Cyclodextrin Conjugated α-Bisabolol Suppresses FAK Phosphorylation and Induces Apoptosis in Pancreatic Cancer.

BACKGROUND/AIM: α-Bisabolol is an essential oil component extracted from plants, such as chamomile. We have previously reported that α-bisabolol suppressed proliferation, invasion, and motility of pancreas cancer. Cyclodextrin improved the solubility of α-bisabolol, therefore it enabled to administer intravenously. The aim of this study was to clarify the effect of cyclodextrin conjugated α-bisabolol (CD-BSB) and the signals pathways associated with α-bisabolol for pancreatic cancer. MATERIALS AND METHODS: Human pancreatic cancer cell lines were treated with or without CD-BSB. Cytomorphology and apoptosis were assessed in these treated groups. In addition, several phosphorylated proteins were analyzed to clarify the signal pathway concerning CD-BSB. In subcutaneous xenograft model, tumor volume and Ki-67 expression were evaluated among Control (untreated), CD-BSB, or Gemcitabine (GEM). RESULTS: CD-BSB significantly changed cytomorphology and induced apoptosis in pancreatic cancer cells. CD-BSB suppressed phosphorylation of focal adhesion kinase (FAK). In addition, pFAK 397 was inhibited by CD-BSB in a concentration-dependent manner in cancer cells. In the subcutaneous xenograft models, the tumor volume in the CD-BSB groups was lower than Control groups. Ki67-positive cells in CD-BSB treated group were lower than the GEM-treated groups. CONCLUSION: We clarified the efficiency of CD-BSB in xenograft tumor using intravenous administration. α-Bisabolol suppresses phosphorylation of FAK 397 and impairs cytoskeletal polymerization in a pancreatic cancer cell line. Further investigations are required to reveal the precise mechanisms of the antitumor effects of solubilized α-bisabolol to facilitate its clinical application. Our data indicate that solubilized α-bisabolol has therapeutic potential and could improve the prognosis of cancer patients.

Antitumor Effects of Deep Ultraviolet Irradiation for Pancreatic Cancer.

BACKGROUND/AIM: Deep ultraviolet (DUV) light spans within the 250 nm to 350 nm invisible wavelength range. Although it strongly damages various cells, the efficacy of DUV irradiation on pancreatic cancer cells has never been clarified. The purpose of this study was to reveal the antitumor effects of DUV irradiation on pancreatic cancer cells. MATERIALS AND METHODS: Human pancreatic cancer cell lines were eradicated with DUV or ultraviolet A (UVA) for 5 s. Several angiogenesis-related proteins were studied in cancer cells after DUV irradiation using a protein antibody array. A subcutaneous xenograft model was established by inoculation of pancreatic cancer cells into mice. Tumors in this model were irradiated with DUV or UVA once or twice for two weeks. Tumor volumes in these groups (DUV×1: one irradiation, DUV×2: two irradiations, and untreated) were analyzed one week after the second irradiation. RESULTS: DUV irradiation significantly changed the cytomorphology of pancreatic cancer cells. In addition, DUV irradiation induced apoptosis on pancreatic cancer cells more strongly than UVA irradiation and no irradiation. Interestingly, lower expression of thrombospondin 1 (TSP1) and tissue inhibitor of metalloproteinase 1 (TIMP1) was identified after DUV treatment. The tumor volume in the DUV-treated groups (DUV×1 and DUV×2) was smaller than that in the untreated group. CONCLUSION: Further investigations are required to reveal the precise mechanisms of the antitumor effects of DUV irradiation and to facilitate its clinical application as a new therapy for pancreatic cancer. Overall, DUV irradiation can be potentially used as a therapeutic option of pancreatic malignancy.

Trefoil factor 1 inhibits the development of esophageal adenocarcinoma from Barrett's epithelium.

