FUNCTIONAL DATA ANALYSIS OF THE KINEMATICS OF GAIT IN SUBJECTS WITH A HISTORY OF ACHILLES TENDON INJURY

Clinical biomechanics research aims to understand the mechanisms of injury to improve prediction, prevention and rehabilitation. Dynamical systems theory suggests that coordination and variability may be key issues in the development of injuries. Traditional analysis has relied on a multivariate approach using discrete measures during stance. This essentially discards kinematic data obtained throughout the entire stance phase. Functional data analysis is an established statistical technique that is now emerging in biomechanics. It views the data as a function, thus using the entire time series data and determines which factors contribute to the variation. The purpose of this study was to examine the predictions of dynamical systems theory on angular kinematic data in subjects with Achilles tendon injury using a functional data analysis approach

Analyzing time-course microarray data using functional data analysis - a review

Gene expression over time can be viewed as a continuous process and therefore represented as a continuous curve or function. Functional data analysis (FDA) is a statistical methodology used to analyze functional data that has become increasingly popular in the analysis of time-course gene expression data. Several FDA techniques have been applied to gene expression profiles including functional regression analysis (to describe the relationship between expression profiles and other covariate(s)), functional discriminant analysis (to discriminate and classify groups of genes) and functional principal components analysis (for dimension reduction and clustering). This paper reviews the use of FDA and it¿s associated methods to analyze time-course microarray gene expression data

Analyzing Time-Course Microarray Data Using Functional Data Analysis - A Review

Gene expression over time can be viewed as a continuous process and therefore represented as a continuous curve or function. Functional data analysis (FDA) is a statistical methodology used to analyze functional data that has become increasingly popular in the analysis of time-course gene expression data. Several FDA techniques have been applied to gene expression profiles including functional regression analysis (to describe the relationship between expression profiles and other covariate(s)), functional discriminant analysis (to discriminate and classify groups of genes) and functional principal components analysis (for dimension reduction and clustering). This paper reviews the use of FDA and its associated methods to analyze time-course microarray gene expression data.

Experimental approaches to the prevention of islet rejection

Chapter 21 focuses on experimental studies of allogeneic islet cell transplantation, with an emphasis on recent advances in the prevention of rejection, as exemplified by studies in rodents and nonhuman primates (NHP)

Predictors of quality of life gains among people with type 1 diabetes participating in the Dose Adjustment for Normal Eating (DAFNE) structured education programme

Journal articleAims: To examine predictors of quality of life gains among people with type 1 diabetes following the Dose Adjustment for Normal Eating (DAFNE) self-management training programme.Methods: Clinical and questionnaire data were collected from 437 patients from 6 hospital centres before, and 18 months post-DAFNE intervention. Glycated haemoglobin (HbA(1c)), weight, height, and blood pressure levels were recorded by clinicians during clinic appointments. Questionnaires included the Diabetes-Specific Quality of Life Scale (DSQOLS), the Problem Areas in Diabetes Scale (PAID) and the Hospital Anxiety and Depression Scale (HADS). Basic demographics were recorded at baseline. Linear mixed models were fitted to identify predictors of change in quality of life at an 18 month follow-up assessment.Results: Patients with high levels of diabetes-related distress experienced greatest improvement in DSQOLS quality of life scores (p = 0.001). Patients with poor glycaemic control (higher levels of HbA(1c); p = 0.03) and those with high levels of anxiety (p = 0.001) experienced the greatest reductions in diabetes-related distress.Conclusions: Patients with higher baseline levels of anxiety, higher levels of diabetes-related distress and higher baseline levels of HbA(1c) are most likely to experience quality of life gain from participation in self-management programmes such as DAFNE. (C) 2012 Elsevier Ireland Ltd. All rights reserved.HRB Irelan

Peripheral Blood Single-Cell Genotyping and Phenotyping in Multiple Myeloma Reveals Shared Mutations across Multiple Hematopoietic Cell Lineages

