Syntenin-1 is a promoter and prognostic marker of head and neck squamous cell carcinoma invasion and metastasis.

Metastasis represents a key factor associated with poor prognosis of head and neck squamous cell carcinoma (HNSC). However, the underlying molecular mechanisms remain largely unknown. In this study, our liquid chromatography with tandem mass spectrometry analysis revealed a number of significantly differentially expressed membrane/membrane-associated proteins between high invasive UM1 and low invasive UM2 cells. One of the identified membrane proteins, Syntenin-1, was remarkably up-regulated in HNSC tissues and cell lines when compared to the controls, and also over-expressed in recurrent HNSC and high invasive UM1 cells. Syntenin-1 over-expression was found to be significantly associated with lymph node metastasis and disease recurrence. HNSC patients with higher syntenin-1 expression had significantly poorer long term overall survival and similar results were found in many other types of cancers based on analysis of The Cancer Genome Atlas data. Finally, knockdown of syntenin-1 inhibited the proliferation, migration and invasion of HNSC cells, and opposite findings were observed when syntenin-1 was over-expressed. Collectively, our studies indicate that syntenin-1 promotes invasion and progression of HNSC. It may serve as a valuable biomarker for lymph node metastasis or a potential target for therapeutic intervention in HNSC

Metabolomic analysis of human oral cancer cells with adenylate kinase 2 or phosphorylate glycerol kinase 1 inhibition.

The purpose of this study was to use liquid chromatography-mass spectrometry (LC-MS) with XCMS for a quantitative metabolomic analysis of UM1 and UM2 oral cancer cells after knockdown of metabolic enzyme adenylate kinase 2 (AK2) or phosphorylate glycerol kinase 1 (PGK1). UM1 and UM2 cells were initially transfected with AK2 siRNA, PGK1 siRNA or scrambled control siRNA, and then analyzed with LC-MS for metabolic profiles. XCMS analysis of the untargeted metabolomics data revealed a total of 3200-4700 metabolite features from the transfected UM1 or UM2 cancer cells and 369-585 significantly changed metabolites due to AK2 or PGK1 suppression. In addition, cluster analysis showed that a common group of metabolites were altered by AK2 knockdown or by PGK1 knockdown between the UM1 and UM2 cells. However, the set of significantly changed metabolites due to AK2 knockdown was found to be distinct from those significantly changed by PGK1 knockdown. Our study has demonstrated that LC-MS with XCMS is an efficient tool for metabolomic analysis of oral cancer cells, and knockdown of different genes results in distinct changes in metabolic phenotypes in oral cancer cells

Towards Low-Latency Byzantine Agreement Protocols Using RDMA

Byzantine fault tolerance (BFT) protocols can mitigate attacks and errors and are increasingly investigated as consensus protocols in blockchains. However, they are traditionally considered costly in terms of message complexity and latency due to the required multiple rounds of message exchanges. With the availability of Remote Direct Memory Access (RDMA) in data centers, message exchange latency can be reduced compared to TCP, as RDMA enables kernel bypassing and thereby avoids intermediate data copying. Retaining the performance benefits for RDMA during its integration, however, is non-trivial and error-prone. While the use of RDMA has previously been explored for key/value stores, databases and distributed file systems, agreement protocols especially for BFT have so far been neglected. We investigate the usage of RDMA in the Reptor BFT protocol for low-latency agreement and show first steps towards an RDMA-enabled consensus protocol. For this, we present RUBIN, a framework offering similar functionality to the Java NIO selector, which can handle multiple network connections efficiently with a single thread and is employed in several BFT protocol implementations such as BFT-SMART and UpRight

Potential protein biomarkers for burning mouth syndrome discovered by quantitative proteomics.

