BackgroundDifferentiating pancreatic cancer (PC) from normal tissue by computer-aided diagnosis of EUS images were quite useful. The current study was designed to investigate the feasibility of using computer-aided diagnostic (CAD) techniques to extract EUS image parameters for the differential diagnosis of PC and chronic pancreatitis (CP).Methodology/Principal FindingsThis study recruited 262 patients with PC and 126 patients with CP. Typical EUS images were selected from the sample sets. Texture features were extracted from the region of interest using computer-based techniques. Then the distance between class algorithm and sequential forward selection (SFS) algorithm were used for a better combination of features; and, later, a support vector machine (SVM) predictive model was built, trained, and validated. Overall, 105 features of 9 categories were extracted from the EUS images for pattern classification. Of these features, the 16 were selected as a better combination of features. Then, SVM predictive model was built and trained. The total cases were randomly divided into a training set and a testing set. The training set was used to train the SVM, and the testing set was used to evaluate the performance of the SVM. After 200 trials of randomised experiments, the average accuracy, sensitivity, specificity, the positive and negative predictive values of pancreatic cancer were 94.2±0.1749%,96.25±0.4460%, 93.38±0.2076%, 92.21±0.4249% and 96.68±0.1471%, respectively.Conclusions/SignificanceDigital image processing and computer-aided EUS image differentiation technologies are highly accurate and non-invasive. This technology provides a kind of new and valuable diagnostic tool for the clinical determination of PC.

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Background Discovery and development of novel drugs that are capable of overcoming drug resistance in tumor cells are urgently needed clinically. In this study, we sought to explore whether ivermectin (IVM), a macrolide antiparasitic agent, could overcome the resistance of cancer cells to the therapeutic drugs. Methods We used two solid tumor cell lines (HCT-8 colorectal cancer cells and MCF-7 breast cancer cells) and one hematologic tumor cell line (K562 chronic myeloid leukemia cells), which are resistant to the chemotherapeutic drugs vincristine and adriamycin respectively, and two xenograft mice models, including the solid tumor model in nude mice with the resistant HCT-8 cells and the leukemia model in NOD/SCID mice with the resistant K562 cells to investigate the reversal effect of IVM on the resistance in vitro and in vivo . MTT assay was used to investigate the effect of IVM on cancer cells growth in vitro . Flow cytometry, immunohistochemistry, and immunofluorescence were performed to investigate the reversal effect of IVM in vivo . Western blotting, qPCR, luciferase reporter assay and ChIP assay were used to detect the molecular mechanism of the reversal effect. Octet RED96 system and Co-IP were used to determine the interactions between IVM and EGFR. Results Our results indicated that ivermectin at its very low dose, which did not induce obvious cytotoxicity, drastically reversed the resistance of the tumor cells to the chemotherapeutic drugs both in vitro and in vivo . Mechanistically, ivermectin reversed the resistance mainly by reducing the expression of P-glycoprotein (P-gp) via inhibiting the epidermal growth factor receptor (EGFR), not by directly inhibiting P-gp activity. Ivermectin bound with the extracellular domain of EGFR, which inhibited the activation of EGFR and its downstream signaling cascade ERK/Akt/NF-κB. The inhibition of the transcriptional factor NF-κB led to the reduced P-gp transcription. Conclusions These findings demonstrated that ivermectin significantly enhanced the anti-cancer efficacy of chemotherapeutic drugs to tumor cells, especially in the drug-resistant cells. Thus, ivermectin, a FDA-approved antiparasitic drug, could potentially be used in combination with chemotherapeutic agents to treat cancers and in particular, the drug-resistant cancers. Electronic supplementary material The online version of this article (10.1186/s13046-019-1251-7) contains supplementary material, which is available to authorized users.

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