Fight Aging! Newsletter, March 2nd 2020
In conclusion, the recently demonstrated protective effects of NMN treatment on neurovascular function can be attributed to multifaceted sirtuin-mediated anti-aging changes in the neurovascular transcriptome. Our present findings taken together with the results of recent studies using mitochondria-targeted interventions suggest that mitochondrial rejuvenation is a critical mechanism to restore neurovascular health and improve cerebral blood flow in aging. Wnt/β-Catenin Signaling as a Point of Intervention to Spur Greater Neural Regeneration https://www.fightaging.org/archives/2020/02/wnt-%ce%b2-catenin-signaling-as-a-point-of-intervention-to-spur-greater-neural-regeneration/ Wnt signaling is a complicated but well studied portion of the regulatory systems governing regeneration. Numerous groups are engaged in the commercial development of regenerative therapies that are based on manipulation of this part of mammalian biochemistry. Of the more prominent ventures, Samumed has reached phase 3 trials with treatments of this type. This open access paper walks through the evidence for Wnt signaling to be a useful point of intervention for researchers aiming to increase neurogenesis and regenerative capacity in the aging brain. This will be developed as a means of treating neurodegenerative conditions, but success would lead to treatments beneficial for all older individuals. A common hallmark of age-dependent neurodegenerative diseases is an impairm...
Publication date: Available online 30 May 2020Source: Life SciencesAuthor(s): Qi Fu, Zhenhai Yu
Publication date: Available online 30 May 2020Source: Life SciencesAuthor(s): Lakshmikripa Jagannathan, Emily Socks, Priya Balasubramanian, Robert McGowan, Thomas M. Herdt, Reza Kianian, Sheba M.J. MohanKumar, P.S. MohanKumar
Publication date: Available online 29 May 2020Source: Life SciencesAuthor(s): Nagesh Kishan Panchal, E.P. Sabina
In this study, five machine learning algorithms and twelve types of molecular fingerprints were employed to generate QSAR discriminant models for mitochondrial toxicity. A threshold moving method was adopted to resolve the imbalance issue in the training data. Consensus of the models by an averaging probability strategy improved prediction performance. The best model has correct classification rates of 81.8% and 88.3% in ten-fold cross validation and external validation, respectively. Substructures such as phenol, carboxylic acid, nitro and arylchloride were found informative through analysis of information gain and freque...
Abstract The absorption and scattering of aerosols are critical factors that influence in global climate and visibility degradation. From January 2013 to December 2015, aerosol scattering coefficients, PM2.5, and meteorological parameters were continuously measured at a monitoring site in Shanghai, China. The annual means of scattering coefficients were 312.3, 232.1, and 261.9 Mm-1 for the years 2013, 2014, and 2015, respectively. The corresponding values for PM2.5 were 61.6, 51.6, and 52.9 μg/m3. Compared with the average scattering coefficient of the year 2013, those of 2014 and 2015decreased by 26%...
Publication date: Available online 29 May 2020Source: Colloids and Surfaces B: BiointerfacesAuthor(s): Changyong Guo, Qirong Wang, Xingjie Zhang, Fengkun Lu, Miao Sun, Peiyu Zeng, Linhong Sun, Lan She, Bingkai Wang, Yunchang Zhang, Chen Wang, Zhiqiang Ma, Feng Yang
Publication date: Available online 29 May 2020Source: Chinese Journal of Chemical EngineeringAuthor(s): Weijie Ding, Min Chen, Ming Zhou, Zhaoxiang Zhong, Zhaoliang Cui, Weihong Xing
Publication date: Available online 29 May 2020Source: Chinese Journal of Chemical EngineeringAuthor(s): Shujuan Xiao, Xiaowen Huo, Shuxin Fan, Kui Zhao, Shouwu Yu, Xiaoyao Tan
Publication date: Available online 29 May 2020Source: Chinese Chemical LettersAuthor(s): Kaijie Wang, Dazhou Xi, Chuantao Liu, Yahui Chen, Hao Gu, Long Jiang, Xiaoqiang Chen, Fang Wang
Publication date: November 2020Source: Journal of Environmental Sciences, Volume 97Author(s): Tianpeng Hu, Yao Mao, Weijie Liu, Cheng Cheng, Mingming Shi, Zhanle Chen, Qian Tian, Jiaquan Zhang, Shihua Qi, Xinli Xing
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