A comprehensive curated resource for follicle stimulating hormone signaling

<p>Abstract</p> <p>Background</p> <p>Follicle stimulating hormone (FSH) is an important hormone responsible for growth, maturation and function of the human reproductive system. FSH regulates the synthesis of steroid hormones such as estrogen and progesterone, proliferation and maturation of follicles in the ovary and spermatogenesis in the testes. FSH is a glycoprotein heterodimer that binds and acts through the FSH receptor, a G-protein coupled receptor. Although online pathway repositories provide information about G-protein coupled receptor mediated signal transduction, the signaling events initiated specifically by FSH are not cataloged in any public database in a detailed fashion.</p> <p>Findings</p> <p>We performed comprehensive curation of the published literature to identify the components of FSH signaling pathway and the molecular interactions that occur upon FSH receptor activation. Our effort yielded 64 reactions comprising 35 enzyme-substrate reactions, 11 molecular association events, 11 activation events and 7 protein translocation events that occur in response to FSH receptor activation. We also cataloged 265 genes, which were differentially expressed upon FSH stimulation in normal human reproductive tissues.</p> <p>Conclusions</p> <p>We anticipate that the information provided in this resource will provide better insights into the physiological role of FSH in reproductive biology, its signaling mediators and aid in further research in this area. The curated FSH pathway data is freely available through NetPath (<url>http://www.netpath.org</url>), a pathway resource developed previously by our group.</p

Vanin-1 Pantetheinase Drives Smooth Muscle Cell Activation in Post-Arterial Injury Neointimal Hyperplasia

The pantetheinase vanin-1 generates cysteamine, which inhibits reduced glutathione (GSH) synthesis. Vanin-1 promotes inflammation and tissue injury partly by inducing oxidative stress, and partly by peroxisome proliferator-activated receptor gamma (PPARγ) expression. Vascular smooth muscle cells (SMCs) contribute to neointimal hyperplasia in response to injury, by multiple mechanisms including modulation of oxidative stress and PPARγ. Therefore, we tested the hypothesis that vanin-1 drives SMC activation and neointimal hyperplasia. We studied reactive oxygen species (ROS) generation and functional responses to platelet-derived growth factor (PDGF) and the pro-oxidant diamide in cultured mouse aortic SMCs, and also assessed neointima formation after carotid artery ligation in vanin-1 deficiency. Vnn1−/− SMCs demonstrated decreased oxidative stress, proliferation, migration, and matrix metalloproteinase 9 (MMP-9) activity in response to PDGF and/or diamide, with the effects on proliferation linked, in these studies, to both increased GSH levels and PPARγ expression. Vnn1−/− mice displayed markedly decreased neointima formation in response to carotid artery ligation, including decreased intima:media ratio and cross-sectional area of the neointima. We conclude that vanin-1, via dual modulation of GSH and PPARγ, critically regulates the activation of cultured SMCs and development of neointimal hyperplasia in response to carotid artery ligation. Vanin-1 is a novel potential therapeutic target for neointimal hyperplasia following revascularization

Evolution of microstructure and crystallographic texture during dissimilar friction stir welding of duplex stainless steel to low carbon-manganese structural steel

Electron backscattered diffraction (EBSD) was used to analyze the evolution of microstructure and crystallographic texture during friction stir welding of dissimilar type 2205 duplex stainless steel (DSS) to type S275 low carbon-manganese structural steel. The results of microstructural analyses show that the temperature in the center of stirred zone reached temperatures between Ac 1 and Ac 3 during welding, resulting in a minor ferrite-to-austenite phase transformation in the S275 steel, and no changes in the fractions of ferrite and austenite in the DSS. Temperatures in the thermomechanically affected and shoulder-affected zones of both materials, in particular toward the root of the weld, did not exceed the Ac 1 of S275 steel. The shear generated by the friction between the material and the rotating probe occurred in austenitic/ferritic phase field of the S275 and DSS. In the former, the transformed austenite regions of the microstructure were transformed to acicular ferrite, on cooling, while the dual-phase austenitic/ferritic structure of the latter was retained. Studying the development of crystallographic textures with regard to shear flow lines generated by the probe tool showed the dominance of simple shear components across the whole weld in both materials. The ferrite texture in S275 steel was dominated by D 1, D 2, E, E¯ , and F, where the fraction of acicular ferrite formed on cooling showed a negligible deviation from the texture for the ideal shear texture components of bcc metals. The ferrite texture in DSS was dominated by D 1, D 2, I, I¯ , and F, and that of austenite was dominated by the A, A¯ , B, and B¯ of the ideal shear texture components for bcc and fcc metals, respectively. While D 1, D 2, and F components of the ideal shear texture are common between the ferrite in S275 steel and that of dual-phase DSS, the preferential partitioning of strain into the ferrite phase of DSS led to the development of I and I¯ components in DSS, as opposed to E and E¯ in the S275 steel. The formations of fine and ultrafine equiaxed grains were observed in different regions of both materials that are believed to be due to strain-induced continuous dynamic recrystallization (CDRX) in ferrite of both DSS and S275 steel, and discontinuous dynamic recrystallization (DDRX) in austenite phase of DSS

