Chiasma crurale: intersection of the tibialis posterior and flexor digitorum longus tendons above the ankle. Magnetic resonance imaging-anatomic correlation in cadavers

Purpose: To determine the precise anatomy and magnetic resonance (MR) imaging appearance of the chiasma crurale in cadavers, paying special attention to degenerative changes Material and methods: Twelve fresh human ankles were harvested from 11 nonembalmed cadavers (mean age at death 77years) and used according to institutional guidelines. MR imaging and MR tenography were used to investigate the anatomy of the chiasma crurale using proton density-weighted sequences. The gross anatomy of the chiasma crurale was evaluated and compared to the MR imaging findings. Histology was used to elucidate further the structure of the chiasma crurale. Results: Above the chiasma, five specimens had a small amount of fat tissue between the tibialis posterior and flexor digitorum longus tendon. In all specimens both tendons had a sheath below the chiasma but not above it. At the central portion of the chiasma there was no soft tissue between the tendons, except in two specimens that showed an anatomic variant consisting of a thick septum connecting the tibial periosteum and the deep transverse fascia of the leg. In MR images, eight specimens showed what were believed to be degenerative changes in the tendons at the level of the chiasma. However, during gross inspection and histologic analysis of the specimens, there was no tendon degeneration visible. Conclusion: At the central portion of the chiasma, there is no tissue between the tibialis posterior and flexor digitorum longus tendons unless there is an anatomic variant. At the chiasma crurale, areas with irregular tendon surfaces are normal findings and are not associated with tendon degeneration (fraying

Carpal bone cysts: MRI, gross pathology, and histology correlation in cadavers

PURPOSEIntraosseous cysts of carpal bones are frequently observed on routine imaging examinations of the wrist. There is controversy regarding the underlying pathogenesis of these cysts. In this study, we aimed to investigate the magnetic resonance imaging (MRI) appearance of intracarpal bone cysts in correlation with histologic analysis, using cadaveric wrists.METHODSFive freshly frozen cadaveric wrist specimens (from three women and two men; mean age at death, 80 years) were studied. Imaging was performed with T1-weighted fast spin-echo, and proton density-weighted fast spin-echo with and without fat-suppression. The existence of cysts was confirmed by comparing MRI and histology findings. Hematoxylin and eosin stain was performed on tissue slices of 3 mm thickness to analyze the structure of cysts and their communication with the joint cavity.RESULTSTen cysts were observed. In all cases, cysts were eccentrically located either in the subchondral bone or beneath the cortex. On histologic examination, there were regions of fat necrosis without inflammation or increased vascularity, surrounded by fibrous walls. There were no giant cells, cholesterol granules, or a true synovial lining. Mucoid change was rare. Fibrous component of cysts varied from small fibrous septa to well-formed walls. Some cysts communicated with the joint cavity. Two cysts were adjacent to ligamentous attachments. Those cysts with fibrous tissue demonstrated variable hypointensity on T2.CONCLUSIONIn contrast to previous reports that described a mucoid composition of intracarpal bone cysts with occasional foamy macrophages, our observations support the concept that these lesions reflect a spectrum of fat necrosis and fibrous changes, without inflammation or hypervascularity. These cysts are typically surrounded by fibrous walls without a true synovial lining

Hamartomatous polyps of the colon - Ganglioneuromatous, stromal, and lipomatous

Intestinal ganglioneuromas comprise benign, hamartomatous polyps characterized by an overgrowth of nerve ganglion cells, nerve fibers, and supporting cells in the gastrointestinal tract. This polyposis has been divided into 3 subgroups, each with a different degree of ganglioneuroma formation: polypoid ganglioneuroma, ganglioneuromatous polyposis, and diffuse ganglioneuromatosis. The ganglioneuromatous polyposis subgroup is not known to coexist with systemic disorders that often have an associated intestinal polyposis, such as multiple endocrine neoplasia type IIb, neurofibromatosis type 1, and Cowden syndrome. We report a case of ganglioneuromatous polyposis plus cutaneous lipomatosis in a 41-year-old man with no established systemic disease. However, he possessed unique anatomic findings in addition to his ganglioneuromatosis, suggesting that the ganglioneuromatosis-lipomatosis in our patient may represent an unrecognized syndrome. This case report and brief review of the literature provide an overview of intestinal ganglioneuromatosis in relation to the hereditary polyposis syndromes and describe the individual ganglioneuromatosis subgroups

