Meninges Outside the Meninges: Ectopic Meningiomas and Meningothlelial Proliferations

Extracranial meningiomas have been reported for decades now and have been described in the head and neck; calvarial, nasal cavity, paranasal sinuses, nasopharynx, parotid gland and in various remote anatomical locations systemically. The presence of microanatomical structures for all intents and purposes resembling and having the histopathological characteristics of meninges outside of the central nervous system meninges is uncommon but well-documented. Typically, these lesions are found in the lung or part of hamartomatous/choristomatous lesions and frequently occur in the head and neck anatomical region. The lesion first described by Suster and Rosai termed "hamartoma of the scalp with ectopic meningothelial elements" is the prototypical example of lesions with meningothelial elements. We have described recently a similar hamartomatous lesion with meningothelial elements occurring in the tongue. In this chapter, we will review the clinicopathological features of ectopic meningiomas and lesions that contain meningothelial elements and their possible pathogenesis

A General System for Automatic Biomedical Image Segmentation Using Intensity Neighborhoods

Image segmentation is important with applications to several problems in biology and medicine. While extensively researched, generally, current segmentation methods perform adequately in the applications for which they were designed, but often require extensive modifications or calibrations before being used in a different application. We describe an approach that, with few modifications, can be used in a variety of image segmentation problems. The approach is based on a supervised learning strategy that utilizes intensity neighborhoods to assign each pixel in a test image its correct class based on training data. We describe methods for modeling rotations and variations in scales as well as a subset selection for training the classifiers. We show that the performance of our approach in tissue segmentation tasks in magnetic resonance and histopathology microscopy images, as well as nuclei segmentation from fluorescence microscopy images, is similar to or better than several algorithms specifically designed for each of these applications

A computer-based automated algorithm for assessing acinar cell loss after experimental pancreatitis

The change in exocrine mass is an important parameter to follow in experimental models of pancreatic injury and regeneration. However, at present, the quantitative assessment of exocrine content by histology is tedious and operatordependent, requiring manual assessment of acinar area on serial pancreatic sections. In this study, we utilized a novel computer-generated learning algorithm to construct an accurate and rapid method of quantifying acinar content. The algorithm works by learning differences in pixel characteristics from input examples provided by human experts. HE-stained pancreatic sections were obtained in mice recovering from a 2-day, hourly caerulein hyperstimulation model of experimental pancreatitis. For training data, a pathologist carefully outlined discrete regions of acinar and non-acinar tissue in 21 sections at various stages of pancreatic injury and recovery (termed the ''ground truth''). After the expert defined the ground truth, the computer was able to develop a prediction rule that was then applied to a unique set of high-resolution images in order to validate the process. For baseline, non-injured pancreatic sections, the software demonstrated close agreement with the ground truth in identifying baseline acinar tissue area with only a difference of 1%±0.05% (p = 0.21). Within regions of injured tissue, the software reported a difference of 2.5%± 0.04% in acinar area compared with the pathologist (p = 0.47). Surprisingly, on detailed morphological examination, the discrepancy was primarily because the software outlined acini and excluded inter-acinar and luminal white space with greater precision. The findings suggest that the software will be of great potential benefit to both clinicians and researchers in quantifying pancreatic acinar cell flux in the injured and recovering pancreas

Nasal Chondromesenchymal Hamartoma: CT and MR Imaging Findings

We report CT and MR imaging findings for a case of nasal chondromesenchymal hamartoma occurring in a 19-month-old boy. A nasal chondromesenchymal hamartoma is a rare benign pediatric hamartoma that can simulate malignancy. Although rare, knowledge of this entity is essential to avoid potentially harmful therapies

Granular cell ameloblastoma: Case report of a particular ameloblastoma histologically resembling oncocytoma

Granular cell ameloblastoma is classified as a histological subtype of solid/multicystic ameloblastoma. Usual granular cell ameloblastoma is histologically characterized by granular changes of stellate-like cells located in the inner portion of the epithelial follicles. Here we report a case of another type of granular cell ameloblastoma, showing predominant anastomosing double-stranded trabeculae of granular cells. This type of granular cell ameloblastoma is extremely rare, and the World Health Organization classification does not contain the entity. We tentatively termed it \u27anastomosing granular cell ameloblastoma\u27 in this report. The present case suggests the importance of differential diagnosis because the histology of \u27anastomosing granular cell ameloblastoma\u27 resembles that of salivary gland oncocytoma rather than that of usual granular cell ameloblastoma. The trabeculae observed in our case continued to the peripheral cells of a small amount of epithelial sheets of plexiform ameloblastoma, and the tumor cells were positive for CK19, which is regarded as an immunohistochemical marker of odontogenic epithelium. Similar to usual granular cell ameloblastoma, the tumor cells had CD68-positive granules. For precise diagnosis of this condition, immunohistochemistry using CK19 and CD68, as well as detailed histological observation, are recommended

Discovery and Validation of a New Class of Small Molecule Toll-Like Receptor 4 (TLR4) Inhibitors

Many inflammatory diseases may be linked to pathologically elevated signaling via the receptor for lipopolysaccharide (LPS), toll-like receptor 4 (TLR4). There has thus been great interest in the discovery of TLR4 inhibitors as potential anti-inflammatory agents. Recently, the structure of TLR4 bound to the inhibitor E5564 was solved, raising the possibility that novel TLR4 inhibitors that target the E5564-binding domain could be designed. We utilized a similarity search algorithm in conjunction with a limited screening approach of small molecule libraries to identify compounds that bind to the E5564 site and inhibit TLR4. Our lead compound, C34, is a 2-acetamidopyranoside (MW 389) with the formula C17H27NO9, which inhibited TLR4 in enterocytes and macrophages in vitro, and reduced systemic inflammation in mouse models of endotoxemia and necrotizing enterocolitis. Molecular docking of C34 to the hydrophobic internal pocket of the TLR4 co-receptor MD-2 demonstrated a tight fit, embedding the pyran ring deep inside the pocket. Strikingly, C34 inhibited LPS signaling ex-vivo in human ileum that was resected from infants with necrotizing enterocolitis. These findings identify C34 and the β-anomeric cyclohexyl analog C35 as novel leads for small molecule TLR4 inhibitors that have potential therapeutic benefit for TLR4-mediated inflammatory diseases. © 2013 Neal et al

Multiresolution identification of germ layer components in teratomas derived from human and nonhuman primate embryonic stem cells

We propose a system for identification of germ layer components in teratomas derived from human and nonhuman primate embryonic stem cells. Tissue regeneration and repair, drug testing and discov-ery, the cure of genetic and developmental syndromes all may rest on the understanding of the biology and behavior of embryonic stem (ES) cells. Within the field of stem cell biology, an ES cell is not con-sidered an ES cell until it can produce a teratoma tumor (the ”gold” standard test); a seemingly disorganized mass of tissue derived from all three embryonic germ layers; ectoderm, mesoderm, and endo-derm. Identification and quantification of tissue types within ter-atomas derived from ES cells may expand our knowledge of abnor-mal and normal developmental programming and the response of ES cells to genetic manipulation and/or toxic exposures. In addition, because of the tissue complexity, identifying and quantifying the tis-sue is tedious and time consuming, but in turn the teratoma provides an excellent biological platform to test robust image analysis algo-rithms. We use a multiresolution (MR) classification system with texture features, as well as develop novel nuclear texture features to recognize germ layer components. With redundant MR transform, we achieve a classification accuracy of approximately 88%. Index Terms — Stem cell biology, multiresolution, classifica-tion, feature extractio