Data mining techniques in breast cancer diagnosis at the cellular-molecular level.

INTRODUCTION: Studies in the field of better diagnosis of breast cancer using machine learning and data mining techniques have always been promising. A new diagnostic method can detect the characteristics of breast cancer in the early stages and help in better treatment. The aim of this study is to provide a method for early detection of breast cancer by reducing human errors based on data mining techniques in medicine using accurate and rapid screening. METHODOLOGY: The proposed method includes data pre-processing and image quality improvement in the first step. The second step consists of separating cancer cells from healthy breast tissue and removing outliers using image segmentation. Finally, a classification model is configured by combining deep neural networks in the third phase. The proposed ensemble classification model uses several effective features extracted from images and is based on majority vote. This model can be used as a screening system to diagnose the grade of invasive ductal carcinoma of the breast. RESULTS: Evaluations have been done using two histopathological microscopic datasets including patients with invasive ductal carcinoma of the breast. With extracting high-level features with average accuracies of 92.65% and 93.34% in these two datasets, the proposed method has succeeded in quickly diagnosing and classifying breast cancer with high performance. CONCLUSION: By combining deep neural networks and extracting features affecting breast cancer, the ability to diagnose with the highest accuracy is provided, and this is a step toward helping specialists and increasing the chances of patients' survival.

Dynamic silver speciation as studied with fluorous-phase ion-selective electrodes: Effect of natural organic matter on the toxicity and speciation of silver.

The widespread application of silver in consumer products and the resulting contamination of natural environments with silver raise questions about the toxicity of Ag(+) in the ecosystem. Natural organic matter, NOM, which is abundant in water supplies, soil, and sediments, can form stable complexes with Ag(+), altering its bioavailability and toxicity. Herein, the extent and kinetics of Ag(+) binding to NOM, matrix effects on Ag(+) binding to NOM, and the effect of NOM on Ag(+) toxicity to Shewanella oneidensis MR-1 (assessed by the BacLight viability assay) were quantitatively studied with fluorous-phase Ag(+) ion-selective electrodes (ISEs). Our findings show fast kinetics of Ag(+) and NOM binding, weak Ag(+) binding for Suwannee River humic acid, fulvic acid, and aquatic NOM, and stronger Ag(+) binding for Pony Lake fulvic acid and Pahokee Peat humic acid. We quantified the effects of matrix components and pH on Ag(+) binding to NOM, showing that the extent of binding greatly depends on the environmental conditions. The effect of NOM on the toxicity of Ag(+) does not correlate with the extent of Ag(+) binding to NOM, and other forms of silver, such as Ag(+) reduced by NOM, are critical for understanding the effect of NOM on Ag(+) toxicity. This work also shows that fluorous-phase Ag(+) ISEs are effective tools for studying Ag(+) binding to NOM because they can be used in a time-resolved manner to monitor the activity of Ag(+) in situ with high selectivity and without the need for extensive sample preparation.

Effects of Humic and Fulvic Acids on Silver Nanoparticle Stability, Dissolution, and Toxicity.

The colloidal stability of silver nanoparticles (AgNPs) in natural aquatic environments influences their transport and environmental persistence, while their dissolution to Ag(+) influences their toxicity to organisms. Here, we characterize the colloidal stability, dissolution behavior, and toxicity of two industrially relevant classes of AgNPs (i.e., AgNPs stabilized by citrate or polyvinylpyrrolidone) after exposure to natural organic matter (NOM, i.e., Suwannee River Humic and Fulvic Acid Standards and Pony Lake Fulvic Acid Reference). We show that NOM interaction with the nanoparticle surface depends on (i) the NOM's chemical composition, where sulfur- and nitrogen-rich NOM more significantly increases colloidal stability, and (ii) the affinity of the capping agent for the AgNP surface, where nanoparticles with loosely bound capping agents are more effectively stabilized by NOM. Adsorption of NOM is shown to have little effect on AgNP dissolution under most experimental conditions, the exception being when the NOM is rich in sulfur and nitrogen. Similarly, the toxicity of AgNPs to a bacterial model (Shewanella oneidensis MR-1) decreases most significantly in the presence of sulfur- and nitrogen-rich NOM. Our data suggest that the rate of AgNP aggregation and dissolution in aquatic environments containing NOM will depend on the chemical composition of the NOM, and that the toxicity of AgNPs to aquatic microorganisms is controlled primarily by the extent of nanoparticle dissolution.