Angiopoietins 1–4 (Ang1–4) represent an important family of growth factors, whose activities are mediated through the tyrosine kinase receptors, Tie1 and Tie2. The best characterized are angiopoietin-1 (Ang1) and angiopoietin-2 (Ang2). Ang1 is a potent angiogenic growth factor signaling through Tie2, whereas Ang2 was initially identified as a vascular disruptive agent with antagonistic activity through the same receptor. Recent data demonstrates that Ang2 has context-dependent agonist activities. Ang2 plays important roles in physiological processes and the deregulation of its expression is characteristic of several diseases. In this review, we summarize the activity of Ang2 on blood and lymphatic endothelial cells, its significance in human physiology and disease, and provide a current view of the molecular signaling pathways regulated by Ang2 in endothelial cells.

Anti-angiogenic approaches targeting the vascular endothelial growth factor (VEGF) signaling pathway have been a significant research focus during the past decades and are well established in clinical practice. Despite the expectations, their benefit is ephemeral in several diseases, including specific cancers. One of the most prominent side effects of the current, VEGF-based, anti-angiogenic treatments remains the development of resistance, mostly due to the upregulation and compensatory mechanisms of other growth factors, with the basic fibroblast growth factor (bFGF) being at the top of the list. Over the past decade, several anti-angiogenic approaches targeting simultaneously different growth factors and their signaling pathways have been developed and some have reached the clinical practice. In the present review, we summarize the knowledge regarding resistance mechanisms upon anti-angiogenic treatment, mainly focusing on bFGF. We discuss its role in acquired resistance upon prolonged anti-angiogenic treatment in different tumor settings, outline the reported resistance mechanisms leading to bFGF upregulation, and summarize the efforts and outcome of combined anti-angiogenic approaches to date.

Metastasis is considered a major burden in cancer, being responsible for more than 90% of cancer-related deaths. Tumor angiogenesis is one of the main processes that lead to tumor metastasis. Penfluridol is a classic and commonly used antipsychotic drug, which has a great ability to cross the blood–brain barrier. Recent studies have revealed that penfluridol has significant anti-cancer activity in diverse tumors, such as metastatic breast cancer and glioblastoma. Here, we aim to identify the effect of low doses of penfluridol on tumor microenvironment and compare it with its effect on tumor cells. Although low concentration of penfluridol was not toxic for endothelial cells, it blocked angiogenesis in vitro and in vivo. In vitro, penfluridol inhibited VEGF-induced primary endothelial cell migration and tube formation, and in vivo, it blocked VEGF- and FGF-induced angiogenesis in the matrigel plug assay. VEGF-induced VEGFR2 phosphorylation and the downstream p38 and ERK signaling pathways were not affected in endothelial cells, although VEGF-induced Src and Akt activation were abrogated by penfluridol treatment. When cancer cells were treated with the same low concentration of penfluridol, basal Src activation levels were mildly impaired, thus impacting their cell migration and wound healing efficiency. The potential of cancer-induced paracrine effect on endothelial cells was explored, although that did not seem to be a player for angiogenesis. Overall, our data demonstrates that low penfluridol levels, similar to the ones clinically used for anti-psychotic conditions, suppress angiogenic efficiency in the tumor microenvironment.

In deep learning, the optimization techniques are the most part dependent on gradient descent methods, such as SGD, Adam; adopt the leading place in the area of optimization methods. Conventional methodologies which depend on stochastic gradients are non-adaptive because the prescribed parameter worth's usage should be tuned for every application. However, the generalization performance of the stochastic optimizers is far superior to the adaptive methods, whereas Adam and its variants cannot maintain this without a fast convergence rate in deep neural networks. To improve this generalization performance, we need to diminish the oscillation of the weights, the general problem of the accuracy fall. To stable the generalization performance, we have introduced Mean-ADAM, a variance of Adam, extends the updated weights by an external weight to diminish the oscillation and overcome a superior accuracy rate than all other adaptive gradient methods till the conclusion of the training. Therefore, we substantially improve the generalization performance, permit it to contend with the existing algorithms on different image classification datasets such as Mnist, Cifar 10, Cifar 100, ImageNet, etc. We have attained 82% at 150 th epochs with Cifar 10 and 99.49% with Mnist where the Adam has indicated 76% and 99.43% individually. The graphical statistics and the quantitative results can indicate that the proposed method is more likely to perform in more diverse image datasets.