Trefoil factor family 1 (TFF1) is one of three members of the trefoil factor family that are abundantly expressed in the gastrointestinal mucosal epithelium. Recent studies have shown that TFF1 acts as a tumor suppressor in gastric, pancreatic and hepatocellular carcinogenesis; however, little is known about its function in esophageal carcinogenesis, especially in esophageal adenocarcinoma (EAC). Barrett's epithelium is the metaplastic columnar epithelium of the esophagus and a known premalignant lesion of EAC. To investigate the role of TFF1 in EAC development, a mouse model of Barrett's epithelium was employed, and human specimens of EAC were assessed by immunohistochemistry (IHC) and methylation-specific PCR. Wild-type (WT) mice underwent gastrojejunostomy on the forestomach, resulting in the development of Barrett's epithelium-like (BE-like) epithelium adjacent to the anastomotic site. BE-like epithelium in these mice expressed TFF1, indicating the association of TFF1 with esophageal adenocarcinoma. TFF1-knockout (TFF1KO) mice underwent the same procedure as well, revealing that a deficiency in TFF1 resulted in the development of adenocarcinoma in the anastomotic site, presumably from BE-like epithelium. IHC of human samples revealed strong TFF1 expression in Barrett's epithelium, which was lost in some EACs, confirming the association between TFF1 and EAC development. Aberrant DNA hypermethylation in TFF1 promoter lesions was detected in TFF1-negative human EAC samples, further confirming not only the role of TFF1 in EAC but also the underlying mechanisms of TFF1 regulation. In addition, IHC revealed the nuclear translocation of ß-catenin in human and mouse EAC, suggesting that activation of the Wnt/ß-catenin pathway was induced by the loss of TFF1. In conclusion, these results indicate that TFF1 functions as a tumor suppressor to inhibit the development of esophageal carcinogenesis from Barrett's epithelium.

Cloth manipulation planning on basis of mesh representations with incomplete domain knowledge and voxel-to-mesh estimation.

Cloth manipulation is common in both housework and manufacturing. However, robotic cloth manipulation remains challenging, especially for less controlled and open-goal settings. We consider the problem of open-goal planning for robotic cloth manipulation, with focus on the roles of cloth representation and epistemic uncertainty. Core of our system is a neural network trained as a forward model of cloth behaviour under manipulation, with planning performed through backpropagation. We introduce a neural network-based routine for estimating mesh representations from voxel input, and perform planning in mesh format internally. We address the problem of planning with incomplete domain knowledge by introducing an explicit epistemic uncertainty penalty, using prediction divergence between two instances of the forward model network as a proxy of epistemic uncertainty. This allows us to avoid plans with high epistemic uncertainty during planning. Finally, we introduce logic for handling restriction of grasp points to a discrete set of candidates, in order to accommodate graspability constraints imposed by robotic hardware. We evaluate the system's mesh estimation, prediction, and planning ability on simulated cloth for sequences of one to three manipulations. Comparative experiments confirm that planning on basis of estimated meshes improves accuracy compared to voxel-based planning, and that epistemic uncertainty avoidance improves performance under conditions of incomplete domain knowledge. Planning time cost is a few seconds. We additionally present qualitative results on robot hardware. Our results indicate that representation format and epistemic uncertainty are important factors to consider for open-goal cloth manipulation planning.

Fast and Flexible Multi-Step Cloth Manipulation Planning Using an Encode-Manipulate-Decode Network (EM*D Net).

We propose a deep neural network architecture, the Encode-Manipulate-Decode (EM*D) net, for rapid manipulation planning on deformable objects. We demonstrate its effectiveness on simulated cloth. The net consists of 3D convolutional encoder and decoder modules that map cloth states to and from latent space, with a "manipulation module" in between that learns a forward model of the cloth's dynamics w.r.t. the manipulation repertoire, in latent space. The manipulation module's architecture is specialized for its role as a forward model, iteratively modifying a state representation by means of residual connections and repeated input at every layer. We train the network to predict the post-manipulation cloth state from a pre-manipulation cloth state and a manipulation input. By training the network end-to-end, we force the encoder and decoder modules to learn a latent state representation that facilitates modification by the manipulation module. We show that the network can achieve good generalization from a training dataset of 6,000 manipulation examples. Comparative experiments without the architectural specializations of the manipulation module show reduced performance, confirming the benefits of our architecture. Manipulation plans are generated by performing error back-propagation w.r.t. the manipulation inputs. Recurrent use of the manipulation network during planning allows for generation of multi-step plans. We show results for plans of up to three manipulations, demonstrating generally good approximation of the goal state. Plan generation takes <2.5 s for a three-step plan and is found to be robust to cloth self-occlusion, supporting the approach' viability for practical application.