Burning mouth syndrome (BMS) is a chronic pain disorder characterized by severe burning sensation in normal looking oral mucosa. Diagnosis of BMS remains to be a challenge to oral healthcare professionals because the method for definite diagnosis is still uncertain. In this study, a quantitative saliva proteomic analysis was performed in order to identify target proteins in BMS patients' saliva that may be used as biomarkers for simple, non-invasive detection of the disease. By using isobaric tags for relative and absolute quantitation labeling and liquid chromatography-tandem mass spectrometry to quantify 1130 saliva proteins between BMS patients and healthy control subjects, we found that 50 proteins were significantly changed in the BMS patients when compared to the healthy control subjects ( p ≤ 0.05, 39 up-regulated and 11 down-regulated). Four candidates, alpha-enolase, interleukin-18 (IL-18), kallikrein-13 (KLK13), and cathepsin G, were selected for further validation. Based on enzyme-linked immunosorbent assay measurements, three potential biomarkers, alpha-enolase, IL-18, and KLK13, were successfully validated. The fold changes for alpha-enolase, IL-18, and KLK13 were determined as 3.6, 2.9, and 2.2 (burning mouth syndrome vs. control), and corresponding receiver operating characteristic values were determined as 0.78, 0.83, and 0.68, respectively. Our findings indicate that testing of the identified protein biomarkers in saliva might be a valuable clinical tool for BMS detection. Further validation studies of the identified biomarkers or additional candidate biomarkers are needed to achieve a multi-marker prediction model for improved detection of BMS with high sensitivity and specificity

Supervised color image segmentation, using LVQ networks and K-means. Application: cellular image

This paper proposes a new method for supervised color image classification by theKohonen map, based on LVQ algorithms. The sample of observations, constituted by image pixels with 3 color components in the color space, is at first projected into a Kohonen map. This map is represented in the 3-dimensional space, from the weight vectors resulting of the learning process . Image classification by kohonen is a low-level image processing task that aims at partitioning an image into homogeneous regions. How region homogeneity is defined depends on the application. In this paper color image quantisation by clustering is discussed. A clustering scheme, based on learning quantisation vector (LVQ), is constructed and compared to the K-means clustering algorithm. It is demonstrated that both perform equally well. However, the former performs better than the latter with respect to the known number of although class. Both depend on their initial conditions and may end up in local optima. Based on these findings, an LVQ scheme is constructed which is completely independent of initial conditions; this approach is a hybrid structure between competitive learning and splitting of the color space. For comparison, a K-means approach is applied; it is known to produce global optimal results, but with high computational load. The clustering scheme is shown to obtain near-global optimal results with low computational loadKeywords: color image, kohonen, LVQ, classification, K-mean

The Expression Levels of XLF and Mutant P53 Are Inversely Correlated in Head and Neck Cancer Cells.

XRCC4-like factor (XLF), also known as Cernunnos, is a protein encoded by the human NHEJ1 gene and an important repair factor for DNA double-strand breaks. In this study, we have found that XLF is over-expressed in HPV(+) versus HPV(-) head and neck squamous cell carcinoma (HNSCC) and significantly down-regulated in the HNSCC cell lines expressing high level of mutant p53 protein versus those cell lines harboring wild-type TP53 gene with low p53 protein expression. We have also demonstrated that Werner syndrome protein (WRN), a member of the NHEJ repair pathway, binds to both mutant p53 protein and NHEJ1 gene promoter, and siRNA knockdown of WRN leads to the inhibition of XLF expression in the HNSCC cells. Collectively, these findings suggest that WRN and p53 are involved in the regulation of XLF expression and the activity of WRN might be affected by mutant p53 protein in the HNSCC cells with aberrant TP53 gene mutations, due to the interaction of mutant p53 with WRN. As a result, the expression of XLF in these cancer cells is significantly suppressed. Our study also suggests that XLF is over-expressed in HPV(+) HNSCC with low expression of wild type p53, and might serve as a potential biomarker for HPV(+) HNSCC. Further studies are warranted to investigate the mechanisms underlying the interactive role of WRN and XLF in NHEJ repair pathway

Oral cancer cells may rewire alternative metabolic pathways to survive from siRNA silencing of metabolic enzymes.

