Experimental Copper Deficiency, Chromium Deficiency and Additional Molybdenum Supplementation in Goats – Pathological Findings

Secondary copper (Cu) deficiency, chromium (Cr) deficiency and molybdenosis (Mo) has been suggested to cause the "mysterious" moose disease in the southwest of Sweden. The present experiment was performed on goats to investigate the clinical, chemical, and pathological alterations after 20 months feeding of a semi-synthetic diet deficient in Cu and Cr. Four groups were included in the study: control group (n = 4), Cu-deficient group (group 1, n = 4), Cr-deficient group (group 2, n = 2) and Cu+Cr-deficient group (group 3, n = 3). Group 3 was additionally supplemented with tetrathiomolybdate during the last 2 months of the experiment. Main histopathological findings in groups 1 and 3 were the lesions in the liver, characterised by a severe active fibrosis, bile duct proliferation, haemosiderosis and mild necroses. Additionally, degenerative alterations of the exocrine pancreas were prominent in groups 1 and 3. Lesions in group 3 were more pronounced than in group 1. In group 3, the skin showed an atrophic dermatosis, while in group 2 a crusty dermatitis caused by Candida spp. was observed. This study shows that liver, pancreas and skin are mainly affected by a long term deficiency of copper and the findings are complicated by molybdenum application while chromium deficiency produced no histomorphological effects in our study

Diagnostic guidelines for the histological particle algorithm in the periprosthetic neo-synovial tissue

Background The identification of implant wear particles and non-implant related particles and the characterization of the inflammatory responses in the periprosthetic neo-synovial membrane, bone, and the synovial-like interface membrane (SLIM) play an important role for the evaluation of clinical outcome, correlation with radiological and implant retrieval studies, and understanding of the biological pathways contributing to implant failures in joint arthroplasty. The purpose of this study is to present a comprehensive histological particle algorithm (HPA) as a practical guide to particle identification at routine light microscopy examination. Methods The cases used for particle analysis were selected retrospectively from the archives of two institutions and were representative of the implant wear and non-implant related particle spectrum. All particle categories were described according to their size, shape, colour and properties observed at light microscopy, under polarized light, and after histochemical stains when necessary. A unified range of particle size, defined as a measure of length only, is proposed for the wear particles with five classes for polyethylene (PE) particles and four classes for conventional and corrosion metallic particles and ceramic particles. Results All implant wear and non-implant related particles were described and illustrated in detail by category. A particle scoring system for the periprosthetic tissue/SLIM is proposed as follows: 1) Wear particle identification at light microscopy with a two-step analysis at low (× 25, × 40, and × 100) and high magnification (× 200 and × 400); 2) Identification of the predominant wear particle type with size determination; 3) The presence of non-implant related endogenous and/or foreign particles. A guide for a comprehensive pathology report is also provided with sections for macroscopic and microscopic description, and diagnosis. Conclusions The HPA should be considered a standard for the histological analysis of periprosthetic neo-synovial membrane, bone, and SLIM. It provides a basic, standardized tool for the identification of implant wear and non-implant related particles at routine light microscopy examination and aims at reducing intra-observer and inter-observer variability to provide a common platform for multicentric implant retrieval/radiological/histological studies and valuable data for the risk assessment of implant performance for regional and national implant registries and government agencies

Early esophageal cancer detection using RF classiers

One of the fastest rising forms of cancer in the Western world is esophageal cancer. Using High-Denition (HD) endoscopy, gastroenterology experts can identify esophageal cancer at an early stage. Recent research shows that early cancer can be found using a state-of-the-art computer-aided detection (CADe) system based on analyzing static HD endoscopic images. Our research aims at extending this system by applying Random Forest (RF) classication which introduces a condence measure for detected cancer regions. To visualize this data, we propose a novel annotation system, employing the unique characteristics of the previous condence measure. This allows reliable modeling of multi-expert knowledge and provides essential data for real-time video processing, to enable future use of the system in a clinical setting. The performance of the CADe system is evaluated on a 39-patient ataset, containing 100 images annotated by ve expert gastroenterologists. The proposed system reaches a precision of 75% and recall of 90%, thereby improving the state-of-the-art results by 11 and 6 percentage points, espectively. Esophageal cancer is one of the fastest rising forms of cancer in the Western world. Using High-Definition (HD) endoscopy, gastroenterology experts can identify esophageal cancer at an early stage. Recent research shows that early cancer can be found using a state-of-the-art computer-aided detection (CADe) system based on analyzing static HD endoscopic images. Our research aims at extending this system by applying Random Forest (RF) classification, which introduces a confidence measure for detected cancer regions. To visualize this data, we propose a novel automated annotation system, employing the unique characteristics of the previous confidence measure. This approach allows reliable modeling of multi-expert knowledge and provides essential data for real-time video processing, to enable future use of the system in a clinical setting. The performance of the CADe system is evaluated on a 39-patient dataset, containing 100 images annotated by 5 expert gastroenterologists. The proposed system reaches a precision of 75% and recall of 90%, thereby improving the state-of-the-art results by 11 and 6 percentage points, respectively