Dataset Information

Quantitative Histomorphometry of the Healthy Peritoneum.

ABSTRACT: The peritoneum plays an essential role in preventing abdominal frictions and adhesions and can be utilized as a dialysis membrane. Its physiological ultrastructure, however, has not yet been studied systematically. 106 standardized peritoneal and 69 omental specimens were obtained from 107 patients (0.1-60 years) undergoing surgery for disease not affecting the peritoneum for automated quantitative histomorphometry and immunohistochemistry. The mesothelial cell layer morphology and protein expression pattern is similar across all age groups. Infants below one year have a thinner submesothelium; inflammation, profibrotic activity and mesothelial cell translocation is largely absent in all age groups. Peritoneal blood capillaries, lymphatics and nerve fibers locate in three distinct submesothelial layers. Blood vessel density and endothelial surface area follow a U-shaped curve with highest values in infants below one year and lowest values in children aged 7-12 years. Lymphatic vessel density is much lower, and again highest in infants. Omental blood capillary density correlates with parietal peritoneal findings, whereas only few lymphatic vessels are present. The healthy peritoneum exhibits major thus far unknown particularities, pertaining to functionally relevant structures, and subject to substantial changes with age. The reference ranges established here provide a framework for future histomorphometric analyses and peritoneal transport modeling approaches.

SUBMITTER: Schaefer B

PROVIDER: S-EPMC4763997 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Quantitative Histomorphometry of the Healthy Peritoneum.

Schaefer Betti B Bartosova Maria M Macher-Goeppinger Stephan S Ujszaszi Akos A Wallwiener Markus M Nyarangi-Dix Joanne J Sallay Peter P Burkhardt Dorothea D Querfeld Uwe U Pfeifle Viktoria V Lahrmann Bernd B Schwenger Vedat V Wühl Elke E Holland-Cunz Stefan S Schaefer Franz F Schmitt Claus P CP

Scientific reports 20160219

The peritoneum plays an essential role in preventing abdominal frictions and adhesions and can be utilized as a dialysis membrane. Its physiological ultrastructure, however, has not yet been studied systematically. 106 standardized peritoneal and 69 omental specimens were obtained from 107 patients (0.1-60 years) undergoing surgery for disease not affecting the peritoneum for automated quantitative histomorphometry and immunohistochemistry. The mesothelial cell layer morphology and protein expre ...[more]

PMID: 26905058

Similar Datasets

Project description:White matter lesions represent a major risk factor for dementia in elderly people. Magnetic Resonance Imaging (MRI) studies have demonstrated cerebral blood flow reduction in age-related white matter lesions, indicating that vascular alterations are involved in developing white matter lesions. Hypoperfusion and changes in capillary morphology are generally linked to dementia. However, a quantitative study describing these microvascular alterations in white matter lesions is missing in the literature; most previous microvascular studies being on the cortex. The aim of this work is to identify and quantify capillary microstructural changes involved in the appearance of deep subcortical lesions (DSCL). We characterize the distribution of capillary diameter, thickness, and density in the deep white matter in a population of 75 elderly subjects, stratified into three equal groups according to DSCL: Control (subject without DSCL), Lesion (sample presenting DSCL), and Normal Appearing White Matter (NAWM, the subject presented DSCL but not at the sampled tissue location). Tissue samples were selected from the Cognitive Function and Aging Study (CFAS), a cohort representative of an aging population, from which immunohistochemically-labeled histological images were acquired. To accurately estimate capillary diameters and thicknesses from the 2D histological images, we also introduce a novel semi-automatic method robust to non-perpendicular incidence angle of capillaries into the imaging plane, and to non-circular deformations of capillary cross sections. Subjects with DSCL presented a significant increase in capillary wall thickness, a decrease in the diameter intra-subject variability (but not in the mean), and a decrease in capillary density. No significant difference was observed between controls and NAWM. Both capillary wall thickening and reduction in capillary density contribute to the reduction of cerebral blood flow previously reported for white matter lesions. The obtained distributions provide reliable statistics of capillary microstructure useful to inform the modeling of human cerebral blood flow, for instance to define microcirculation models for their estimation from MRI or to perform realistic cerebral blood flow simulations.