100 Years of Growth and Success Story of Nestle India - A Fast Moving Consumer Goods (FMCG) Industry

Background/Purpose: As a Swiss multinational company, Nestle has a subsidiary in India called Nestle India Limited (NIL). In Haryana, the company is headquartered in Gurugram (Gurgaon). Food, beverages, chocolate, and confectioneries are among the company's offerings. Because of Nestle’s focus on its core strengths and its alignment with opportunities available, the company's product portfolio and global presence continue to expand. Nestle celebrated its 100th anniversary in India in 2012 with a new commercial featuring the company's products. Objective: In this paper, we analyze Nestle India's influence on customers during the COVID-19 pandemic, and to know the company's CSR activities. This paper also analyses the FMCG industry evolution in the country. Design/Methodology/Approach This study was undertaken using secondary sources, such as journals and conference articles, annual reports, websites of Nestle Company, the internet, scholarly&nbsp;articles, and&nbsp;social media reviews. A&nbsp;SWOT analysis assessment was made on the company. It is an explorative research case study that aims at identifying the growth of Nestle India Limited-A FMCG industry in the Indian economy. Findings/Results: During the COVID-19 pandemic, the company gained growth. Net income for Nestle India in 2020 is more than 20 billion Indian rupees compared to 19 billion rupees in the year before. Conclusion: Nestle India is a major player in the Indian FMCG market. One of India's top-valued&nbsp;companies, as well as one of the country's top job creators. As part of its mission, Nestle India strives to provide various high-quality, safe-food items at affordable prices. The firm is constantly striving to better understand modern Indian lives and anticipate consumer needs, it is also constantly working to improve its product offerings in terms of convenience, taste, nutrition, and wellness. Paper Type: Company Analysis as a Research Case Stud

A Case Study on Coronary Heart Disease using Machine Learning Techniques

Background/Purpose: We have seen an increase in coronary heart disease and heart attack risk in recent years. This is a case study on Sri Jayadeva Institute of Cardiovascular Sciences and Research, Bengaluru to get a better understanding of the heart related ailments and their related symptoms. The hospital specializes in cardiology, cardiothoracic surgery and paediatric cardiology. Based on the symptoms various ailments are diagnosed and treated with different treatments like angioplasty, placement of stent, lifestyle changes and medicines. As part of the research, various health parameters will be collected and analyzed for diagnosing heart related ailments using Machine Learning methods. Determining the appropriate Machine Learning technique to achieve maximum accuracy is the key to achieve a better treatment and prevention of mortality. Design/Methodology/Approach: This study was undertaken using secondary sources, such as website of Sri Jayadeva Institute of Cardiovascular Science and Research, journals, conference articles, the internet and scholarly articles. The SWOT framework is used to analyse, and present, the information acquired from web articles, scholarly papers and other sources. Findings/Results: Heart ailments can be predicted using a few key parameters which can help in avoiding mortality. For this purpose machine learning algorthims, Neural Networks, Particle Swarm algorithm and many more can be applied on those medicial parameters. Originality/Value: This paper reports an exhaustive and comprehensive overview of Coronary Heart Diseases and the treatment provided by Jayadeva Cardiology Hospital on different data collected. Paper Type: Case study-based Research Analysi

Prediction of Coronary Artery Disease using Artificial Intelligence – A Systematic Literature Review

Purpose: Coronary heart disease and the risk of having a heart attack have both risen in recent years. Angioplasty, lifestyle changes, stent implantation, and medications are only some of the methods used to diagnose and treat various diseases. In this study, we will gather and analyze a variety of health indicators in order to identify heart-related illnesses via Machine Learning and Deep Learning prediction models. The best way to improve treatment and mortality prevention is to identify the relevant critical parameters and use Machine Learning or Deep Learning algorithms to achieve optimum accuracy. Design/Methodology/Approach: Secondary sources were used for this investigation. These included periodicals, papers presented at conferences, online sources, and scholarly books and articles. In order to analyze and present the data gathered from academic journals, websites, and other sources, the SWOT analysis is being used. Findings/Results: Predicting heart problems and their severity with a handful of crucial characteristics can save lives. Machine Learning algorithms such as Linear Regression, Deep Learning algorithms such as Neural Networks, and many others can all be applied to those medical parameters for this goal. Originality/Value: This literature study utilizes secondary data collected from diverse sources. Understanding the many types of coronary artery disease and evaluating the most recent advances in predicting the same using Machine Learning approaches will be facilitated by the learned knowledge. This knowledge will aid in the development of a new model or the enhancement of an existing model for predicting coronary artery disease in an individual. Included are tables detailing the forms of coronary artery disease, a variety of recently published research publications on the topic, and standard datasets. Paper Type: Literature Revie

Traffic Flow Prediction using Machine Learning Techniques - A Systematic Literature Review

Purpose: Traffic control in large cities is extremely tough. To alleviate costs associated with traffic congestion, some nations of the world have implemented Intelligent Transportation Systems (ITS). This paper reviews the application of artificial neural network (ANN) and machine learning (ML) techniques and also their implementation issues in TFP. Techniques other than ML and ANN have also been discussed. Methodology: The survey of literature on TFP (TFP) and ITS was conducted using several secondary sources of information such as conference proceedings Journals, Books, and Research Reports published in various publications, and then the kinds of literature that are reported as promising have been included. The collected information is then reviewed to discover possible key areas of concern in the TFP and ITS. Findings/Results: Traffic management in cities is important for smooth traffic flow. TFP and ITS are drawing much attention from researchers these days. Application of ML, ANN, and other techniques are being tried to alleviate the traffic flow problem in cities. TFP using ITS employing ML techniques to overcome the problem of traffic congestion looks promising. Originality: This review of literature is conducted using secondary data gathered from various sources. The information acquired will be useful to expand on existing theories and frameworks or to develop a new technique or modify to improve the accuracy of TFP. Tables containing categories of prediction, ML Pipelining, open-source ML tools available, standard datasets available have been included. Paper Type: Literature Review