BackgroundCancer cells may undergo metabolic adaptations that support their growth as well as drug resistance properties. The purpose of this study is to test if oral cancer cells can overcome the metabolic defects introduced by using small interfering RNA (siRNA) to knock down their expression of important metabolic enzymes.MethodsUM1 and UM2 oral cancer cells were transfected with siRNA to transketolase (TKT) or siRNA to adenylate kinase (AK2), and Western blotting was used to confirm the knockdown. Cellular uptake of glucose and glutamine and production of lactate were compared between the cancer cells with either TKT or AK2 knockdown and those transfected with control siRNA. Statistical analysis was performed with student T-test.ResultsDespite the defect in the pentose phosphate pathway caused by siRNA knockdown of TKT, the survived UM1 or UM2 cells utilized more glucose and glutamine and secreted a significantly higher amount of lactate than the cells transferred with control siRNA. We also demonstrated that siRNA knockdown of AK2 constrained the proliferation of UM1 and UM2 cells but similarly led to an increased uptake of glucose/glutamine and production of lactate by the UM1 or UM2 cells survived from siRNA silencing of AK2.ConclusionsOur results indicate that the metabolic defects introduced by siRNA silencing of metabolic enzymes TKT or AK2 may be compensated by alternative feedback metabolic mechanisms, suggesting that cancer cells may overcome single defective pathways through secondary metabolic network adaptations. The highly robust nature of oral cancer cell metabolism implies that a systematic medical approach targeting multiple metabolic pathways may be needed to accomplish the continued improvement of cancer treatment

Characterization of Electronic Cigarette Aerosol and Its Induction of Oxidative Stress Response in Oral Keratinocytes.

In this study, we have generated and characterized Electronic Cigarette (EC) aerosols using a combination of advanced technologies. In the gas phase, the particle number concentration (PNC) of EC aerosols was found to be positively correlated with puff duration whereas the PNC and size distribution may vary with different flavors and nicotine strength. In the liquid phase (water or cell culture media), the size of EC nanoparticles appeared to be significantly larger than those in the gas phase, which might be due to aggregation of nanoparticles in the liquid phase. By using in vitro high-throughput cytotoxicity assays, we have demonstrated that EC aerosols significantly decrease intracellular levels of glutathione in NHOKs in a dose-dependent fashion resulting in cytotoxicity. These findings suggest that EC aerosols cause cytotoxicity to oral epithelial cells in vitro, and the underlying molecular mechanisms may be or at least partially due to oxidative stress induced by toxic substances (e.g., nanoparticles and chemicals) present in EC aerosols

Modélisation de la rétention de phénols séparés par CLHP – PI avec une phase mobile méthanol – eau

La rétention (log k) d’un mélange hétérogène de phénols séparés en régime isochratique par CLHP – PI, sur une colonne Partisil ODS, avec une phase mobile méthanol – eau a été reliée aux conditions d’analyse (température T ; fraction volumique, φ, du co-solvant organique) et au coefficient de partage n-octanol / eau de Moriguchi (M log P) calculé à l’aide du logiciel DRAGON. L’ensemble de calibrage (40 éléments), obtenu en appliquant l’algorithme DUPLEX, permet de calculer un modèle vérifiant les hypothèses d’un modèle statistique linéaire à effets fixes, robuste, et dont la capacité de prédiction interne n’est pas trop dissemblable de son pouvoir d’ajustement. La validation statistique externe, sur un ensemble test de 26 éléments, atteste de la bonne capacité prédictive des log k n’ayant pas servi au calcul du modèle.Mots-clés: Phénols – CLHP / PI – Rétention –Modèle QSRR.La rétention (log k) d’un mélange hétérogène de phénols séparés en régime isochratique par CLHP – PI, sur une colonne Partisil ODS, avec une phase mobile méthanol – eau a été reliée aux conditions d’analyse (température T ; fraction volumique, φ, du co-solvant organique) et au coefficient de partage n-octanol / eau de Moriguchi (M log P) calculé à l’aide du logiciel DRAGON. L’ensemble de calibrage (40 éléments), obtenu en appliquant l’algorithme DUPLEX, permet de calculer un modèle vérifiant les hypothèses d’un modèle statistique linéaire à effets fixes, robuste, et dont la capacité de prédiction interne n’est pas trop dissemblable de son pouvoir d’ajustement. La validation statistique externe, sur un ensemble test de 26 éléments, atteste de la bonne capacité prédictive des log k n’ayant pas servi au calcul du modèle.Mots-clés: Phénols – CLHP / PI – Rétention –Modèle QSRR