Project description:Molecular subtypes of medulloblastoma [Sonic Hedgehog (SHH), Wingless/INT (WNT), Group 3, and Group 4] are defined by common patterns of gene expression. These differential gene expression patterns appear to result in different histomorphology and prognosis. Quantitative histomorphometry is a well-known method of computer-aided pathology image analysis. The hypotheses we sought to examine in this preliminary proof of concept study were whether computer extracted nuclear morphological features of medulloblastomas from digitized tissue slide images could independently: (1) distinguish between molecularly determined subgroups and (2) identify patterns within these subgroups that correspond with clinical outcome. Our dataset was composed of 46 medulloblastoma patients: 16 SHH (5 dead, 11 survived), 3 WNT (0 dead, 3 survived), 12 Group 3 (4 dead, 8 survived), and 15 were Group 4 (5 dead, 10 survived). A watershed-based thresholding scheme was used to automatically identify individual nuclei within digitized whole slide hematoxylin and eosin tissue images. Quantitative histomorphometric features corresponding to the texture (variation in pixel intensity), shape (variations in size, roundness), and architectural rearrangement (distances between, and number of connected neighbors) of nuclei were subsequently extracted. These features were ranked using feature selection schemes and these top-ranked features were then used to train machine-learning classifiers via threefold cross-validation to separate patients based on: (1) molecular subtype and (2) disease-specific outcomes within the individual molecular subtype groups. SHH and WNT tumors were separated from Groups 3 and 4 tumors with a maximum area under the receiver operating characteristic curve (AUC) of 0.7, survival within Group 3 tumors was predicted with an AUC of 0.92, and Group 3 and 4 patients were separated into high- and low-risk groups with p = 0.002. Model prediction was quantitatively compared with age, stage, and histological subtype using univariate and multivariate Cox hazard ratio models. Age was the most statistically significant variable for predicting survival in Group 3 and 4 tumors, but model predictions had the highest hazard ratio value. Quantitative nuclear histomorphometry can be used to study medulloblastoma genetic expression phenotypes as it may distinguish meaningful features of disease pathology.

Project description:BackgroundGene-expression companion diagnostic tests, such as the Oncotype DX test, assess the risk of early stage Estrogen receptor (ER) positive (+) breast cancers, and guide clinicians in the decision of whether or not to use chemotherapy. However, these tests are typically expensive, time consuming, and tissue-destructive.MethodsIn this paper, we evaluate the ability of computer-extracted nuclear morphology features from routine hematoxylin and eosin (H&E) stained images of 178 early stage ER+ breast cancer patients to predict corresponding risk categories derived using the Oncotype DX test. A total of 216 features corresponding to the nuclear shape and architecture categories from each of the pathologic images were extracted and four feature selection schemes: Ranksum, Principal Component Analysis with Variable Importance on Projection (PCA-VIP), Maximum-Relevance, Minimum Redundancy Mutual Information Difference (MRMR MID), and Maximum-Relevance, Minimum Redundancy - Mutual Information Quotient (MRMR MIQ), were employed to identify the most discriminating features. These features were employed to train 4 machine learning classifiers: Random Forest, Neural Network, Support Vector Machine, and Linear Discriminant Analysis, via 3-fold cross validation.ResultsThe four sets of risk categories, and the top Area Under the receiver operating characteristic Curve (AUC) machine classifier performances were: 1) Low ODx and Low mBR grade vs. High ODx and High mBR grade (Low-Low vs. High-High) (AUC = 0.83), 2) Low ODx vs. High ODx (AUC = 0.72), 3) Low ODx vs. Intermediate and High ODx (AUC = 0.58), and 4) Low and Intermediate ODx vs. High ODx (AUC = 0.65). Trained models were tested independent validation set of 53 cases which comprised of Low and High ODx risk, and demonstrated per-patient accuracies ranging from 75 to 86%.ConclusionOur results suggest that computerized image analysis of digitized H&E pathology images of early stage ER+ breast cancer might be able predict the corresponding Oncotype DX risk categories.

