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Urinary kynurenine,tryptophan, and neopterin concentrations during physiological pregnancy
Abstract During pregnancy the alterations of kynurenine and tryptophan metabolism play an important role in local signalling and the prevention of fetal rejection. The aim was to investigate urinary levels of kynurenine and tryptophan during physiological pregnancy, and to determine their stability in urine during sample storage at different conditions. Urinary samples were obtained from 73 healthy pregnant women (median age 31 years), during the 1st, 2nd, and 3rd trimesters and from 42 healthy non-pregnant women (median age 30 years). Liquid chromatography methods using selective and sensitive mass spectrometry detection were used for analysis. Urinary neopterin, kynurenine, and tryptophan increased during the pregnancy and in comparison to the non-pregnant women. No correlation of the analytes with gestation age within each trimester and among the different analytes was observed. Kynurenine and tryptophan were stable in urine for 14 days at 4 °C, 6 months at − 22 °C, and 12 months at − 84 °C. Present results demonstrate differences in urinary concentrations of kynurenine, tryptophan, and neopterin between women with physiological pregnancy and healthy women. Simultaneous determination of kynurenine, tryptophan and neopterin may be explored in the disorders of pregnancy in future investigations.
Kharasch-Type Haloalkylation of Alkenes by Photoinduced Copper Catalysis
Investigating barriers to drones implementation in sustainable construction using PLS-SEM
Electrodeposition of Magnonic V(tetracyanoethylene)<sub>2</sub> Thin Films
Apoptotic effects of cold atmospheric pressure plasma on A549 and LL/2 lung carcinoma cell lines
Molecular Design for Optically Induced Magnetization: Targeting Excited State Orbital Degeneracy in Tungsten(V) Complexes
Association between street greenery and physical activity among Chinese older adults in Beijing, China
Sterically Controlled Cyclobutane-Dioxetane Ultrabright Afterglow Nanosystem for Cyclic Therapy of Choroidal Neovascularization in Mice
A blockchain based deep learning framework for a smart learning environment
Abstract In the contemporary digital age, education is no longer limited to traditional educational environments. Many educational institutions shifted to depend on the smart learning process but expressed concern about this solution due to its various challenges in securing the learning process and learners’ data. By virtue of the most recent technologies like blockchain and artificial intelligence, which played a significant role in solving many challenges that faced the educational sector and overcoming issues like fake certificates, manipulation, tracking learners’ activities, and predicting learners’ academic performance. The study proposed a smart framework based on blockchain and deep learning to enhance smart learning processes and provide solutions for challenges in the field. The framework is intended to store the learner’s data on the blockchain through the interplanetary file system and reap the benefits of securing the learner’s data and ensuring its integrity, as well as ensuring the confidentiality and authentication of the users through the wallets that are created on the Ethereum private blockchain platform. Then apply the deep learning model to this secured data to predict the learner’s performance. The smart contract functions also play a role in enabling the university to issue learners’ certificates that are stored on the blockchain to be available and verifiable by all the nodes in the network. Based on the experimental results, deep neural networks were used to model the encrypted data that was stored on the blockchain and predict the learner’s performance and achieved a high degree of accuracy (91.29%) and low loss (about 0.18) in comparison to other studies that depended on the centralized nature of the data. As well, the university blockchain’s functionality was tested, and it successfully returned all the functional requirements and showed its legitimacy.