Abstract Background: Multiple myeloma (MM) is a hematologic malignancy of terminally differentiated plasma cells. While most genomic studies of the disease have focused on characterizing isolated bone marrow plasma cells, few have addressed somatic mutations within the immune microenvironment. We previously demonstrated that somatic mutations are significantly more numerous in unsorted blood compared to bone marrow and that the number of mutations correlated with elevated markers of disease. We also reported that genes implicated in clonal hematopoiesis of indeterminate potential (CHIP) are commonly mutated in the blood from patients with MM. Aim: To integrate single-cell DNA mutation analysis and protein expression to map the clonal relationship between circulating plasma cells and non-plasma cells within the peripheral blood of patients with MM. Methods: Although bulk DNA sequencing can estimate clonal heterogeneity using analytic deconvolution techniques, it is unable to distinguish which mutations occur in the same clone(s) nor can it correlate genotype with immunophenotype from the same cell. To accurately describe the clonal architecture of the blood, we combined targeted single-cell DNA sequencing (scDNA-seq) coupled with protein expression to define mutations specific to cell phenotypes using the Tapestri Platform (Mission Bio). For this analysis, we developed a custom amplicon panel covering 22 frequently mutated genes in MM and/or CHIP to perform scDNA-seq and 4 antibodies targeting CD138, CD19, CD3, and CD33 for immunophenotyping or plasma cells, B cells, T cells, and myeloid cells respectively. Results: Peripheral blood mononuclear cells were isolated from 17 blood samples collected from 12 patients with MM. Three samples were collected at diagnosis before treatment, 8 were collected post-treatment during remission, and 6 were collected at relapse. Serial samples were obtained before and after autologous stem cell transplant from 4 patients. A total of 85,262 cells were sequenced uncovering 154 unique variants in 22 genes after quality filtering. There were 47 somatic variants, defined as those present in less than 40% of cells per sample. There were 52,136 total clones, defined as cells with identical genotypes, within this heterogenous population of cells. The most common non-synonymous somatic mutations were identified in genes SETD2, KMT2C, PPM1D. An immunophenotype was assigned to 82% of cells according to the protein with the most abundant number of reads. Dimensionality reduction of single-cell genotype using t-distributed stochastic neighbor embedding (t-SNE) effectively clustered cells according to sample and by patient in cases where there were multiple samples from the same individual. Patients with active myeloma and known circulating plasma cells detected by clinical flow cytometry were found to have the greatest proportion of CD138+ cells as well as the highest mutational burden. It was rare to detect the same clone in two separate samples collected from the same patient at different time points (median time be collection was 248 days) which may be due to under-sampling or clonal evolution. Surprisingly, all somatic variants detected in plasma cells, were also present in other cell types suggesting they were acquired from a common progenitor cell rather than after differentiation. Conclusions: scDNA-seq coupled with immunophenotyping of the peripheral blood of patients with MM reveals somatic variants are shared across multiple hematopoietic cell lineages including plasma cells indicating these mutational are acquired from a common progenitor. Disclosures Durruthy-Durruthy: Mission Bio Inc.: Current Employment. Arribas-Layton: Mission Bio, Inc.: Current Employment. Wang: Mission Bio, Inc: Current Employment

Long-term survival of nonhuman primate islets implanted in an omental pouch on a biodegradable scaffold

The aim of this study was to test whether an omental pouch can be used as an alternative site for islet implantation in diabetic monkeys. Here we report the successful engraftment of islets in diabetic cynomolgus monkeys when loaded on a synthetic biodegradable scaffold and placed in an omental pouch. One autologous and 5 allogeneic diabetic monkey transplants under the cover of steroid-free immune suppression were undertaken. Fasting blood glucose (FBG) and c-peptide (CP), exogenous insulin requirements (EIR), intravenous glucose tolerance test (IVGTT), A1C and histopathology were used to assess islet engraftment and survival. All animals achieved CP levels > 1.0 ng/ml following transplant, a 66 – 92% post-transplant decrease in EIR and reduced A1C. Following graft removal, CP became negative and histopathological analysis of the explanted grafts demonstrated well granulated and well vascularized, insulin positive islets, surrounded by T cell subsets and macrophages. Compared to intrahepatic allogeneic islet transplants (n=20), there was a delayed engraftment for omental pouch recipients but similar levels of CP production were ultimately achieved, with a broad range of IEQ/kg transplanted in both sites. Our results suggest this extrahepatic transplantation site has potential as an alternative site for clinical islet cell transplantation