Saint Francis University

About: Saint Francis University is a education organization based out in Loretto, Pennsylvania, United States. It is known for research contribution in the topics: Population & Osteoblast. The organization has 1694 authors who have published 2038 publications receiving 87149 citations.

Effect of bone-derived growth factor on DNA, RNA, and proteoglycan synthesis in cultures of rabbit costal chondrocytes.

Abstract: Calvariae and chondrocytes in culture have been reported to release growth factors which stimulate bone and cartilage growth respectively. In the present studies, we examined the effects of bone-derived growth factor (BDGF) on DNA, RNA and proteoglycan synthesis in cultured rabbit chondrocytes. Two partially purified fractions of BDGF were tested, one with an approximate molecular weight (MW) of 20–30,000 and with greater activity on calvarial DNA labeling (BDGF I) and another with an approximate MW 6–13,000 and greater activity on bone collagen labeling (BDGF II). Both fractions had a similar effect and increased the incorporation of −3H-uridine into acid insoluble residues in chondrocytes and the incorporation of 35SO42-, 3H-glucosamine and 3H-serine into proteoglycans. However, BDGF II had a greater stimulatory effect on the incorporation of 3H-thymidine than BDGF I. These findings suggest that factor(s) released by bone cells are capable of stimulating cartilage metabolism and growth.

Assisted History Matching Using Pattern Recognition Technology

Abstract: This study examines the application of pattern recognition technologies to improve the time and effort required for completing successful history matching projects. The pattern recognition capabilities of artificial intelligence and data mining techniques are used to develop a surrogate reservoir model (SRM), which is then employed to perform the assisted history matching process. A well-known reservoir model, PUNQ-S3, was selected to study the potentials of the SRM in an assisted history matching process. The SRM is a prototype of a full-field reservoir simulation model that demands a low development cost and has a high implementation pace. SRMs are built based on a spatio-temporal database, which includes different types of data extracted from a few realisations of the simulation model. The SRM was coupled with the differential evolution optimisation method to construct an automated history matching workflow. The results of this study prove the SRMs' capability in assisting history matching processes. [Received: December 3, 2015; Accepted: June 17, 2016]

An FBN1 pseudoexon mutation in a patient with Marfan syndrome: Confirmation of cryptic mutations leading to disease

Abstract: Marfan syndrome (MFS) results from heterozygous mutations in FBN1. However, genetic analyses of deoxyribonucleic acid (DNA) from approximately 10–30% of MFS patients who meet diagnostic criteria do not reveal an identifiable FBN1 mutation. In a patient who met the diagnostic criteria for MFS, bidirectional DNA sequencing of exons and intron–exon boundaries of FBN1 failed to reveal a mutation. Assessment of the FBN1 message in dermal fibroblasts from the patient revealed insertion of a pseudoexon between exons 63 and 64. Sequencing of intron 63 identified a point mutation, IVS63+373, located near the middle of intron 63 of FBN1 that created a donor splice site in intron 63, leading to inclusion of a 93-bp fragment of intronic sequence in the FBN1 message. Identification of a novel pseudoexon mutation in FBN1, in association with a clinical diagnosis of MFS, confirms that cryptic mutations that are missed by the current DNA-based diagnostic methods have a causative role.