Insulinlike Growth Factor 1- and 2-Augmented Collagen Gel Repair of Facial Osseous Defects

BACKGROUND: Defects of the facial bone structure are common problems for the facial plastic surgeon. Native type 1 collagen gels (T1CGs) have been shown to mediate repair of facial critical-size defects in rat models. OBJECTIVE: To evaluate the efficacy of T1CG augmented with insulinlike growth factor (IGF) 1, IGF-2, and a combination of IGF-1 and IGF-2 on the repair of facial critical-size defects in a rodent model. METHODS: Twenty-four retired male breeder Sprague-Dawley rats were divided into 4 groups of 6 animals. Facial critical-size defects were created by removing the nasalis bones with a bone-cutting drill. Defects were treated with 300 pg of type 1 collagen gel (T1CG), T1CG augmented with 3 microg of IGF-1, T1CG augmented with 3 microg of IGF-2, or T1CG augmented with a combination of 3 microg of IGF-1 and 3 microg of IGF-2. After 30 days the animals were examined at necropsy with precise planimetry, histological analysis of new bone growth, and radiodensitometric analysis of bone thickness. RESULTS: Radiodensitometric measurements showed that IGF-2 augmentation resulted in greatest osseous healing, with measurements being statistically significant over those of all other groups (P\u3c or = .03). Combination IGF-1 and IGF-2 had osseous healing that was intermediate between IGF-1 augmentation and IGF-2 augmentation alone, with measurements being statistically significant over those of unaugmented gels (P CONCLUSION: Collagen gels augmented with IGF significantly enhance the osteoconductive repair of nasal critical-size defects in a rodent model, with IGF-2 showing highest efficacy

GENE-Counter: A Computational Pipeline for the Analysis of RNA-Seq Data for Gene Expression Differences

GENE-counter is a complete Perl-based computational pipeline for analyzing RNA-Sequencing (RNA-Seq) data for differential gene expression. In addition to its use in studying transcriptomes of eukaryotic model organisms, GENE-counter is applicable for prokaryotes and non-model organisms without an available genome reference sequence. For alignments, GENE-counter is configured for CASHX, Bowtie, and BWA, but an end user can use any Sequence Alignment/Map (SAM)-compliant program of preference. To analyze data for differential gene expression, GENE-counter can be run with any one of three statistics packages that are based on variations of the negative binomial distribution. The default method is a new and simple statistical test we developed based on an over-parameterized version of the negative binomial distribution. GENE-counter also includes three different methods for assessing differentially expressed features for enriched gene ontology (GO) terms. Results are transparent and data are systematically stored in a MySQL relational database to facilitate additional analyses as well as quality assessment. We used next generation sequencing to generate a small-scale RNA-Seq dataset derived from the heavily studied defense response of Arabidopsis thaliana and used GENE-counter to process the data. Collectively, the support from analysis of microarrays as well as the observed and substantial overlap in results from each of the three statistics packages demonstrates that GENE-counter is well suited for handling the unique characteristics of small sample sizes and high variability in gene counts