The design and development of a water-soluble heterocyclic ligand are believed to be an alternative way for improving the separation efficiency of actinides from lanthanides. Herein, we designed and synthesized a novel hydrophilic multidentate ligand: disulfonated N,N′-diphenyl-2,9-diamide-1,10phenanthroline (DS-Ph-DAPhen) with soft and hard donor atoms, as a masking agent in aqueous solutions for Am(III) separation. The combination of N,N,N′,N′-tetraoctyldiglycolamide in kerosene and DS-Ph-DAPhen in aqueous phases could separate Am(III) from Eu(III) across a range of nitric acid concentrations with very high selectivity. The coordination behaviors of Eu(III) with DS-Ph-DAPhen in aqueous solutions were studied by UV−vis titration, electrospray ionization mass spectrometry, and Fourier transform infrared spectra. The results indicated that Eu(III) ions could form both 1:1 and 1:2 complexes with the DS-Ph-DAPhen ligand in aqueous solution. Density functional theory calculation suggests that there are more covalent characters for Am−N bonds than that for Eu−N bonds in the complexes, which supports the better selectivity of the DS-Ph-DAPhen ligand toward Am(III) over Eu(III). This work demonstrates a feasible alternative approach to separating trivalent actinides from lanthanides with high selectivity.

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Diabetic nephropathy (DN) is rapidly becoming the leading cause of end-stage renal disease worldwide and a major cause of morbidity and mortality in patients of diabetes. The main pathological change of DN is renal fibrosis. Paeonol (PA), a single phenolic compound extracted from the root bark of Cortex Moutan, has been demonstrated to have many potential pharmacological activities. However, the effects of PA on DN have not been fully elucidated. In this study, high glucose (HG)-treated glomerular mesangial cells (GMCs) and streptozotocin (STZ)-induced diabetic mice were analyzed in exploring the potential mechanisms of PA on DN. Results in vitro showed that: (1) PA inhibited HG-induced fibronectin (FN) and ICAM-1 overexpressions; (2) PA exerted renoprotective effect through activating the Nrf2/ARE pathway; (3) Sirt1 mediated the effects of PA on the activation of Nrf2/ARE pathway. What is more, in accordance with the in vitro results, significant elevated levels of Sirt1, Nrf2 and downstream proteins related to Nrf2 were observed in the kidneys of PA treatment group compared with model group. Taken together, our study shows that PA delays the progression of diabetic renal fibrosis, and the underlying mechanism is probably associated with regulating the Nrf2 pathway. The effect of PA on Nrf2 is at least partially dependent on Sirt1 activation.

Retinopathy of prematurity (ROP) is the leading cause of blindness in preterm infants. In this study, we investigated the cytokine levels in cord blood of normal preterm neonates and preterm infants developed ROP. Serum levels of 10 cytokines in umbilical cord blood were measured by multiplex protein arrays from 62 healthy preterm neonates and 30 preterm neonate cases who developed ROP at later stage. Results showed that serum levels of cytokines including interleukin 7 (IL-7), monocyte chemotactic protein-1 (MCP-1), macrophage inflammatory protein 1 alpha (MIP-1α), and macrophage inflammatory protein 1 beta (MIP-1β) were significantly increased in cases who developed ROP than in healthy preterm neonates (3.5-fold, 3.2-fold, 3.4-fold, and 2.1-fold, respectively), whereas levels of these four cytokines did not reveal any significant differences between healthy preterm infants and normal infants. When comparing the expression of cytokines in ROP patients with different clinical parameters, ROP cases whose gestational age at delivery earlier than 29.0 weeks demonstrated increased levels of MCP-1 and MIP-1β than those later than 29.0 weeks (p < 0.05). Also, ROP cases with birth weight less than 1.28 kg revealed significantly higher level of MIP-1β than those who were heavier than 1.28 kg (p < 0.05). These data indicated that levels of IL-7, MCP-1, MIP-1α, and MIP-1β were associated with increased risk of ROP, in which MIP-1β may be further correlated with the severity of ROP.

Face images contain many important biological characteristics. The research directions of face images mainly include face age estimation, gender judgment, and facial expression recognition. Taking face age estimation as an example, the estimation of face age images through algorithms can be widely used in the fields of biometrics, intelligent monitoring, human-computer interaction, and personalized services. With the rapid development of computer technology, the processing speed of electronic devices has greatly increased, and the storage capacity has been greatly increased, allowing deep learning to dominate the field of artificial intelligence. Traditional age estimation methods first design features manually, then extract features, and perform age estimation. Convolutional neural networks (CNN) in deep learning have incomparable advantages in processing image features. Practice has proven that the accuracy of using convolutional neural networks to estimate the age of face images is far superior to traditional methods. However, as neural networks are designed to be deeper, and networks are becoming larger and more complex, this makes it difficult to deploy models on mobile terminals. Based on a lightweight convolutional neural network, an improved ShuffleNetV2 network based on the mixed attention mechanism (MA-SFV2: Mixed Attention-ShuffleNetV2) is proposed in this paper by transforming the output layer, merging classification and regression age estimation methods, and highlighting important features by preprocessing images and data augmentation methods. The influence of noise vectors such as the environmental information unrelated to faces in the image is reduced, so that the final age estimation accuracy can be comparable to the state-of-the-art.