Mechanical Cell-Matrix Feedback Explains Pairwise and Collective Endothelial Cell Behavior In Vitro

During the embryonic development of multicellular organisms, millions of cells cooperatively build structured tissues, organs and whole organisms, a process called morphogenesis. How the behavior of so many cells is coordinated to produce complex structures is still incompletely understood. Most biomedical research focuses on the molecular signals that cells exchange with one another. It has now become clear that cells also communicate biomechanically during morphogenesis. In cell cultures, endothelial cells—the building blocks of blood vessels—can organize into structures resembling networks of capillaries. Experimental work has shown that the endothelial cells pull onto the protein gel that they live in, called the extracellular matrix. On sufficiently compliant matrices, the strains resulting from these cellular pulling forces slow down and reorient adjacent cells. Here we propose a new computational model to show that this simple form of mechanical cell-cell communication suffices for reproducing the formation of blood vessel-like structures in cell cultures. These findings advance our understanding of biomechanical signaling during morphogenesis, and introduce a new set of computational tools for modeling mechanical interactions between cells and the extracellular matrix

The On-orbit Calibrations for the Fermi Large Area Telescope

The Large Area Telescope (LAT) on--board the Fermi Gamma ray Space Telescope began its on--orbit operations on June 23, 2008. Calibrations, defined in a generic sense, correspond to synchronization of trigger signals, optimization of delays for latching data, determination of detector thresholds, gains and responses, evaluation of the perimeter of the South Atlantic Anomaly (SAA), measurements of live time, of absolute time, and internal and spacecraft boresight alignments. Here we describe on orbit calibration results obtained using known astrophysical sources, galactic cosmic rays, and charge injection into the front-end electronics of each detector. Instrument response functions will be described in a separate publication. This paper demonstrates the stability of calibrations and describes minor changes observed since launch. These results have been used to calibrate the LAT datasets to be publicly released in August 2009.Comment: 60 pages, 34 figures, submitted to Astroparticle Physic

Urinary pentosidine does not predict cartilage loss among subjects with symptomatic knee OA: the BOKS Study

SummaryObjectiveAge-related changes in articular cartilage are likely to play a role in the etiology of osteoarthritis (OA). One of the major changes in the extracellular matrix of cartilage is the age-related accumulation of advanced glycation end products (AGEs). Pentosidine, an AGE crosslink, is one of the few characterized AGEs and is considered an adequate marker for the many AGEs that are formed in vivo. We used data from a longitudinal observation study to determine if urinary pentosidine could serve as a marker to predict cartilage loss.MethodsWe conducted a prospective analysis of data from the Boston Osteoarthritis of the Knee Study (BOKS); a completed natural history study of knee OA. All subjects in the study met American College of Rheumatology (ACR) criteria for knee OA. Knee magnetic resonance (MR) images were scored for cartilage in 14 plates of the knee using the Whole Organ Magnetic Resonance Imaging Score (WORMS) semiquantitative grading scheme. Within the BOKS population, a nested sample of 127 subjects (39% of the whole sample) who had both baseline pentosidine and longitudinal magnetic resonance imaging (MRI) measurements (MRIs performed at baseline and 30 months later) was assessed. Urinary pentosidine was assayed and normalized to creatinine to account for differences in urine concentrations. We analyzed the data using three different methods to assess if baseline measures of pentosidine predicted subsequent cartilage loss on MRI. These were (1) analysis 1: logistic regression with the outcome cartilage loss in any plate; (2) analysis 2: proportional odds model where the outcome was defined as 0=no cartilage loss, 1=cartilage loss in one plate, 2=cartilage loss in two plates, and 3=cartilage loss in at least three plates; and (3) analysis 3: Poisson regression with the outcome the number of plates with cartilage loss. All analyses were adjusted for age, sex and Body Mass Index (BMI).ResultsAt baseline the mean (standard deviation) age was 67 (9) years and 54% were male. The results for the three analytic steps are as follows: Analysis 1: the odds ratio for cartilage loss is 1.01 (95% confidence interval (CI) 0.93–1.09) with 1 unit increase in pentosidine. Analysis 2: the odds ratio for more cartilage loss is 0.99 (95% CI 0.92–1.06) with 1 unit increase in pentosidine. Analysis 3: the relative number of plates with cartilage loss decreased was 1.00 (95% CI 0.95–1.03) with a 1 unit increase in pentosidine.ConclusionUrinary pentosidine does not predict knee cartilage loss. Previous studies have suggested that local content within cartilage of AGEs is elevated in persons at high risk for progression. Our data suggest that these changes are not measurable systemically. Alternatively, urinary pentosidine levels reflect cartilage degradation in all joints (thus whole body cartilage breakdown) and may therefore not relate to OA severity in a single knee joint

Biochemical markers of bone turnover and their association with bone marrow lesions

Introduction: Our objective was to determine whether markers of bone resorption and formation could serve as markers for the presence of bone marrow lesions (BMLs). Methods: We conducted an analysis of data from the Boston Osteoarthritis of the Knee Study (BOKS). Knee magnetic resonance images were scored for BMLs using a semiquantitative grading scheme. In addition, a subset of persons with BMLs underwent quantitative volume measurement of their BML, using a proprietary software method. Within the BOKS population, 80 people with BMLs and 80 without BMLs were selected for the purposes of this case-control study. Bone biomarkers assayed included type I collagen N-telopeptide (NTx) corrected for urinary creatinine, bone-specific alkaline phosphatase, and osteocalcin. The same methods were used and applied to a nested case-control sample from the Framingham study, in which BMD assessments allowed evaluation of this as a covariate. Logistic regression models were fit using BML as the outcome and biomarkers, age, sex, and body mass index as predictors. An receiver operating characteristic curve was generated for each model and the area under the curve assessed. Results: A total of 151 subjects from BOKS with knee OA were assessed. The mean (standard deviation) age was 67 (9) years and 60% were male. Sixty-nine per cent had maximum BML score above 0, and 48% had maximum BML score above 1. The only model that reached statistical significance used maximum score of BML above 0 as the outcome. Ln-NTx (Ln is the natural log) exhibited a significant association with BMLs, with the odds of a BML being present increasing by 1.4-fold (95% confidence interval = 1.0-fold to 2.0-fold) per 1 standard deviation increase in the LnNTx, and with a small partial R2 of 3.05. We also evaluated 144 participants in the Framingham Osteoarthritis Study, whose mean age was 68 years and body mass index was 29 kg/m2, and of whom 40% were male. Of these participants 55% had a maximum BML score above 0. The relationship between NTx and maximum score of BML above 0 revealed a significant association, with an odds ratio fo 1.7 (95% confidence interval = 1.1 to 2.7) after adjusting for age, sex, and body mass index. Conclusions: Serum NTx was weakly associated with the presence of BMLs in both study samples. This relationship was not strong and we would not advocate the use of NTx as a marker of the presence of BMLs. © 2008 Hunter et al.; licensee BioMed Central Ltd. Chemicals / CAS: alkaline phosphatase, 9001-78-9; creatinine, 19230-81-0, 60-27-5; osteocalcin, 136461-80-8; Alkaline Phosphatase, EC 3.1.3.1; Biological Markers; collagen type I trimeric cross-linked peptide; Collagen Type I; Osteocalcin, 104982-03-8; Peptide