Publications by authors named "Claus B Pedersen"

3 Publications

  • Page 1 of 1

Finite deformation elastography of articular cartilage and biomaterials based on imaging and topology optimization.

Sci Rep 2020 05 14;10(1):7980. Epub 2020 May 14.

Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, 47907, US.

Tissues and engineered biomaterials exhibit exquisite local variation in stiffness that defines their function. Conventional elastography quantifies stiffness in soft (e.g. brain, liver) tissue, but robust quantification in stiff (e.g. musculoskeletal) tissues is challenging due to dissipation of high frequency shear waves. We describe new development of finite deformation elastography that utilizes magnetic resonance imaging of low frequency, physiological-level (large magnitude) displacements, coupled to an iterative topology optimization routine to investigate stiffness heterogeneity, including spatial gradients and inclusions. We reconstruct 2D and 3D stiffness distributions in bilayer agarose hydrogels and silicon materials that exhibit heterogeneous displacement/strain responses. We map stiffness in porcine and sheep articular cartilage deep within the bony articular joint space in situ for the first time. Elevated cartilage stiffness localized to the superficial zone is further related to collagen fiber compaction and loss of water content during cyclic loading, as assessed by independent T measurements. We additionally describe technical challenges needed to achieve in vivo elastography measurements. Our results introduce new functional imaging biomarkers, which can be assessed nondestructively, with clinical potential to diagnose and track progression of disease in early stages, including osteoarthritis or tissue degeneration.
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May 2020

Lower risk of hypoglycemia with insulin detemir than with neutral protamine hagedorn insulin in older persons with type 2 diabetes: a pooled analysis of phase III trials.

J Am Geriatr Soc 2007 Nov;55(11):1735-40

Division of Diabetes, Endocrinology and Metabolism, Baylor College of Medicine, Houston, Texas, USA.

Objectives: To compare the safety and efficacy of insulin detemir with that of neutral protamine Hagedorn (NPH) insulin in older (aged >/=65) and younger (aged 18-64) persons with type 2 diabetes mellitus (DM).

Design: Pooled, post hoc analysis of data from three open-label, randomized studies.

Setting: Three multinational Phase III trials.

Participants: Four hundred sixteen older and 880 younger persons with DM, treated for 22 to 26 weeks with basal insulin plus mealtime insulin or oral agents.

Measurements: Hemoglobin A(1c) (HbA(1c)), fasting plasma glucose, glucose variability, hypoglycemic episodes.

Results: Mean treatment difference for HbA(1c) (insulin detemir-NPH insulin) indicated that insulin detemir was not inferior to NPH insulin for both age groups (0.035%, 95% confidence interval (CI)=-0.114-0.183 and 0.100%, 95% CI=-0.017-0.217, for older and younger persons, respectively). Relative risk of all hypoglycemic episodes (insulin detemir/NPH insulin) was 0.59 (95% CI-0.42-0.83) for older persons and 0.75 (95% CI-0.59-0.96) for younger persons. Adverse events were similar between treatments. Fasting plasma glucose was similar between treatments (mean treatment difference 0.97 mg/dL, 95% CI=-8.01-9.95, and 4.69 mg/dL, 95% CI=-2.30-11.67, for older and younger persons, respectively). Mean treatment difference for weight was -1.02 kg (95% CI -1.61 to -0.42) and -1.13 (95% CI -1.58 to -0.69) for older and younger persons, respectively.

Conclusion: Previously reported benefits of insulin detemir, particularly less hypoglycemia and less weight gain, compared with NPH insulin, were the same for older and younger persons with DM at similar levels of HbA(1c).
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November 2007

Novel analytical methods applied to type 1 diabetes genome-scan data.

Am J Hum Genet 2004 Apr 11;74(4):647-60. Epub 2004 Mar 11.

Steno Diabetes Center, Gentofte, Denmark.

Complex traits like type 1 diabetes mellitus (T1DM) are generally taken to be under the influence of multiple genes interacting with each other to confer disease susceptibility and/or protection. Although novel methods are being developed, analyses of whole-genome scans are most often performed with multipoint methods that work under the assumption that multiple trait loci are unrelated to each other; that is, most models specify the effect of only one locus at a time. We have applied a novel approach, which includes decision-tree construction and artificial neural networks, to the analysis of T1DM genome-scan data. We demonstrate that this approach (1) allows identification of all major susceptibility loci identified by nonparametric linkage analysis, (2) identifies a number of novel regions as well as combinations of markers with predictive value for T1DM, and (3) may be useful in characterizing markers in linkage disequilibrium with protective-gene variants. Furthermore, the approach outlined here permits combined analyses of genetic-marker data and information on environmental and clinical covariates.
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April 2004