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Klin Lab Diagn ; 60(10): 32-6, 2015 Oct.
Artigo em Russo | MEDLINE | ID: mdl-26841670

RESUMO

The ovarian carcinoma is the most frequent cause of death because of malignant neoplasins in women. At that, there is no pathognomonic symptoms permitting diagnosing stage of process with sufficient degree of confidence. This is a determinative not only for choosing treatment regimen but also for correlation of expected results of treatments with economic factors with purpose to evaluate cost effectiveness under monitoring of these patients. The study was carried out to develop mode of clarifying diagnostic of progressing forms of ovarian carcinoma on the basis of evaluation of oxidative modification of proteins in blood plasma. In 100 female patients with primary ovarian carcinoma (stage III and IV according FIGO,) in blood plasma 21 indicators were determined in blood plasma using standardized techniques: absolute number of leukocytes, absolute and relative number of neutrophils, products of oxidative modification of proteins of basic and ketonic character (under wavelength of 356, 370, 430, 530 nm), Her-2/neu CA-125, matrix metalloproteinase 2 and 9 in blood serum and neutrophils, interleukin-6, interleukin-10, interleukin-1ß, interferon-γ, tumor necrosis factor-α, malonic dialdehyde, catalase, glutaihione transferase, glutathione reductase. The least squares method was applied to every parameter to appmximnate relative rates by density of Rayleigh distribution. For every indicator likelihood ratio functions of likelihood ratio were constructed and intervals at every stage were determined. The reliability of approximation was proved under testing with Pirson criterion hypothesis of compliance of practical values of parameters to theoretical law of density of distribution. The results permit to assert that level of oxidative modification of proteins cab be used as a reliable indicator for differentiated diagnostic of stage III and IV of ovarian carcinoma.


Assuntos
Algoritmos , Biomarcadores Tumorais/sangue , Carcinoma/sangue , Neoplasias Ovarianas/sangue , Interpretação Estatística de Dados , Feminino , Humanos
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