High-grade Endometrial Carcinomas: Morphologic and Immunohistochemical Features, Diagnostic Challenges and Recommendations

This review of challenging diagnostic issues concerning high-grade endometrial carcinomas is derived from the authors' review of the literature followed by discussions at the Endometrial Cancer Workshop sponsored by the International Society of Gynecological Pathologists in 2016. Recommendations presented are evidence-based, insofar as this is possible, given that the levels of evidence are weak or moderate due to small sample sizes and nonuniform diagnostic criteria used in many studies. High-grade endometrioid carcinomas include FIGO grade 3 endometrioid carcinomas, serous carcinomas, clear cell carcinomas, undifferentiated carcinomas, and carcinosarcomas. FIGO grade 3 endometrioid carcinoma is diagnosed when an endometrioid carcinoma exhibits >50% solid architecture (excluding squamous areas), or when an architecturally FIGO grade 2 endometrioid carcinoma exhibits marked cytologic atypia, provided that a glandular variant of serous carcinoma has been excluded. The most useful immunohistochemical studies to make the distinction between these 2 histotypes are p53, p16, DNA mismatch repair proteins, PTEN, and ARID1A. Endometrial clear cell carcinomas must display prototypical architectural and cytologic features for diagnosis. Immunohistochemical stains, including, Napsin A and p504s can be used as ancillary diagnostic tools; p53 expression is aberrant in a minority of clear cell carcinomas. Of note, clear cells are found in all types of high-grade endometrial carcinomas, leading to a tendency to overdiagnose clear cell carcinoma. Undifferentiated carcinoma (which when associated with a component of low-grade endometrioid carcinoma is termed "dedifferentiated carcinoma") is composed of sheets of monotonous, typically dyscohesive cells, which can have a rhabdoid appearance; they often exhibit limited expression of cytokeratins and epithelial membrane antigen, are usually negative for PAX8 and hormone receptors, lack membranous e-cadherin and commonly demonstrate loss of expression of DNA mismatch repair proteins and SWI-SNF chromatin remodeling proteins. Carcinosarcomas must show unequivocal morphologic evidence of malignant epithelial and mesenchymal differentiation

Towards an integrative understanding of soil biodiversity

Soil is one of the most biodiverse terrestrial habitats. Yet, we lack an integrative conceptual framework for understanding the patterns and mechanisms driving soil biodiversity. One of the underlying reasons for our poor understanding of soil biodiversity patterns relates to whether key biodiversity theories (historically developed for aboveground and aquatic organisms) are applicable to patterns of soil biodiversity. Here, we present a systematic literature review to investigate whether and how key biodiversity theories (species-energy relationship, theory of island biogeography, metacommunity theory, niche theory and neutral theory) can explain observed patterns of soil biodiversity. We then discuss two spatial compartments nested within soil at which biodiversity theories can be applied to acknowledge the scale-dependent nature of soil biodiversity.Peer reviewe

Global distribution of earthworm diversity

Soil organisms, including earthworms, are a key component of terrestrial ecosystems. However, little is known about their diversity, their distribution, and the threats affecting them. We compiled a global dataset of sampled earthworm communities from 6928 sites in 57 countries as a basis for predicting patterns in earthworm diversity, abundance, and biomass. We found that local species richness and abundance typically peaked at higher latitudes, displaying patterns opposite to those observed in aboveground organisms. However, high species dissimilarity across tropical locations may cause diversity across the entirety of the tropics to be higher than elsewhere. Climate variables were found to be more important in shaping earthworm communities than soil properties or habitat cover. These findings suggest that climate change may have serious implications for earthworm communities and for the functions they provide

Global distribution of earthworm diversity

International audienceSoil organisms, including earthworms, are a key component of terrestrial ecosystems. However, little is known about their diversity, distribution, and the threats affecting them. Here, we compiled a global dataset of sampled earthworm communities from 6928 sites in 57 countries to predict patterns in earthworm diversity, abundance, and biomass. We identified that local species richness and abundance typically peaked at higher latitudes, patterns opposite to those observed in aboveground organisms. However, diversity across the entirety of the tropics may be higher than elsewhere, due to high species dissimilarity across locations. Climate variables were more important in shaping earthworm communities than soil properties or habitat cover. These findings suggest that climate change may have serious implications for earthworm communities and therefore the functions they provide

Global data on earthworm abundance, biomass, diversity and corresponding environmental properties

Data collated from data provided by original data collectors or from data provided within published articles. The MetaData.csv provides information on each of the original data sources, including bibliographic information about the original article and information on how many sites were sampled. The SiteData.csv gives site-level variables, such as geographic coordinates, the environmental parameters as well as site-level community metrics (species richness, total abundance and total biomass). The SppOccData.csv provides the observation level data - the occurrence, abundance and/or biomass of individual species/morpho-species/life-stage at a particular site. Not every data source contained such observation level data. Metadata information about the variables in each file are provided in the files MetaData_info.csv, SiteData_info.csv and SppOccData_info.csv, respectively. All files provided use the character encoding UTF-8, and missing values are represented by "NA"