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To inject skip similarity into a GNN, we construct a modified version of the original network, called the skip graph. We then develop an iterative fusion scheme that optimizes a GNN using both the skip graph and the original graph. Experiments on four interaction networks, including drug-drug, drug-target, protein-protein, and gene-disease interactions, show that SkipGNN achieves superior and robust performance. Furthermore, we show that unlike popular GNNs, SkipGNN learns biologically meaningful embeddings and performs especially well on noisy, incomplete interaction networks.A numerical analysis of a hexagonal PCF structure with four circular air hole rings around the core has been presented in this paper. By utilizing a full vectorial finite element method with perfectly matched layers, propagation properties such as birefringence, chromatic dispersion and confinement losses are numericaly evaluated for the proposed PCF structure. Specifically, birefringence of 2.018 × 10-2, nonlinear coefficients of 40.682 W-1 km-1, negative chromatic dispersion of - 47.72 ps/km.nm at 1.55 µm and - 21 to - 105 ps/km.nm at the telecommunication band of C-U have been reported.Hepatic fibrogenesis is characterized by activation of hepatic stellate cells (HSCs) and accumulation of extracellular matrix (ECM). The impact of ECM on TGF-β-mediated fibrogenic signaling pathway in HSCs has remained obscure. We studied the role of non-receptor tyrosine kinase focal adhesion kinase (FAK) family members in TGF-β-signaling in HSCs. We used a CCl4-induced liver fibrosis mice model to evaluate the effect of FAK family kinase inhibitors on liver fibrosis. RT-PCR and Western blot were used to measure the expression of its target genes; α-SMA, collagen, Nox4, TGF-β1, Smad7, and CTGF. Pharmacological inhibitors, siRNA-mediated knock-down, and plasmid-based overexpression were adopted to modulate the function and the expression level of proteins. Association of PYK2 activation with liver fibrosis was confirmed in liver samples from CCl4-treated mice and patients with significant fibrosis or cirrhosis. TGF-β treatment up-regulated expression of α-SMA, type I collagen, NOX4, CTGF, TGF-β1, and Smad7 in LX-2 cells. Inhibition of FAK family members suppressed TGF-β-mediated fibrogenic signaling. SiRNA experiments demonstrated that TGF-β1 and Smad7 were upregulated via Smad-dependent pathway through FAK activation. In addition, CTGF induction was Smad-independent and PYK2-dependent. Furthermore, RhoA activation was essential for TGF-β-mediated CTGF induction, evidenced by using ROCK inhibitor and dominant negative RhoA expression. We identified that TGF-β1-induced activation of PYK2-Src-RhoA triad leads to YAP/TAZ activation for CTGF induction in liver fibrosis. These findings provide new insights into the role of focal adhesion molecules in liver fibrogenesis, and targeting PYK2 may be an attractive target for developing novel therapeutic strategies for the treatment of liver fibrosis.Multiple therapeutic proteins can be combined into a single dose for synergistic targeting to multiple sites of action. Such proteins would be mixed in dose-specific ratios to provide the correct potency for each component, and yet the formulations must also preserve their activity and keep degradation to a minimum. Mixing different therapeutic proteins could adversely affect their stability, and reduce the shelf life of each individual component, making the control of such products very challenging. In this study, a therapeutic monoclonal antibody and a related Fab fragment, were combined to investigate the impact of coformulation on their degradation kinetics. Under mildly destabilizing conditions, these proteins were found to protect each other from degradation. The protective effect appeared to originate from the interaction of Fab and IgG1 in small soluble oligomers, or through the rapid coalescence of pre-existing monomeric IgG1 nuclei into a dead-end aggregate, rather than through macromolecular crowding or diffusion-limitations.This study aimed to investigate the predictive efficacy of positron emission tomography/computed tomography (PET/CT) and magnetic resonance imaging (MRI) for the pathological response of advanced breast cancer to neoadjuvant chemotherapy (NAC). The breast PET/MRI image deep learning model was introduced and compared with the conventional methods. PET/CT and MRI parameters were evaluated before and after the first NAC cycle in patients with advanced breast cancer [n = 56; all women; median age, 49 (range 26-66) years]. The maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were obtained with the corresponding baseline values (SUV0, MTV0, and TLG0, respectively) and interim PET images (SUV1, MTV1, and TLG1, respectively). Mean apparent diffusion coefficients were obtained from baseline and interim diffusion MR images (ADC0 and ADC1, respectively). The differences between the baseline and interim parameters were measured (ΔSUV, ΔMTV, ΔTLG, and ΔADC). Subgroup analysis was performed for the HER2-negative and triple-negative groups. Datasets for convolutional neural network (CNN), assigned as training (80%) and test datasets (20%), were cropped from the baseline (PET0, MRI0) and interim (PET1, MRI1) images. Histopathologic responses were assessed using the Miller and Payne system, after three cycles of chemotherapy. Receiver operating characteristic curve analysis was used to assess the performance of the differentiating responders and non-responders. There were six responders (11%) and 50 non-responders (89%). The area under the curve (AUC) was the highest for ΔSUV at 0.805 (95% CI 0.677-0.899). The AUC was the highest for ΔSUV at 0.879 (95% CI 0.722-0.965) for the HER2-negative subtype. AUC improved following CNN application (SUV0PET0 = 0.6520.886, SUV1PET1 = 0.6870.980, and ADC1MRI1 = 0.5370.701), except for ADC0 (ADC0MRI0 = 0.7030.602). PET/MRI image deep learning model can predict pathological responses to NAC in patients with advanced breast cancer.The flow inside the perivascular space (PVS) is modeled using a first-principles approach in order to investigate how the cerebrospinal fluid (CSF) enters the brain through a permeable layer of glial cells. Lubrication theory is employed to deal with the flow in the thin annular gap of the perivascular space between an impermeable artery and the brain tissue. The artery has an imposed peristaltic deformation and the deformable brain tissue is modeled by means of an elastic Hooke's law. The perivascular flow model is solved numerically, discovering that the peristaltic wave induces a steady streaming to/from the brain which strongly depends on the rigidity and the permeability of the brain tissue. Tipifarnib concentration A detailed quantification of the through flow across the glial boundary is obtained for a large parameter space of physiologically relevant conditions. The parameters include the elasticity and permeability of the brain, the curvature of the artery, its length and the amplitude of the peristaltic wave. A steady streaming component of the through flow due to the peristaltic wave is characterized by an in-depth physical analysis and the velocity across the glial layer is found to flow from and to the PVS, depending on the elasticity and permeability of the brain.
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