Protective effect of MUC2 in mice with colitis and the expression of anti-CBir1 antibody in serum / 中国病理生理杂志

AIM:To investigate the protective effect of mucin 2(MUC2)on intestinal mucosa of colitis model mice,and to explore the correlation between the expression of anti-CBir1 flagellin antibody and MUC2.METHODS:The mice were randomly divided into normal control group,2,4,6-trinitrobenzenesulfonic acid(TNBS)group,lipopolysaccha-ride(LPS)+ovalalbumin(OVA)+TNBS group and ketotifen+TNBS group.The expression of MUC2 in colon tissue was determined by PAS staining and immunohistochemistry, and the anti-CBir1 antibody level in the serum of mice in each group was measured by ELISA.RESULTS:The scores of disease activity index and histological index in TNBS group were higher than those in normal control group(P<0.05).The scores in LPS +OVA+TNBS group were much higher than those in TNBS group(P<0.05).However, the values in ketotifen +TNBS group were lower than those in TNBS group (P<0.05).PAS staining showed a decrease in goblet cells in TNBS group.Compared with TNBS group,the colonic mu-cosa integrity in LPS+OVA+TNBS group was destroyed, and the number of goblet cells in ketotifen +TNBS group in-creased significantly.Immunohistochemical staining showed that the expression of MUC 2 in the intestinal tract of each mo-del group was basically consistent with the results of PAS staining.The serum anti-CBir1 antibody level in TNBS group was higher than that in normal control group(P<0.05), and that in LPS+OVA+TNBS group was significantly higher than that in TNBS group(P<0.05),whereas that in ketotifen +TNBS group was decreased slightly(P<0.05).CONCLU-SION:MUC2 plays a protective role in the pathogenesis of colitis in mice,and there is a negative correlation between the expression of MUC2 and the bacterial flagellin in the intestinal mucosa of mice with colitis.

RECUPERACIÓN BASADA EN CONTENIDO DE IMÁGENES MICROSCÓPICAS DE CUELLO UTERINO INFECTADAS CON EL VIRUS DEL PAPILOMA HUMANO EMPLEANDO CARACTERÍSTICAS DE TEXTURA / CONTENT-BASED RETRIEVAL OF CERVICAL MICROSCOPIC IMAGES INFECTED WITH HUMAN PAPILLOMAVIRUS USING TEXTURAL FEATURES / RECUPERAÇÃO BASEADA NO CONTEÚDO DE IMAGENS MICROSCÓPICAS DE COLO DO ÚTERO INFECTADO COM VÍRUS DO PAPILOMA HUMANO, UTILIZANDO CARACTERÍSTICAS DE TEXTURA

Se presenta el proceso de caracterización implementado para la obtención de descriptores visuales que representan el contenido visual de imágenes digitales de biopsias de cuello uterino infectadas con el Virus del Papiloma Humano (VPH), en las que se capturan tejidos con lesiones conocidas como Condiloma Plano Viral. A partir de la construcción de una base de datos de imágenes de biopsias de cuello uterino y el análisis e implementación de técnicas de filtrado que resaltan la información relacionada a las texturas contenidas en los tejidos que captura cada imagen y de técnicas de extracción de características que describen el contenido de las imágenes; se propone un conjunto de características que describen el contenido de las imágenes a partir de modificaciones propias de la Transformada Discreta de Wavelets y el cálculo de la Matriz de Coocurrencia, donde este conjunto de características propuesto proporcionó un porcentaje promedio de recuperación del 80% en imágenes microscópicas de cuello uterino infectadas con el VPH, sobre las cuales no se conocen sistemas CBIR desarrollados. Finalmente, se determina el porcentaje de recuperación promedio a partir del uso de métricas de similaridad basadas en la norma LP.

The purpose of this work is to report the characterization process implemented to obtain visual descriptors representing visual content of digital images of cervical biopsies infected with Human Papilloma Virus (HPV). Positive biopsies with infected tissues present lesions known as Condyloma Plano Viral. A database of images of cervical biopsies was constructed in addition to the implementation of techniques that enhance the texture information and describe the content of images. This work proposed a set of features to describe the content of images from custom modifications of Discrete Wavelet Transform and the calculation of the Co-occurrence Matrix. This proposed feature set provided an average recovery rate of 80% in microscopic images of the cervix infected with HPV, from which CBIR systems have not been developed. Finally, this work determines the average recovery rate from the use of similarity metrics based on the standard LP.

