background: The development of new blood vessels plays an essential role in growth and survival of endometriosis. Epigallocatechin gallate (EGCG) from green tea has powerful anti-angiogenic properties and our aim was to evaluate these properties in experimental endometriosis. methods and results: Eutopic endometrium from endometriosis patients was transplanted s.c. to severely compromised immunodeficient mice, randomly treated i.p. with EGCG (anti-angiogenic and -oxidant), Vitamin E (a non-angiogenic antioxidant) or saline for 2 weeks. The endometrial implant, including adjacent host outer skin and subcutaneous layers plus inner abdominal muscle and peritoneum, was collected. New microvessels were determined by species-specific immunohistochemistry. Angiogenic factors in lesions and abdominal muscle were detected by quantitative real-time PCR. Apoptosis was studied by terminal deoxynucleotidyltransferase-mediated dUTP nickend labelling and quantitative real-time PCR. In saline control, endometrial implants developed new blood vessels with proliferating glandular epithelium and were tightly adhered to host subcutaneous and abdominal muscle layers. After EGCG, endometriotic lesions were smaller than control (P , 0.05), and glandular epithelium was smaller and eccentrically distributed. Angiogenesis in lesions from the implant and adjacent tissues was under-developed, and microvessel size and density were lower (both P , 0.01) than control. mRNA for angiogenic vascular endothelial growth factor A, but not hypoxia inducible factor 1, alpha subunit, was significantly down-regulated in lesions after EGCG (P , 0.05). In addition, apoptosis in the lesions was more obvious, and nuclear factor kappa B and mitogen activated protein kinase 1 mRNA levels were up-regulated (P , 0.05) after EGCG treatment. No differences were observed with Vitamin E treatment.

Anti-angiogenic effects of green tea catechin on an experimental endometriosis mouse model

Skin biopsy histopathological analysis is one of the primary methods used for pathologists to assess the presence and deterioration of melanoma in clinical. A comprehensive and reliable pathological analysis is the result of correctly segmented melanoma and its interaction with benign tissues, and therefore providing accurate therapy. In this study, we applied the deep convolution network on the hyperspectral pathology images to perform the segmentation of melanoma. To make the best use of spectral properties of three dimensional hyperspectral data, we proposed a 3D fully convolutional network named Hyper-net to segment melanoma from hyperspectral pathology images. In order to enhance the sensitivity of the model, we made a specific modification to the loss function with caution of false negative in diagnosis. The performance of Hyper-net surpassed the 2D model with the accuracy over 92%. The false negative rate decreased by nearly 66% using Hyper-net with the modified loss function. These findings demonstrated the ability of the Hyper-net for assisting pathologists in diagnosis of melanoma based on hyperspectral pathology images.

/pdf/identification-of-melanoma-from-hyperspectral-pathology-541x3gh97z.pdf

Identification of Melanoma From Hyperspectral Pathology Image Using 3D Convolutional Networks

Skin disease diagnosis with deep learning: A review

Skin cancer is one of the most threatening diseases worldwide. However, diagnosing skin cancer correctly is challenging. Recently, deep learning algorithms have emerged to achieve excellent performance on various tasks. Particularly, they have been applied to the skin disease diagnosis tasks. In this paper, we present a review on deep learning methods and their applications in skin disease diagnosis. We first present a brief introduction to skin diseases and image acquisition methods in dermatology, and list several publicly available skin datasets for training and testing algorithms. Then, we introduce the conception of deep learning and review popular deep learning architectures. Thereafter, popular deep learning frameworks facilitating the implementation of deep learning algorithms and performance evaluation metrics are presented. As an important part of this article, we then review the literature involving deep learning methods for skin disease diagnosis from several aspects according to the specific tasks. Additionally, we discuss the challenges faced in the area and suggest possible future research directions. The major purpose of this article is to provide a conceptual and systematically review of the recent works on skin disease diagnosis with deep learning. Given the popularity of deep learning, there remains great challenges in the area, as well as opportunities that we can explore in the future.

Skin disease diagnosis with deep learning: a review.

Thyrotropin (TSH)-secreting pituitary tumors are the rarest functioning pituitary tumors. Nonetheless, they are not infrequently plurihormonal, as they may express/secrete hormones made by other pituitary cells derived from the Pit-1 lineage such as growth hormone (GH), prolactin (PRL), and α-subunit (αSU). However, adrenocorticotropin (ACTH) or gonadotropin secretion by such a tumor is exceptional. Although double pituitary tumors are rare, they often combine ACTH and GH secretion. A 41-year-old presented almost 2 years after delivering her 10th child; she had thyrotoxicosis (goiter and palpitations) masquerading as autoimmune postpartum thyroiditis. She was still breastfeeding and amenorrheic. She proved to have TSH, GH, PRL, and ACTH hypersecretion. Imaging revealed an invasive pituitary macrotumor. She had stigmata neither of Cushing’s disease nor of acromegaly. Prior to surgery, hormonal control was achieved for close to 1 year by combined octreotide and cabergoline treatment with significant shrinking of the tumor. Following surgery, pathology revealed a collision tumor; the dominant lesion was positive for GH, βTSH, βFSH, and αSU and expressed both Pit-1 and SF-1.The smaller lesion was a corticotroph tumor. We report an unusual plurihormonal tumor co-expressing Pit-1 and SF-1 along with hormones made by cells of both lineages. Its simultaneous occurrence adjacent to a corticotroph tumor raises questions regarding the pathogenesis of these tumors. We propose the possibility of a stem cell tumor with multiple lineage differentiation. We hypothesize that pregnancy might have played a permissive role in tumorigenesis.

