Biomedical subjects

S S Dlay

Publications and source records attributed to S S Dlay.

3 recordsLinked to original sources

PubMed2006

Nonlinear signal separation for multinonlinearity constrained mixing model.

In this letter, a new type of nonlinear mixture is derived and developed into a multinonlinearity constrained mixing model. The proposed signal separation solution integrates the Theory of Series Reversion with a polynomial neural network whereby the hidden neurons are spanned by a set of mutually reversed activation functions. Simulations have been undertaken to support the theory of the proposed scheme and the results indicate promising performance.

Algorithms↗

PubMed2003

Prediction of nodal spread of breast cancer by using artificial neural network-based analyses of S100A4, nm23 and steroid receptor expression.

The expression of tumour promoter gene S100A4, metastasis suppressor gene nm23, oestrogen and progesterone receptors, and tumour grade and size have been investigated for their potential to predict breast cancer progression. The molecular and cellular data have been analysed using artificial neural networks to determine the potential of these markers to predict the presence of metastatic tumour in the regional lymph nodes. This study shows that tumour grade and size are poor predictors. The relative expression of S100A4 and nm23 genes is the single most effective predictor of nodal status. Inclusion of oestrogen- and progesterone-receptor status with tumour grade and size markers improves prediction; however, there may be some overlap between steroid receptors and molecular markers. This study also underscores the power of artificial neural network techniques to predict the potential of primary breast cancers to spread to axillary lymph nodes. This could aid the clinician in determining whether invasive procedures of axially node dissection can be obviated and whether conservative forms of treatment might be appropriate in the management of the patient.

Adult↗

PubMedDate not supplied

Oestrogen and progesterone receptor expression influences DNA ploidy and the proliferation potential of breast cancer cells.

The influence of oestrogen (ER) and progesterone (PgR) receptor has not been investigated in relation to DNA ploidy and cell proliferation. Here we have investigated a series of 46 breast cancer fine-needle aspirates in order to define the prognostic value of ER/PgR and possible correlations between DNA ploidy, size of the S-phase fraction (SPF) and cell cycle distribution features measured by image cytometry (ICM) and ER/PgR status of the primary tumours. The breast cancers were grouped into ER+/PgR+, ER+/PgR- and ER-/PgR-. Inter-group comparisons were made of DNA ploidy, SPF and the pattern of cell cycle distribution defined by the G0G1/G2M ratio in order to determine the influence of ER and PgR expression on the respective cell features. Our studies suggest that ER and PgR exert differential effects on the cell features examined. This study also analysed the possibility of predicting nodal involvement and 5-year disease-free survival by combining ER/PgR status with DNA ploidy, SPF and G0G1/G2M ratios. A high degree of accuracy was achieved for predicting both nodal involvement and 5-year disease-free survival. These findings suggest that a combination of ER/PgR status with DNA ploidy, SPF and cell cycle distribution provide a powerful marker for disease prognosis.

Breast Neoplasms↗