Pinealectomy affects bone mineral density and structure - an experimental study in sheep

<p>Abstract</p> <p>Background</p> <p>Osteoporosis and associated fractures are a major public health burden and there is great need for a large animal model. Melatonin, the hormone of the pineal gland, has been shown to influence bone metabolism. This study aims to evaluate whether absence of melatonin due to pinealectomy affects the bone mass, structure and remodeling in an ovine animal model.</p> <p>Methods</p> <p>Female sheep were arranged into four groups: Control, surgically ovariectomized (Ovx), surgically pinealectomized (Px) and Ovx+Px. Before and 6 months after surgery, iliac crest biopsies were harvested and structural parameters were measured using μCT. Markers of bone formation and resorption were determined. To evaluate long term changes after pinealectomy, bone mineral density (BMD) was analyzed at the distal radius at 0, 3, 9, 18 and 30 months.</p> <p>Results</p> <p>Cancellous bone volume (BV/TV) declined after 6 months by -13.3% Px and -21.5% OvxPx. The bone loss was due to increased trabecular separation as well as decreased thickness. The histomorphometric quantification and determination of collagen degradation products showed increased bone resorption following pinealectomy. Ovariectomy alone results in a transient bone loss at the distal radius followed by continuous increase to baseline levels. The bone resorption activity after pinealectomy causes a bone loss which was not transient, since a continuous decrease in BMD was observed until 30 months.</p> <p>Conclusions</p> <p>The changes after pinealectomy in sheep are indicative of bone loss. Overall, these findings suggest that the pineal gland may influence bone metabolism and that pinealectomy can be used to induce bone loss in sheep.</p

Label-Free Detection of Neuronal Differentiation in Cell Populations Using High-Throughput Live-Cell Imaging of PC12 Cells

Detection of neuronal cell differentiation is essential to study cell fate decisions under various stimuli and/or environmental conditions. Many tools exist that quantify differentiation by neurite length measurements of single cells. However, quantification of differentiation in whole cell populations remains elusive so far. Because such populations can consist of both proliferating and differentiating cells, the task to assess the overall differentiation status is not trivial and requires a high-throughput, fully automated approach to analyze sufficient data for a statistically significant discrimination to determine cell differentiation. We address the problem of detecting differentiation in a mixed population of proliferating and differentiating cells over time by supervised classification. Using nerve growth factor induced differentiation of PC12 cells, we monitor the changes in cell morphology over [Image: see text] days by phase-contrast live-cell imaging. For general applicability, the classification procedure starts out with many features to identify those that maximize discrimination of differentiated and undifferentiated cells and to eliminate features sensitive to systematic measurement artifacts. The resulting image analysis determines the optimal post treatment day for training and achieves a near perfect classification of differentiation, which we confirmed in technically and biologically independent as well as differently designed experiments. Our approach allows to monitor neuronal cell populations repeatedly over days without any interference. It requires only an initial calibration and training step and is thereafter capable to discriminate further experiments. In conclusion, this enables long-term, large-scale studies of cell populations with minimized costs and efforts for detecting effects of external manipulation of neuronal cell differentiation

3C 129 at 90cm: Evidence for a radio relic?

SIGLEAvailable from: http://www.mpa-garching.mpg.de / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman