Visulization of peroxynitrite variation for accurate diagnosis and assessing treatment response of hepatic fibrosis using a Golgi-targetable ratiometric fluorescent probe.

Hepatic fibrosis (HF) is caused by persistent inflammation, which is closely associated with hepatic oxidative stress. Peroxynitrite (ONOO-) is significantly elevated in HF, which would be regarded as a potential biomarker for the diagnosis of HF. Research has shown that ONOO- in the Golgi apparatus can be overproduced in HF, and it can induce hepatocyte injury by triggering Golgi oxidative stress. Meanwhile, the ONOO- inhibitors could effectively relieve HF by inhibiting Golgi ONOO-, but as yet, no Golgi-targetable fluorescent probe available for diagnosis and assessing treatment response of HF through sensing Golgi ONOO-. To this end, we reported a ratiometric fluorescent probe, Golgi-PER, for diagnosis and assessing treatment response of HF through monitoring the Golgi ONOO-. Golgi-PER displayed satisfactory sensitivity, low detection limit, and exceptional selectivity to ONOO-. Combined with excellent biocompatibility and good Golgi-targeting ability, Golgi-PER was further used for ratiometric monitoring the Golgi ONOO- fluctuations and screening of ONOO- inhibitors from polyphenols in living cells. Meanwhile, using Golgi-PER as a probe, the overexpression of Golgi ONOO- in HF and the treatment response of HF to the screened rosmarinic acid were precisely visualized for the first time. Furthermore, the screened RosA has a remarkable therapeutic effect on HF, which may be a new strategy for HF treatment. These results demonstrated the practicability of Golgi-PER for monitoring the occurrence, development, and personalized treatment response of HF.

Coactivation of Hydrogen Peroxide Using Pyrogenic Carbon and Magnetite for Sustainable Oxidation of Organic Pollutants.

Pyrogenic carbon and magnetite (Fe3O4) were mixed together for the activation of hydrogen peroxide (H2O2), aiming to enhance the oxidation of refractory pollutants in a sustainable way. The experimental results indicated that the straw-derived carbon obtained by pyrolysis at 500-800 °C was efficient on coactivation of H2O2, and the most efficient one was that prepared at 700 °C (C700) featured with abundant defects. Specifically, the reaction rate constant (kobs) for removal of an antibiotic ciprofloxacin in the coactivation system (C700/Fe3O4/H2O2) is 12.5 times that in the magnetite-catalyzed system (Fe3O4/H2O2). The faster pollutant oxidation is attributed to the sustainable production of •OH in the coactivation process, in which the carbon facilitated decomposition of H2O2 and regeneration of Fe(II). Besides the enhanced H2O2 utilization in the coactivation process, the leaching of iron was controlled within the concentration limit in drinking water (0.3 mg·L-1) set by the World Health Organization.

A survey of deep learning applications in cryptocurrency.

This study aims to comprehensively review a recently emerging multidisciplinary area related to the application of deep learning methods in cryptocurrency research. We first review popular deep learning models employed in multiple financial application scenarios, including convolutional neural networks, recurrent neural networks, deep belief networks, and deep reinforcement learning. We also give an overview of cryptocurrencies by outlining the cryptocurrency history and discussing primary representative currencies. Based on the reviewed deep learning methods and cryptocurrencies, we conduct a literature review on deep learning methods in cryptocurrency research across various modeling tasks, including price prediction, portfolio construction, bubble analysis, abnormal trading, trading regulations and initial coin offering in cryptocurrency. Moreover, we discuss and evaluate the reviewed studies from perspectives of modeling approaches, empirical data, experiment results and specific innovations. Finally, we conclude this literature review by informing future research directions and foci for deep learning in cryptocurrency.