Pegylated ADI is a promising drug that capitalizes on a significant enzymatic deficiency in HCC. It is safe, well tolerated, and may benefit patients with unresectable HCC.

BackgroundHuman pancreatic cancer is currently one of the deadliest cancers with high mortality rate. It has been previously shown that (−)-epigallocatechin-3-gallate (EGCG), the most abundant catechin found in green tea, has showed suppressive effects on human pancreatic cancer cells. Bleomycin, (BLM), an anti-cancer chemotherapeutic drug that induces DNA damage, has antitumor effects by induction of apoptosis in several cancer cell lines and also in pancreatic cancer cells. The present study investigated for the first time, the inhibitory effect of EGCG and BLM on pancreatic cancer cell growth.MethodsUsing the pancreatic cancer cell lines MIA PaCa-2 cells the efficacy and synergism of EGCG and BLM were evaluated by in vitro tests. Inhibition of cell proliferation was determined by MTT assay. Mitochondrial depolarization was performed with JC-1 probe. Viability and apoptosis were determined by Flow Cytometry with annexin V, propidium iodide staining and DNA fragmentation assay.ResultsCell proliferation assay revealed significant additive inhibitory effects with combination of EGCG and BLM at 72 h in a dose dependent manner. The combination of EGCG and BLM induced cell cycle S-phase arrest and mitochondrial depolarization. Viability, apoptosis and DNA fragmentation assay indicated that the combination of EGCG and bleomycin potentiated apoptosis.ConclusionsOur results indicate that EGCG and BLM have additive anti-proliferative effects in vitro by induction of apoptosis of MIA PaCa-2 cells. This combination could represent a new strategy with potential advantages for treatment of pancreatic cancer. To date, this is the first report published of the inhibitory effect of EGCG and BLM on human pancreatic cancer MIA Paca-2 cell growth.

Angiosarcoma of the breast is one of the rarest malignancies. Breast angiosarcoma can be classified into primary when arising de novo and secondary to chronic lymphoedema or breast irradiation. Molecular pathways involved in angiosarcoma development have not been described clearly, yet some gene point mutations and protein altered expression levels have been detected. So far, their management is based above all on surgery. Hence, further studies starting from the few known key points may help to develop more effective strategies based both on target therapies, together with surgery.

The aim of the study was to estimate the diagnostic accuracy of textural features extracted by dual-energy contrast-enhanced mammography (CEM) images, by carrying out univariate and multivariate statistical analyses including artificial intelligence approaches. In total, 80 patients with known breast lesion were enrolled in this prospective study according to regulations issued by the local Institutional Review Board. All patients underwent dual-energy CEM examination in both craniocaudally (CC) and double acquisition of mediolateral oblique (MLO) projections (early and late). The reference standard was pathology from a surgical specimen for malignant lesions and pathology from a surgical specimen or fine needle aspiration cytology, core or Tru-Cut needle biopsy, and vacuum assisted breast biopsy for benign lesions. In total, 104 samples of 80 patients were analyzed. Furthermore, 48 textural parameters were extracted by manually segmenting regions of interest. Univariate and multivariate approaches were performed: non-parametric Wilcoxon–Mann–Whitney test; receiver operating characteristic (ROC), linear classifier (LDA), decision tree (DT), k-nearest neighbors (KNN), artificial neural network (NNET), and support vector machine (SVM) were utilized. A balancing approach and feature selection methods were used. The univariate analysis showed low accuracy and area under the curve (AUC) for all considered features. Instead, in the multivariate textural analysis, the best performance considering the CC view (accuracy (ACC) = 0.75; AUC = 0.82) was reached with a DT trained with leave-one-out cross-variation (LOOCV) and balanced data (with adaptive synthetic (ADASYN) function) and a subset of three robust textural features (MAD, VARIANCE, and LRLGE). The best performance (ACC = 0.77; AUC = 0.83) considering the early-MLO view was reached with a NNET trained with LOOCV and balanced data (with ADASYN function) and a subset of ten robust features (MEAN, MAD, RANGE, IQR, VARIANCE, CORRELATION, RLV, COARSNESS, BUSYNESS, and STRENGTH). The best performance (ACC = 0.73; AUC = 0.82) considering the late-MLO view was reached with a NNET trained with LOOCV and balanced data (with ADASYN function) and a subset of eleven robust features (MODE, MEDIAN, RANGE, RLN, LRLGE, RLV, LZLGE, GLV_GLSZM, ZSV, COARSNESS, and BUSYNESS). Multivariate analyses using pattern recognition approaches, considering 144 textural features extracted from all three mammographic projections (CC, early MLO, and late MLO), optimized by adaptive synthetic sampling and feature selection operations obtained the best results (ACC = 0.87; AUC = 0.90) and showed the best performance in the discrimination of benign and malignant lesions.