Hamartomatous polyps of the colon - Ganglioneuromatous, stromal, and lipomatous

Intestinal ganglioneuromas comprise benign, hamartomatous polyps characterized by an overgrowth of nerve ganglion cells, nerve fibers, and supporting cells in the gastrointestinal tract. This polyposis has been divided into 3 subgroups, each with a different degree of ganglioneuroma formation: polypoid ganglioneuroma, ganglioneuromatous polyposis, and diffuse ganglioneuromatosis. The ganglioneuromatous polyposis subgroup is not known to coexist with systemic disorders that often have an associated intestinal polyposis, such as multiple endocrine neoplasia type IIb, neurofibromatosis type 1, and Cowden syndrome. We report a case of ganglioneuromatous polyposis plus cutaneous lipomatosis in a 41-year-old man with no established systemic disease. However, he possessed unique anatomic findings in addition to his ganglioneuromatosis, suggesting that the ganglioneuromatosis-lipomatosis in our patient may represent an unrecognized syndrome. This case report and brief review of the literature provide an overview of intestinal ganglioneuromatosis in relation to the hereditary polyposis syndromes and describe the individual ganglioneuromatosis subgroups

Improving hybrid models for precipitation forecasting by combining nonlinear machine learning methods

Abstract Precipitation forecast is key for water resources management in semi-arid climates. The traditional hybrid models simulate linear and nonlinear components of precipitation series separately. But they do not still provide accurate forecasts. This research aims to improve hybrid models by using an ensemble of linear and nonlinear models. Preprocessing configurations and each of the Gene Expression Programming (GEP), Support Vector Regression (SVR), and Group Method of Data Handling (GMDH) models were used as in the traditional hybrid models. They were compared against the proposed hybrid models with a combination of all these three models. The performance of the hybrid models was improved by different methods. Two weather stations of Tabriz and Rasht in Iran with respectively annual and monthly time steps were selected to test the improved models. The results showed that Theil’s coefficient, which measures the inequality degree to which forecasts differ from observations, improved by 9% and 15% for SVR and GMDH relative to GEP for the Tabriz station. The applied error criteria indicated that the proposed hybrid models have a better representation of observations than the traditional hybrid models. Mean square error decreased by 67% and Nash Sutcliffe increased by 5% in the Rasht station when we combined the three machine learning models using genetic algorithm instead of SVR. Generally, the representation of the nonlinear models within the improved hybrid models showed better performance than the traditional hybrid models. The improved models have implications for modeling highly nonlinear systems using the full advantages of machine learning methods

Overbreak prediction and optimization in tunnel using neural network and bee colony techniques

Overbreak is an undesirable phenomenon in blasting operations. The causing factors of overbreak can be generally divided as blasting and geological parameters. Due to multiplicity of effective parameters and complexity of interactions among these parameters, empirical methods may not be fully appropriated for blasting pattern design. In this research, artificial neural network (ANN) as a powerful tool for solving such complicated problems is developed to predict overbreak induced by blasting operations in the Gardaneh Rokh tunnel, Iran. To develop an ANN model, an established database comprising of 255 datasets has been utilized. A three-layer ANN was found as an optimum model for prediction of overbreak. The coefficient of determination (R2) and root mean square error (RMSE) values of the selected model were obtained as 0.921, 0.4820, 0.923 and 0.4277 for training and testing, respectively, which demonstrate a high capability of ANN in predicting overbreak. After selecting the best model, the selected model was used for optimization purpose using artificial bee colony (ABC) algorithm as one of the most powerful optimization algorithms. Considering this point that overbreak is one of the main problems in tunneling, reducing its amount causes to have a good tunneling operation. After making several models of optimization and variations in its weights, the optimum amount for the extra drilling was 1.63 m2, which is 47% lower than the lowest value (3.055 m2). It can be concluded that ABC algorithm can be introduced as a new optimizing algorithm to minimize overbreak induced by tunneling