Targeted Molecular Magnetic Resonance Imaging Detects Brown Adipose Tissue with Ultrasmall Superparamagnetic Iron Oxide

The peptide (CKGGRAKDC-NH2) specifically targets the brown adipose tissue (BAT). Here we applied this peptide coupled with polyethylene glycol (PEG)-coated ultrasmall superparamagnetic iron oxide (USPIO) nanoparticles to detect BAT in vivo by magnetic resonance imaging (MRI). The peptide was conjugated with PEG-coated USPIO nanoparticles to obtain targeted USPIO nanoprobes. Then the nanoprobes for BAT were evaluated in mice. T2⁎-weighted images were performed, precontrast and postcontrast USPIO nanoparticles. Finally, histological analyses proved the specific targeting. The specificity of targeted USPIO nanoprobes was observed in mice. The T2⁎ relaxation time of BAT in the targeted group decreased obviously compared to the controls (P<0.001). Prussian blue staining and transmission electron microscope confirmed the specific presence of iron oxide. This study demonstrated that peptide (CKGGRAKDC-NH2) coupled with PEG-coated USPIO nanoparticles could identify BAT noninvasively in vivo with MRI

Magnetic Nanofibrous Scaffolds Accelerate the Regeneration of Muscle Tissue in Combination with Extra Magnetic Fields

The reversal of loss of the critical size of skeletal muscle is urgently required using biomaterial scaffolds to guide tissue regeneration. In this work, coaxial electrospun magnetic nanofibrous scaffolds were fabricated, with gelatin (Gel) as the shell of the fiber and polyurethane (PU) as the core. Iron oxide nanoparticles (Mag) of 10 nm diameter were added to the shell and core layer. Myoblast cells (C2C12) were cultured on the magnetic scaffolds and exposed to the applied magnetic fields. A mouse model of skeletal muscle injury was used to evaluate the repair guided by the scaffolds under the magnetic fields. It was shown that VEGF secretion and MyoG expression for the myoblast cells grown on the magnetic scaffolds under the magnetic fields were significantly increased, while, the gene expression of Myh4 was up-regulated. Results from an in vivo study indicated that the process of skeletal muscle regeneration in the mouse muscle injury model was accelerated by using the magnetic actuated strategy, which was verified by histochemical analysis, immunofluorescence staining of CD31, electrophysiological measurement and ultrasound imaging. In conclusion, the integration of a magnetic scaffold combined with the extra magnetic fields enhanced myoblast differentiation and VEGF secretion and accelerated the defect repair of skeletal muscle in situ

Inter-Rater Uncertainty Quantification in Medical Image Segmentation via Rater-Specific Bayesian Neural Networks

Automated medical image segmentation inherently involves a certain degree of uncertainty. One key factor contributing to this uncertainty is the ambiguity that can arise in determining the boundaries of a target region of interest, primarily due to variations in image appearance. On top of this, even among experts in the field, different opinions can emerge regarding the precise definition of specific anatomical structures. This work specifically addresses the modeling of segmentation uncertainty, known as inter-rater uncertainty. Its primary objective is to explore and analyze the variability in segmentation outcomes that can occur when multiple experts in medical imaging interpret and annotate the same images. We introduce a novel Bayesian neural network-based architecture to estimate inter-rater uncertainty in medical image segmentation. Our approach has three key advancements. Firstly, we introduce a one-encoder-multi-decoder architecture specifically tailored for uncertainty estimation, enabling us to capture the rater-specific representation of each expert involved. Secondly, we propose Bayesian modeling for the new architecture, allowing efficient capture of the inter-rater distribution, particularly in scenarios with limited annotations. Lastly, we enhance the rater-specific representation by integrating an attention module into each decoder. This module facilitates focused and refined segmentation results for each rater. We conduct extensive evaluations using synthetic and real-world datasets to validate our technical innovations rigorously. Our method surpasses existing baseline methods in five out of seven diverse tasks on the publicly available \emph{QUBIQ} dataset, considering two evaluation metrics encompassing different uncertainty aspects. Our codes, models, and the new dataset are available through our GitHub repository: https://github.com/HaoWang420/bOEMD-net .Comment: submitted to a journal for revie