Project description:Dynamic bone histomorphometry is the standard method for measuring bone remodeling at the level of individual events. Although dynamic bone histomorphometry is an invaluable tool for understanding osteoporosis and other metabolic bone diseases, the technique's two-dimensional nature requires the use of stereology and prevents measures of individual remodeling event number and size. Here, we used a novel three-dimensional fluorescence imaging technique to achieve measures of individual resorption cavities and formation events. We performed this three-dimensional histomorphometry approach using a common model of postmenopausal osteoporosis, the ovariectomized rat. The three-dimensional images demonstrated the spatial relationship between resorption cavities and formation events consistent with the hemiosteonal model of cancellous bone remodeling. Established ovariectomy was associated with significant increases in the number of resorption cavities per unit bone surface (2.38 ± 0.24 mm⁻² sham surgery versus 3.86 ± 0.35 mm⁻² bilateral ovariectomy [OVX], mean ± SD, p < 0.05) and total volume occupied by cavities per unit bone volume (0.38% ± 0.06% sham versus 1.12% ± 0.18% OVX, p < 0.001), but there was no difference in surface area per resorption cavity, maximum cavity depth, or cavity volume. In addition, we found that established ovariectomy is associated with increased size of bone formation events because of the merging of formation events (23,700 ± 6,890 µm² sham verusus 33,300 ± 7,950 µm² OVX). No differences in mineral apposition rate (determined in 3D) were associated with established ovariectomy. That established estrogen depletion is associated with increased number of remodeling events with only subtle changes in remodeling event size suggests that circulating estrogens may have their primary effect on the origination of new basic multicellular units with relatively little effect on the progression and termination of active remodeling events.

Project description:Background and objectivesOver the past decade, the management of CKD-mineral and bone disorder has changed substantially, altering the pattern of bone disease in CKD. We aimed to evaluate the natural history of kidney bone disease in contemporary kidney transplant recipients and patients on dialysis.Design, settings, participants, & measurementsSixty one patients on dialysis who were referred to kidney transplantation participated in this prospective cohort study during November 2009 and December 2010. We performed baseline bone biopsies while the patients were on dialysis and repeated the procedure in 56 patients at 2 years after kidney transplantation or 2 years after baseline if transplantation was not performed. Measurements of mineral metabolism and bone turnover, as well as dual energy x-ray absorptiometry scans, were obtained concurrently.ResultsA total of 37 out of 56 participants received a kidney transplant, of which 27 underwent successful repeat bone biopsy. The proportion of patients with high bone turnover declined from 63% at baseline to 19% at 2 years after kidney transplantation, whereas the proportion of those with low bone turnover increased from 26% to 52%. Of 19 participants remaining on dialysis after 2 years, 13 underwent successful repeat biopsy. The proportion of patients remaining on dialysis with high bone turnover decreased from 69% to 31%, and low bone turnover increased from 8% to 38%. Abnormal bone mineralization increased in transplant recipients from 33% to 44%, but decreased in patients remaining on dialysis from 46% to 15%. Trabecular bone volume showed little change after transplantation, but low bone volume increased in patients remaining on dialysis. Bone mineral density did not correlate with histomorphometric findings.ConclusionsBone turnover decreased over time both in patients remaining on dialysis and in kidney transplant recipients. Bone mineral density and bone biomarkers were not associated with bone metabolism changes detected in bone biopsy specimens.

Dataset Information

Quantitative Histomorphometry of the Healthy Peritoneum.

Publications

Quantitative Histomorphometry of the Healthy Peritoneum.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets