Hydrogen peroxide production in an atmospheric pressure RF glow discharge: comparison of models and experiments

The production of (Formula presented.) in an atmospheric pressure RF glow discharge in helium-water vapor mixtures has been investigated as a function of plasma dissipated power, water concentration, gas flow (residence time) and power modulation of the plasma. (Formula presented.) concentrations up to 8 ppm in the gas phase and a maximum energy efficiency of 0.12 g/kWh are found. The experimental results are compared with a previously reported global chemical kinetics model and a one dimensional (1D) fluid model to investigate the chemical processes involved in (Formula presented.) production. An analytical balance of the main production and destruction mechanisms of (Formula presented.) is made which is refined by a comparison of the experimental data with a previously published global kinetic model and a 1D fluid model. In addition, the experiments are used to validate and refine the computational models. Accuracies of both model and experiment are discussed

Tumor cell-derived PDGF-B potentiates mouse mesenchymal stem cells-pericytes transition and recruitment through an interaction with NRP-1

<p>Abstract</p> <p>Background</p> <p>New blood vessel formation, or angiogenic switch, is an essential event in the development of solid tumors and their metastatic growth. Tumor blood vessel formation and remodeling is a complex and multi-step processes. The differentiation and recruitment of mural cells including vascular smooth muscle cells and pericytes are essential steps in tumor angiogenesis. However, the role of tumor cells in differentiation and recruitment of mural cells has not yet been fully elucidated. This study focuses on the role of human tumor cells in governing the differentiation of mouse mesenchymal stem cells (MSCs) to pericytes and their recruitment in the tumor angiogenesis process.</p> <p>Results</p> <p>We show that C3H/10T1/2 mouse embryonic mesenchymal stem cells, under the influence of different tumor cell-derived conditioned media, differentiate into mature pericytes. These differentiated pericytes, in turn, are recruited to bind with capillary-like networks formed by endothelial cells on the matrigel under <it>in vitro </it>conditions and recruited to bind with blood vessels on gel-foam under <it>in vivo </it>conditions. The degree of recruitment of pericytes into <it>in vitro </it>neo-angiogenesis is tumor cell phenotype specific. Interestingly, invasive cells recruit less pericytes as compared to non-invasive cells. We identified tumor cell-secreted platelet-derived growth factor-B (PDGF-B) as a crucial factor controlling the differentiation and recruitment processes through an interaction with neuropilin-1 (NRP-1) in mesenchymal stem cells.</p> <p>Conclusion</p> <p>These new insights into the roles of tumor cell-secreted PDGF-B-NRP-1 signaling in MSCs-fate determination may help to develop new antiangiogenic strategies to prevent the tumor growth and metastasis and result in more effective cancer therapies.</p

Cyr61/CCN1 signaling is critical for epithelial-mesenchymal transition and stemness and promotes pancreatic carcinogenesis

<p>Abstract</p> <p>Background</p> <p>Despite recent advances in outlining the mechanisms involved in pancreatic carcinogenesis, precise molecular pathways and cellular lineage specification remains incompletely understood.</p> <p>Results</p> <p>We show here that Cyr61/CCN1 play a critical role in pancreatic carcinogenesis through the induction of EMT and stemness. Cyr61 mRNA and protein were detected in the early precursor lesions and their expression intensified with disease progression. Cyr61/CCN1 expression was also detected in different pancreatic cancer cell lines. The aggressive cell lines, in which the expressions of mesenchymal/stem cell molecular markers are predominant; exhibit more Cyr61/CCN1 expression. Cyr61 expression is exorbitantly higher in cancer stem/tumor initiating Panc-1-side-population (SP) cells. Upon Cyr61/CCN1 silencing, the aggressive behaviors are reduced by obliterating interlinking pathobiological events such as reversing the EMT, blocking the expression of stem-cell-like traits and inhibiting migration. In contrast, addition of Cyr61 protein in culture medium augments EMT and stemness features in relatively less aggressive BxPC3 pancreatic cancer cells. Using a xenograft model we demonstrated that cyr61/CCN1 silencing in Panc-1-SP cells reverses the stemness features and tumor initiating potency of these cells. Moreover, our results imply a miRNA-based mechanism for the regulation of aggressive behaviors of pancreatic cancer cells by Cyr61/CCN1.</p> <p>Conclusions</p> <p>In conclusion, the discovery of the involvement of Cyr61/CCN1 in pancreatic carcinogenesis may represent an important marker for PDAC and suggests Cyr61/CCN1 can be a potential cancer therapeutic target.</p

Amorphous carbon film deposition on inner surface of tubes using atmospheric pressure pulsed filamentary plasma source

Uniform amorphous carbon film is deposited on the inner surface of quartz tube having the inner diameter of 6 mm and the outer diameter of 8 mm. A pulsed filamentary plasma source is used for the deposition. Long plasma filaments (~ 140 mm) as a positive discharge are generated inside the tube in argon with methane admixture. FTIR-ATR, XRD, SEM, LSM and XPS analyses give the conclusion that deposited film is amorphous composed of non-hydrogenated sp2 carbon and hydrogenated sp3 carbon. Plasma is characterized using optical emission spectroscopy, voltage-current measurement, microphotography and numerical simulation. On the basis of observed plasma parameters, the kinetics of the film deposition process is discussed

Adversarial attacks and adversarial robustness in computational pathology.

Artificial Intelligence (AI) can support diagnostic workflows in oncology by aiding diagnosis and providing biomarkers directly from routine pathology slides. However, AI applications are vulnerable to adversarial attacks. Hence, it is essential to quantify and mitigate this risk before widespread clinical use. Here, we show that convolutional neural networks (CNNs) are highly susceptible to white- and black-box adversarial attacks in clinically relevant weakly-supervised classification tasks. Adversarially robust training and dual batch normalization (DBN) are possible mitigation strategies but require precise knowledge of the type of attack used in the inference. We demonstrate that vision transformers (ViTs) perform equally well compared to CNNs at baseline, but are orders of magnitude more robust to white- and black-box attacks. At a mechanistic level, we show that this is associated with a more robust latent representation of clinically relevant categories in ViTs compared to CNNs. Our results are in line with previous theoretical studies and provide empirical evidence that ViTs are robust learners in computational pathology. This implies that large-scale rollout of AI models in computational pathology should rely on ViTs rather than CNN-based classifiers to provide inherent protection against perturbation of the input data, especially adversarial attacks