Scleromyxedematous Changes in a Patient With Long-Standing Mycosis Fungoides Who Progressed to Sézary Syndrome.

Mycosis fungoides (MF) variants with different clinicopathologic and immunohistochemical features have been well-delineated. We report a case of scleromyxedematous changes arising in a patient with long-standing MF who progressed to Sézary syndrome (SS) shortly afterward. Total-skin electron-beam radiation therapy resulted in an excellent response, controlling both the MF/SS and the scleromyxedematous lesions; however, the patient died few months later. Although mucin deposition has been described in association with MF/SS (mainly follicular mucinosis in folliculotropic MF), there are limited reports in the literature on dermal mucinosis and scleromyxedematous changes in MF/SS. The mechanism of this association and its prognostic implications requires further investigation.

Somatic mutation analysis in melanoma using targeted next generation sequencing.

Advanced stage malignant melanoma often responds poorly to therapy with low survival rates. New therapeutic approaches are based upon a growing understanding of the underlying molecular abnormalities. We demonstrate the feasibility of a next generation sequencing (NGS) assay, which targets hotspots in 50 cancer genes, to assess genotypes that may influence therapeutic selection and response. DNA was extracted from formalin fixed paraffin embedded (FFPE) melanoma specimens to create multiplexed libraries which were sequenced. Of the 121 cases, BRAF mutations were present in 48 cases (40%) and NRAS mutations in 24 cases (20%). We identified other gene variants in 20 BRAF-mutated cases. Additional gene variants were also identified in the 57 BRAF wild-type cases. Four patients harbored different gene mutations at metastatic sites as compared to their primary lesions or metastasis from different sites. Concurrent gene variants may provide additional targets for future therapies and may suggest alternative mechanisms of secondary resistance.

The many masks of cutaneous Lyme disease.

Early cutaneous Lyme disease, erythema migrans, may show different histopathologic patterns. The intent of this case series is to raise awareness of these findings to prevent misdiagnosis and keep this entity in the differential. Erythema migrans develops after a tick bite and subsequent infection with the spirochete, Borrelia burgdorferi. It most commonly manifests as a solitary, annular lesion with a bull's-eye appearance. Classic histopathologic findings include superficial and deep perivascular and interstitial lymphocytic infiltrates mixed with plasma cells and eosinophils. We identified and reviewed eight cases of early erythema migrans. Each patient had confirmed B. burgdorferi IgM seropositivity and IgG seronegativity. Histopathologic evaluation of these biopsies reveals a diversity of patterns. Seven of eight cases show sparse to mild perivascular and interstitial mixed infiltrate of variable amount of lymphocytes, eosinophils, neutrophils and plasma cells, with only one case showing a dense inflammatory infiltrate. Epidermal changes such as spongiosis and interface change are seen in some cases. Additionally, perineural lymphocytic infiltrate is seen in one case, periadnexal infiltrate in four cases and pigment incontinence in one case. Based on variable histopathologic findings, it is important to consider erythema migrans in the differential diagnosis for prompt diagnosis and treatment.

Clinical-grade computational pathology using weakly supervised deep learning on whole slide images.

The development of decision support systems for pathology and their deployment in clinical practice have been hindered by the need for large manually annotated datasets. To overcome this problem, we present a multiple instance learning-based deep learning system that uses only the reported diagnoses as labels for training, thereby avoiding expensive and time-consuming pixel-wise manual annotations. We evaluated this framework at scale on a dataset of 44,732 whole slide images from 15,187 patients without any form of data curation. Tests on prostate cancer, basal cell carcinoma and breast cancer metastases to axillary lymph nodes resulted in areas under the curve above 0.98 for all cancer types. Its clinical application would allow pathologists to exclude 65-75% of slides while retaining 100% sensitivity. Our results show that this system has the ability to train accurate classification models at unprecedented scale, laying the foundation for the deployment of computational decision support systems in clinical practice.

Deep Learning for Classification of Colorectal Polyps on Whole-slide Images.

