The endothelial cells play a crucial role in the progression of angiogenesis, which causes cell re-modulation, proliferation, adhesion, migration, invasion and survival. Angiogenic factors like cytokines, cell adhesion molecules, growth factors, vasoactive peptides, proteolytic enzymes (metalloproteinases) and plasminogen activators bind to their receptors on endothelial cells and activate the signal transduction pathways like epidermal growth factor receptor (EGFR phosphatidylinositol 3-kinase and (PI3K)/AKT/mammalian target of rapamycin (mTOR) which initiate the process of angiogenesis.Cytokines that stimulate angiogenesis include direct and indirect proangiogenic markers. The direct proangiogenic group of markers consists of vascular endothelial growth factor (VEGF), basic fibroblast growth factor (FGF-2) and hepatocyte growth factor (HGF) whereas the indirect proangiogenic markers include transforming growth factor-beta (TGF-β), interleukin 6 (IL-6), interleukin 8 (IL-8) and platelet-derived growth factor (PDGF).VEGF and FGF-2 are the strongest activators of angiogenesis which stimulate migration and proliferation of endothelial cells in existing vessels to generate and stabilize new blood vessels. VEGF is released in hypoxic conditions as an effect of the hypoxia-inducible factor (HIF-1α) and causes re-modulation and inflammation of bronchi cell. Cell re-modulation and inflammation leads to the development of various lung disorders like pulmonary hypertension, chronic obstructive pulmonary disease, asthma, fibrosis and lung cancer.This indicates that there is a firm link between overexpression of VEGF and FGF-2 with lung disorders. Various natural and synthetic drugs are available for reducing the overexpression of VEGF and FGF-2 which can be helpful in treating lung disorders. Researchers are still searching for new angiogenic inhibitors which can be helpful in the treatment of lung disorders.The present review emphasizes on molecular mechanisms and new drug discovery focused on VEGF and FGF-2 inhibitors and their role as anti-angiogenetic agents in lung disorders.

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Autonomous vehicles rely on robust real-time detection and future motion prediction of traffic participants to safely navigate urban environments. We present a novel end-toend approach that uses raw time-series LiDAR data to jointly solve both detection and prediction. We use the range view representation of LiDAR instead of voxelization since it does not discard information and is more efficient due to its compactness. However, for time-series fusion the data needs to be projected to a common viewpoint, and often this viewpoint is different from where it was captured leading to distortions. These distortions have an adverse impact on performance. Thus, we propose a novel architecture which reduces the impact of distortions by sequentially projecting each sweep into the viewpoint of the next sweep in time. We demonstrate that our sequential fusion approach is superior to methods that directly project all the data into the most recent viewpoint. Furthermore, we compare our approach to existing state-of-the art methods on multiple autonomous driving datasets and show competitive results.