Nanofibrous Scaffolds Incorporating PDGF-BB Microspheres Induce Chemokine Expression and Tissue Neogenesis In Vivo

Platelet-derived growth factor (PDGF) exerts multiple cellular effects that stimulate wound repair in multiple tissues. However, a major obstacle for its successful clinical application is the delivery system, which ultimately controls the in vivo release rate of PDGF. Polylactic-co-glycolic acid (PLGA) microspheres (MS) in nanofibrous scaffolds (NFS) have been shown to control the release of rhPDGF-BB in vitro. In order to investigate the effects of rhPDGF-BB release from MS in NFS on gene expression and enhancement of soft tissue engineering, rhPDGF-BB was incorporated into differing molecular weight (MW) polymeric MS. By controlling the MW of the MS over a range of 6.5 KDa–64 KDa, release rates of PDGF can be regulated over periods of weeks to months in vitro. The NFS-MS scaffolds were divided into multiple groups based on MS release characteristics and PDGF concentration ranging from 2.5–25.0 µg and evaluated in vivo in a soft tissue wound repair model in the dorsa of rats. At 3, 7, 14 and 21 days post-implantation, the scaffold implants were harvested followed by assessments of cell penetration, vasculogenesis and tissue neogenesis. Gene expression profiles using cDNA microarrays were performed on the PDGF-releasing NFS. The percentage of tissue invasion into MS-containing NFS at 7 days was higher in the PDGF groups when compared to controls. Blood vessel number in the HMW groups containing either 2.5 or 25 µg PDGF was increased above those of other groups at 7d (p<0.01). Results from cDNA array showed that PDGF strongly enhanced in vivo gene expression of the CXC chemokine family members such as CXCL1, CXCL2 and CXCL5. Thus, sustained release of rhPDGF-BB, controlled by slow-releasing MS associated with the NFS delivery system, enhanced cell migration and angiogenesis in vivo, and may be related to an induced expression of chemokine-related genes. This approach offers a technology to accurately control growth factor release to promote soft tissue engineering in vivo

Kernel-Based Reconstruction of C-11-Hydroxyephedrine Cardiac PET Images of the Sympathetic Nervous System

Image reconstruction for positron emission tomography (PET) can be challenging and the resulting image typically has high noise. The kernel-based reconstruction method [1], incorporates prior anatomic information in the reconstruction algorithm to reduce noise while preserving resolution. Prior information is incorporated in the reconstruction algorithm by means of spatial kernels originally used in machine learning. In this paper, the kernel-based method is used to reconstruct PET images of sympathetic innervation in the heart. The resulting images are compared with standard Ordered Subset Expectation Maximization (OSEM) reconstructed images qualitatively and quantitatively using data from 6 human subjects. The kernel-based method demonstrated superior SNR with preserved contrast and accuracy compared to OSEM

Total-Body PET Multiparametric Imaging of Cancer Using a Voxelwise Strategy of Compartmental Modeling.

Quantitative dynamic PET with compartmental modeling has the potential to enable multiparametric imaging and more accurate quantification than static PET imaging. Conventional methods for parametric imaging commonly use a single kinetic model for all image voxels and neglect the heterogeneity of physiologic models, which can work well for single-organ parametric imaging but may significantly compromise total-body parametric imaging on a scanner with a long axial field of view. In this paper, we evaluate the necessity of voxelwise compartmental modeling strategies, including time delay correction (TDC) and model selection, for total-body multiparametric imaging. Methods: Ten subjects (5 patients with metastatic cancer and 5 healthy volunteers) were scanned on a total-body PET/CT system after injection of 370 MBq of 18F-FDG. Dynamic data were acquired for 60 min. Total-body parametric imaging was performed using 2 approaches. One was the conventional method that uses a single irreversible 2-tissue-compartment model with and without TDC. The second approach selects the best kinetic model from 3 candidate models for individual voxels. The differences between the 2 approaches were evaluated for parametric imaging of microkinetic parameters and the 18F-FDG net influx rate, KiResults: TDC had a nonnegligible effect on kinetic quantification of various organs and lesions. The effect was larger in lesions with a higher blood volume. Parametric imaging of Ki with the standard 2-tissue-compartment model introduced vascular-region artifacts, which were overcome by the voxelwise model selection strategy. Conclusion: The time delay and appropriate kinetic model vary in different organs and lesions. Modeling of the time delay of the blood input function and model selection improved total-body multiparametric imaging