Nanoformulation of Polyphenol Curcumin Enhances Cisplatin-Induced Apoptosis in Drug-Resistant MDA-MB-231 Breast Cancer Cells.

Triple Negative Breast Cancer (TNBC) is the aggressive and lethal type of breast malignancy that develops resistance to current therapies. Combination therapy has proven to be an effective strategy on TNBC. We aimed to study whether the nano-formulation of polyphenolic curcumin (Gemini-Cur) would affect the cisplatin-induced toxicity in MDA-MB-231 breast cancer cells. MDA-MB-231 cells were treated with Gemini-Cur, cisplatin and combination of Gemini-Cur/Cisplatin in a time- and dose-dependent manner. Cell viability was studied by using MTT, fluorescence microscopy and cell cycle assays. The mode of death was also determined by Hoechst staining and annexin V-FITC. Real-time PCR and western blotting were employed to detect the expression of BAX and BCL-2 genes. Our data demonstrated that Gemini-Cur significantly sensitizes cancer cells to cisplatin (combination index ≤ 1) and decreases IC50 values in comparison with Gemini-cur or cisplatin. Further studies confirmed that Gemini-Cur/Cisplatin suppresses cancer cell growth through induction of apoptosis (p < 0.001). In conclusion, the data confirm the synergistic effect of polyphenolic curcumin on cisplatin toxicity and provide attractive strategy to attain its apoptotic effect on TNBC.

A Perceptual Distinguishability Predictor For JND-noise-contaminated Images.

Just noticeable difference (JND) models are widely used for perceptual redundancy estimation in images and videos. A common method for measuring the accuracy of a JND model is to inject random noise in an image based on the JND model, and check whether the JND-noise-contaminated image is perceptually distinguishable from the original image or not. Also, when comparing the accuracy of two different JND models, the model that produces the JND-noise-contaminated image with better quality at the same level of noise energy is the better model. But in both of these cases, a subjective test is necessary, which is very time consuming and costly. In this paper, we present a full-reference metric called PDP (perceptual distinguishability predictor), which can be used to determine whether a given JND-noise-contaminated image is perceptually distinguishable from the reference image. The proposed metric employs the concept of sparse coding, and extracts a feature vector out of a given image pair. The feature vector is then fed to a multilayer neural network for classification. To train the network, we built a public database of 999 natural images with distinguishbility thresholds for four different JND models obtained from an extensive subjective experiment. The results indicated that PDD achieves high classification accuracy of 97.1%. The proposed method can be used to objectively compare various JND models without performing any subjective test. It can also be used to obtain proper scaling factors to improve the JND thresholds estimated by an arbitrary JND model.