Treating Proximal Tibial Growth Plate Injuries Using Poly(Lactic-co-Glycolic Acid) Scaffolds

Growth plate fractures account for nearly 18.5% of fractures in children. Depending on the type and severity of the injury, inhibited bone growth or angular deformity caused by bone forming in place of the growth plate can occur. The current treatment involves removal of the bony bar and replacing it with a filler substance, such as a free fat graft. Unfortunately, reformation of the bony bar frequently occurs, preventing the native growth plate from regenerating. The goal of this pilot study was to determine whether biodegradable scaffolds can enhance native growth plate regeneration following a simulated injury that resulted in bony bar formation in the proximal tibial growth plate of New Zealand white rabbits. After removing the bony bar, animals received one of the following treatments: porous poly(lactic-co-glycolic acid) (PLGA) scaffold; PLGA scaffold loaded with insulin-like growth factor I (IGF-I); PLGA scaffold loaded with IGF-I and seeded with autogenous bone marrow cells (BMCs) harvested at the time of implantation; or fat graft (as used clinically). The PLGA scaffold group showed an increased chondrocyte population and a reduced loss of the remaining native growth plate compared to the fat graft group (the control group). An additional increase in chondrocyte density was seen in scaffolds loaded with IGF-I, and even more so when BMCs were seeded on the scaffold. While there was no significant reduction in the angular deformation of the limbs, the PLGA scaffolds increased the amount of cartilage and reduced the amount of bony bar reformation

Small Molecule Drug Release Form in Situ Forming Degradable Scaffolds Incorporating Hydrogels and Bioceramic Microparticles

The present invention relates to an injectable system combining a hydrogel, a bioceramic and a degradable matrix that provides for sustained drug delivery and structural support to recovering tissue, such as bone and the periodontium

Defining hierarchical protein interaction networks from spectral analysis of bacterial proteomes

Cellular behaviors emerge from layers of molecular interactions: proteins interact to form complexes, pathways, and phenotypes. We show that hierarchical networks of protein interactions can be defined from the statistical pattern of proteome variation measured across thousands of diverse bacteria and that these networks reflect the emergence of complex bacterial phenotypes. Our results are validated through gene-set enrichment analysis and comparison to existing experimentally derived databases. We demonstrate the biological utility of our approach by creating a model of motility i

Early Growth Response Gene 1–mediated Apoptosis Is Essential for Transforming Growth Factor β1–induced Pulmonary Fibrosis

Fibrosis and apoptosis are juxtaposed in pulmonary disorders such as asthma and the interstitial diseases, and transforming growth factor (TGF)-β1 has been implicated in the pathogenesis of these responses. However, the in vivo effector functions of TGF-β1 in the lung and its roles in the pathogenesis of these responses are not completely understood. In addition, the relationships between apoptosis and other TGF-β1–induced responses have not been defined. To address these issues, we targeted bioactive TGF-β1 to the murine lung using a novel externally regulatable, triple transgenic system. TGF-β1 produced a transient wave of epithelial apoptosis that was followed by mononuclear-rich inflammation, tissue fibrosis, myofibroblast and myocyte hyperplasia, and septal rupture with honeycombing. Studies of these mice highlighted the reversibility of this fibrotic response. They also demonstrated that a null mutation of early growth response gene (Egr)-1 or caspase inhibition blocked TGF-β1–induced apoptosis. Interestingly, both interventions markedly ameliorated TGF-β1–induced fibrosis and alveolar remodeling. These studies illustrate the complex effects of TGF-β1 in vivo and define the critical role of Egr-1 in the TGF-β1 phenotype. They also demonstrate that Egr-1–mediated apoptosis is a prerequisite for TGF-β1–induced fibrosis and remodeling

Artemin, a Novel Member of the GDNF Ligand Family, Supports Peripheral and Central Neurons and Signals through the GFRα3–RET Receptor Complex

AbstractThe glial cell line–derived neurotrophic factor (GDNF) ligands (GDNF, Neurturin [NTN], and Persephin [PSP]) signal through a multicomponent receptor system composed of a high-affinity binding component (GFRα1–GFRα4) and a common signaling component (RET). Here, we report the identification of Artemin, a novel member of the GDNF family, and demonstrate that it is the ligand for the former orphan receptor GFRα3–RET. Artemin is a survival factor for sensory and sympathetic neurons in culture, and its expression pattern suggests that it also influences these neurons in vivo. Artemin can also activate the GFRα1–RET complex and supports the survival of dopaminergic midbrain neurons in culture, indicating that like GDNF (GFRα1–RET) and NTN (GFRα2–RET), Artemin has a preferred receptor (GFRα3–RET) but that alternative receptor interactions also occur

Confronting the Challenge of Modeling Cloud and Precipitation Microphysics

In the atmosphere, microphysics refers to the microscale processes that affect cloud and precipitation particles and is a key linkage among the various components of Earth\u27s atmospheric water and energy cycles. The representation of microphysical processes in models continues to pose a major challenge leading to uncertainty in numerical weather forecasts and climate simulations. In this paper, the problem of treating microphysics in models is divided into two parts: (i) how to represent the population of cloud and precipitation particles, given the impossibility of simulating all particles individually within a cloud, and (ii) uncertainties in the microphysical process rates owing to fundamental gaps in knowledge of cloud physics. The recently developed Lagrangian particle‐based method is advocated as a way to address several conceptual and practical challenges of representing particle populations using traditional bulk and bin microphysics parameterization schemes. For addressing critical gaps in cloud physics knowledge, sustained investment for observational advances from laboratory experiments, new probe development, and next‐generation instruments in space is needed. Greater emphasis on laboratory work, which has apparently declined over the past several decades relative to other areas of cloud physics research, is argued to be an essential ingredient for improving process‐level understanding. More systematic use of natural cloud and precipitation observations to constrain microphysics schemes is also advocated. Because it is generally difficult to quantify individual microphysical process rates from these observations directly, this presents an inverse problem that can be viewed from the standpoint of Bayesian statistics. Following this idea, a probabilistic framework is proposed that combines elements from statistical and physical modeling. Besides providing rigorous constraint of schemes, there is an added benefit of quantifying uncertainty systematically. Finally, a broader hierarchical approach is proposed to accelerate improvements in microphysics schemes, leveraging the advances described in this paper related to process modeling (using Lagrangian particle‐based schemes), laboratory experimentation, cloud and precipitation observations, and statistical methods