Plurihormonal Pituitary Tumor of Pit-1 and SF-1 Lineages, with Synchronous Collision Corticotroph Tumor: a Possible Stem Cell Phenomenon.

Calretinin and CD34 immunoreactivity of the endometrial stroma in normal endometrium and change of the immunoreactivity in dysfunctional uterine bleeding with evidence of 'disordered endometrial stroma'.

Our work applies modern machine learning techniques to melanoma diagnostics. First, we curated a new dataset of 50 patient cases of cutaneous melanoma in whole slide images (WSIs). We applied gold standard annotations for three tissue types (tumour, epidermis, and dermis) which are important for the prognostic measurements known as Breslow thickness and Clark level. Then, we devised a novel multi-stride fully convolutional network (FCN) architecture that outperformed other networks trained and tested using the same data and evaluated on standard metrics. Three pathologists measured the Breslow thickness on the network's output. Their responses were diagnostically equivalent to the ground truth measurements, showing that it is possible to overcome the discriminative challenges of the skin and tumour anatomy for segmentation. Though more work is required to improve the network's performance on dermis segmentation, we have shown it is possible to achieve a level of accuracy required to manually perform the Breslow thickness measurement.

/pdf/segmentation-of-prognostic-tissue-structures-in-cutaneous-52bz0qojyr.pdf

Segmentation of Prognostic Tissue Structures in Cutaneous Melanoma Using Whole Slide Images

Aims: To investigate the reactivity for oestrogen and progesterone receptors (ER and PR) in renal oncocytoma (RO) and chromophobe renal cell carcinoma (CHRCC).



Materials and methods:  Thirty-eight RO, 25 CHRCC, 20 papillary RCC with oncocytic cytoplasm and 10 clear cell RCC with dominant eosinophilic cytoplasm were submitted for immunohistochemistry for ER, PR, CD117 and RCC.



Results:  All cases of RO and CHRCC displayed moderately positive reactivity for PR. The nuclear reactivity ranged from 60% to 90% in RO and from occasional cells to 70% in CHRCC. In CHRCC, reactivity tended to be more prevalent in areas of tumour cells with eosinophilic cytoplasm. Progesterone reactivity was focal in areas. All RO and most CHRCC were reactive for CD117 and neither RO nor CHRCC was reactive for RCC. CD117 reactivity tended to be more intense in CHRCC than in RO. Negative reactivity for CD117 and positive reactivity for RCC were observed in almost all RCC, as reported in the literature.



Conclusions:  PR can be used in combination with CD117 and RCC in the differential diagnosis of RO and eosinophilic variant of CHRCC with other RCC with oncocytic or eosinophilic cytoplasm.

Progesterone receptor reactivity in renal oncocytoma and chromophobe renal cell carcinoma.

Intraperitoneal vancomycin is used for empiric treatment of peritoneal dialysis peritonitis. It is dosed intermittently and a high systemic concentration is often achieved. Despite this, there are very few reports of systemic toxicity from intraperitoneal vancomycin. We report the course of a patient who developed a drug reaction with eosinophilia and systemic symptoms (DRESS) syndrome after three weeks of intraperitoneal vancomycin. We review the literature and conclude that this is the first ever reported case of DRESS syndrome from intraperitoneal vancomycin.

/pdf/systemic-toxicity-of-intraperitoneal-vancomycin-41einnohu1.pdf

Systemic Toxicity of Intraperitoneal Vancomycin

Objective To evaluate preoperative diagnosis of low-grade urothelial carcinoma (LGUC) and urothelial neoplasms of unknown malignant potential (UMP UN) of the upper urinary tract (UUT) and its role in disease management, especially in the context of nephron-sparing treatment possibilities. Study design Wash and brush ureteral specimens of LGUC/UMP UN of the UUT with histopathologic correlation were retrieved at our institution for 7 years and studied along with 7 ureteral specimens from nonneoplastic ureteral lesions. Results Of 30 specimens from 25 LGUC/UMP UN, 5 were negative for tumor cells and 3 showed cytologic atypia. The remaining 22 contained tumor cells with characteristic features of urothelial carcinoma, including hard and soft criteria. The 4 hard criteria included branching stromal cores, dyshesive cell networks, 3-dimensional papillary clusters with stromal core and atypia associated with CK20-positive cells. The 2 soft criteria were hypercellularity and atypia in CK20-negative cells. All LGUC/UMP UN of the UUT were associated with at least 1 hard criterion or both soft criteria. Conclusion Branching stromal cores, 3-dimensional papillary clusters, dyshesive cell networks and CK20-positive atypia immunostaining appear specific for LGUC/UMP UN of the UUT but are seen in few cases. Combined soft and hard criteria will increase sensitivity to 83%.

Immunostaining as a diagnostic aid in cytopathologic study of upper urinary tract urothelial carcinoma

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