Neste trabalho é apresentado o processo implementado de caracterização para a obtenção de descrições visuais que representam o conteúdo visual de imagens digitais de biópsias cervicais infectadas com Papilomavírus Humano (HPV), capturadas em lesões de tecidos conhecidas como Condiloma Plano Viral. A partir da construção de uma base de dados de imagens de biópsias do colo uterino, análise e implementação de técnicas de filtragem de características que descrevem o conteúdo das imagems, propõe-se um conjunto de características que descrevem o conteúdo das imagens a partir de modificações próprias da Transformada Discreta de Wavelets e o cálculo da Matriz de co-ocorrência, onde o conjunto de características propostas resultou numa porcentagem média de 80% de recuperação nas imagens microscópicas de colo uterino infectado com o VPH, sobre as quais não se percebe o desenvolvimento dos sistemas CBIR. Finalmente, a taxa de recuperação média foi determinada a partir da utilização de métricas de similaridade com base na indicação de LP.

Sistema computacional para auxílio ao diagnóstico em exames de tuberculose animal / A computer software system to assist in the diagnosis of animal tuberculosis

The results obtained in evaluating the efficiency of a Neuro-Fuzzy System NEFCLASS (Neuro-Fuzzy Classification) in image classification of cattle tuberculosis, based on its texture features extracted using the wavelet transform are presented. For testing, images of animal tissues diagnosed with tuberculosis were used, as provided by the Tuberculosis Laboratory at the Instituto Biológico de São Paulo. The results of this study can serve as a basis for developing systems for diagnosis aimed at reducing human effort, by automating all or parts of the classification of images, helping lab technicians to sort amongst different pathologies.

Implementação e avaliação de um sistema de gerenciamento de imagens médicas com suporte à recuperação baseada em conteúdo / Implementation and evaluation of a medical image management system with content-based retrieval support

OBJETIVO: Neste artigo são descritas a implementação e avaliação de um sistema de gerenciamento de imagens médicas com suporte à recuperação baseada em conteúdo (PACS-CBIR), integrando módulos voltados para a aquisição, armazenamento e distribuição de imagens, e a recuperação de informação textual por palavras-chave e de imagens por similaridade. MATERIAIS E MÉTODOS: O sistema foi implementado com tecnologias para Internet, utilizando-se programas livres, plataforma Linux e linguagem de programação C++, PHP e Java. Há um módulo de gerenciamento de imagens compatível com o padrão DICOM e outros dois módulos de busca, um baseado em informações textuais e outro na similaridade de atributos de textura de imagens. RESULTADOS: Os resultados obtidos indicaram que as imagens são gerenciadas e armazenadas corretamente e que o tempo de retorno das imagens, sempre menor do que 15 segundos, foi considerado bom pelos usuários. As avaliações da recuperação por similaridade demonstraram que o extrator escolhido possibilitou a separação das imagens por região anatômica. CONCLUSÃO: Com os resultados obtidos pode-se concluir que é viável a implementação de um PACS-CBIR. O sistema apresentou-se compatível com as funcionalidades do DICOM e integrável ao sistema de informação local. A funcionalidade de recuperação de imagens similares pode ser melhorada com a inclusão de outros descritores.

OBJECTIVE: The present paper describes the implementation and evaluation of a medical images management system with content-based retrieval support (PACS-CBIR) integrating modules focused on images acquisition, storage and distribution, and text retrieval by keyword and images retrieval by similarity. MATERIALS AND METHODS: Internet-compatible technologies were utilized for the system implementation with freeware, and C++, PHP and Java languages on a Linux platform. There is a DICOM-compatible image management module and two query modules, one of them based on text and the other on similarity of image texture attributes. RESULTS: Results demonstrate an appropriate images management and storage, and that the images retrieval time, always < 15 sec, was found to be good by users. The evaluation of retrieval by similarity has demonstrated that the selected images extractor allowed the sorting of images according to anatomical areas. CONCLUSION: Based on these results, one can conclude that the PACS-CBIR implementation is feasible. The system has demonstrated to be DICOM-compatible, and that it can be integrated with the local information system. The similar images retrieval functionality can be enhanced by the introduction of further descriptors.

Development of Histopathological Breast Tumor Image Retrieval System Based on Internet Using a Content-based Retrieval Method / 대한의료정보학회지

OBJECTIVE: We have developed breast tumor image retrieval system using content-based retrieval method. It compares the breast tumor image with Fibrocystic Change images, Ductal Carcinoma in Situ images and Invasive Ductal Carcinoma images and find most similar one. Since the final diagnosis for breast tumor image is done only by pathologist manually, this system can provide the objectivity and the reproducibility for determining and diagnosing the breast tumor. METHODS: The breast tumor image features used in the content-based image retrieval are color feature, texture feature and texture features of wavelet transformed images. And the system can be accessed through the internet. We used Windows 2003 as an operating system, Internet Information Server 6.0 as Web a server and ms-sql server 2000 as a database server. Also we use ActiveX Data Object to connect database easily. RESULT: We evaluated the recall and precision performance of the system according to the combinations of feature types and usage of partial or whole image. Results showed that the use of multiple features and whole image gave consistently higher rates compared to the use of single feature and partial image. CONCLUSION: This retrieval system can help pathologist determine the type of breast tumor more efficiently. Also it is working based on the internet, we can use it for researching and teaching in pathology later.