Influence of growth hormone on the mandibular condylar cartilage of rats

Growth hormone (GH) stimulates mandibular growth but its effect on the mandibular condylar cartilage is not well. understood. Objective: This study was designed to understand the influence of GH on mitotic activity and on chondrocytes maturation. The effect of GH on cartilage thickness was also determined. Design: An animal model witt differences in GH status was determined by comparing mutant Lewis dwarf rats with reduced pituitary GH synthesis (dwarf), with normal rats and dwarf animals treated with GH. Six dwarf rats were injected with GH for 6 days, while other six normal rats and six dwarf rats composed other two groups. Mandibular condylar tissues were processed and stained for Herovici's stain and immunohistochemistry, for proliferating cell nuclear antigen (PCNA) and alkaline phosphatase (ALP). Measurements of cartilage thickness as well as the numbers of immunopositive cells for each antibody were analysed by one-way analysis of variance. Results: Cartilage thickness was significantly reduced in the dwarf animals treated with GH. PCNA expression was significant lower in the dwarf rats, but significantly increased when these animals were treated with GH. ALP expression was significant higher in the dwarf animals, while it was significantly reduced in the dwarf animals treated with GH. Conclusions: The results from this study showed that GH stimulates mitotic activity and delays cartilage cells maturation in the mandibular condyte. This effect at the cellular Level may produce changes in the cartilage thickness. (C) 2004 Elsevier Ltd. All rights reserved.

Arthritic disease suppression and cartilage protection with glycosaminoglycan polypeptide complexes (Peptacans) derived from the cartilage extracellular matrix: A novel approach to therapy

Molecular fragments of cartilage are antigenic and can stimulate an autoimmune response. Oral administration of type II collagen prevents disease onset in animal models of arthritis but the effects of other matrix components have not been reported. We evaluated glycosaminoglycan polypeptides (GAG-P) and matrix proteins (CaP) from cartilage for a) mitigating disease activity in rats with collagen-induced arthritis (CIA) and adjuvant-induced arthritis (AIA) and b) stimulating proteoglycan (PG) synthesis by chondrocytes in-vitro. CIA and AIA were established in Wistar rats using standard methods. Agents were administered orally (10–200 mg/kg), either for seven days prior to disease induction (toleragenic protocol), or continuously for 15 days after injecting the arthritigen (prophylactic protocol). Joint swelling and arthritis scores were determined on day 15. Histological sections of joint tissues were assessed post-necropsy. In chondrocyte cultures, CaP + / − interleukin-1 stimulated PG biosynthesis. CaP was also active in preventing arthritis onset at 3.3, 10 or 20 mg/kg in the rat CIA model using the toleragenic protocol. It was only active at 20 and 200 mg/kg in the CIA prophylactic protocol. GAG-P was active in the CIA toleragenic protocol at 20 mg/kg but chondroitin sulfate and glucosamine hydrochloride or glucosamine sulfate were all inactive. The efficacy of CaP in the rat AIA model was less than in the CIA model. These findings lead us to suggest that oral CaP could be used as a disease-modifying anti-arthritic drug.

Automatic initialisation of 3D deformable models for cartilage segmentation

Deformable models are a highly accurate and flexible approach to segmenting structures in medical images. The primary drawback of deformable models is that they are sensitive to initialisation, with accurate and robust results often requiring initialisation close to the true object in the image. Automatically obtaining a good initialisation is problematic for many structures in the body. The cartilages of the knee are a thin elastic material that cover the ends of the bone, absorbing shock and allowing smooth movement. The degeneration of these cartilages characterize the progression of osteoarthritis. The state of the art in the segmentation of the cartilage are 2D semi-automated algorithms. These algorithms require significant time and supervison by a clinical expert, so the development of an automatic segmentation algorithm for the cartilages is an important clinical goal. In this paper we present an approach towards this goal that allows us to automatically providing a good initialisation for deformable models of the patella cartilage, by utilising the strong spatial relationship of the cartilage to the underlying bone.