework that integrates a pre-trained vision-language model to generate referring prompts, with SAM handling the segmentation task.
View Article and Find Full Text PDFJ Xray Sci Technol
September 2025
Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao, China.
Parkinson's disease (PD) is a challenging neurodegenerative condition often prone to diagnostic errors, where early and accurate diagnosis is critical for effective clinical management. However, existing diagnostic methods often fail to fully exploit multimodal data or systematically incorporate expert domain knowledge. To address these limitations, we propose MKD-Net, a multimodal and knowledge-driven diagnostic framework that integrates imaging and non-imaging clinical data with structured expert insights to enhance diagnostic performance.
View Article and Find Full Text PDFRadiol Med
September 2025
Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, 20141, Milan, Italy.
Metastatic involvement (MB) of the breast from extramammary malignancies is rare, with an incidence of 0.09-1.3% of all breast malignancies.
View Article and Find Full Text PDF