CONTEXT: Histopathological characterization of colorectal polyps is critical for determining the risk of colorectal cancer and future rates of surveillance for patients. However, this characterization is a challenging task and suffers from significant inter- and intra-observer variability. AIMS: We built an automatic image analysis method that can accurately classify different types of colorectal polyps on whole-slide images to help pathologists with this characterization and diagnosis. SETTING AND DESIGN: Our method is based on deep-learning techniques, which rely on numerous levels of abstraction for data representation and have shown state-of-the-art results for various image analysis tasks. SUBJECTS AND METHODS: Our method covers five common types of polyps (i.e., hyperplastic, sessile serrated, traditional serrated, tubular, and tubulovillous/villous) that are included in the US Multisociety Task Force guidelines for colorectal cancer risk assessment and surveillance. We developed multiple deep-learning approaches by leveraging a dataset of 2074 crop images, which were annotated by multiple domain expert pathologists as reference standards. STATISTICAL ANALYSIS: We evaluated our method on an independent test set of 239 whole-slide images and measured standard machine-learning evaluation metrics of accuracy, precision, recall, and F1 score and their 95% confidence intervals. RESULTS: Our evaluation shows that our method with residual network architecture achieves the best performance for classification of colorectal polyps on whole-slide images (overall accuracy: 93.0%, 95% confidence interval: 89.0%-95.9%). CONCLUSIONS: Our method can reduce the cognitive burden on pathologists and improve their efficacy in histopathological characterization of colorectal polyps and in subsequent risk assessment and follow-up recommendations.

Identification of the potent phytoestrogen glycinol in elicited soybean (Glycine max).

The primary induced isoflavones in soybean, the glyceollins, have been shown to be potent estrogen antagonists in vitro and in vivo. The discovery of the glyceollins' ability to inhibit cancer cell proliferation has led to the analysis of estrogenic activities of other induced isoflavones. In this study, we investigated a novel isoflavone, glycinol, a precursor to glyceollin that is produced in elicited soy. Sensitive and specific in vitro bioassays were used to determine that glycinol exhibits potent estrogenic activity. Estrogen-based reporter assays were performed, and glycinol displayed a marked estrogenic effect on estrogen receptor (ER) signaling between 1 and 10 microM, which correlated with comparable colony formation of MCF-7 cells at 10 microM. Glycinol also induced the expression of estrogen-responsive genes (progesterone receptor and stromal-cell-derived factor-1). Competitive binding assays revealed a high affinity of glycinol for both ER alpha (IC(50) = 13.8 nM) and ER beta (IC(50) = 9.1 nM). In addition, ligand receptor modeling (docking) studies were performed and glycinol was shown to bind similarly to both ER alpha and ER beta. Taken together, these results suggest for the first time that glycinol is estrogenic and may represent an important component of the health effects of soy-based foods.

Biomonitoring of surface and coastal water for Cryptosporidium, Giardia, and human-virulent microsporidia using molluscan shellfish.

Surface inland and coastal waters in Ireland were surveyed for the human waterborne enteropathogens; Cryptosporidium parvum, Giardia lamblia, Encephalitozoon intestinalis, Encephalitozoon hellem, and Enterocytozoon bieneusi by utilizing bivalve mussel species, i.e., Mytilus edulis (blue mussel), Anodonta anatina (duck 'mussel', actually a unionid clam), and the invasive Dreissena polymorpha (zebra mussel) as biomonitors at twelve sites located in three Irish river-basin districts with various water-quality pressures. Biomolecular techniques were utilized to assess the presence and concentration of these pathogens. At least one pathogen species was detected in shellfish at each site. Cryptosporidium, implicated in several recent Irish gastrointestinal epidemics, was recorded at all sites subjected to agricultural runoff and at one sewage discharge site, linking source-track directly to human and animal fecal wastes. G. lamblia was present at eleven of the twelve sites in a range of concentrations. A coastal bay with raw urban sewage discharge was 100% positive for all analyzed enteropathogens. Overall, the results demonstrate long-term human enteropathogen contamination of Irish waters with consequent public-health risk factors for drinking-water abstraction and water-based activities.

Human enteropathogen load in activated sewage sludge and corresponding sewage sludge end products.

This study demonstrated a significant reduction in the concentrations of Cryptosporidium parvum and Cryptosporidium hominis oocysts, Giardia lamblia cysts, and spores of human-virulent microsporidia in dewatered and biologically stabilized sewage sludge cake end products compared to those of the respective pathogens in the corresponding samples collected during the sludge activation process.