双对苯醌的抗氧化活性分析及其固体发酵条件优化

为探索生物活性未知的双对苯醌（2,7-dihydroxy-3,6,9-trimethyl-9H-xanthene-1,4,5,8-tetraone，DTXT）的抗氧化活性，并提高其发酵产量，考察DTXT的还原力以及对超氧阴离子自由基、羟自由基、1,1-二苯基-2-三硝基苯肼（1,1-diphenyl-2-picrylhydrazyl，DPPH）自由基的清除效果，在单因素试验基础上，采用响应面法优化了DTXT产生菌瓶生顶孢霉（Acremonium cavaraeanum）CA022菌株的固体发酵培养基。结果表明：在200 μg/mL质量浓度下，DTXT的还原力与芦丁差异不显著，高于VE和2,6-二叔丁基-4甲基苯酚，对超氧阴离子自由基清除率达到67.00%，对羟自由基清除率达到78.83%，对DPPH自由基清除率达到76.53%。通过响应面试验，得到最佳培养基配方为葡萄糖0.773%、硝酸钠0.185%、H3BO3 0.032%、VB1 100 μg/100 g，在此条件下实际获得的DTXT产量为4 150.8 mg/kg，是优化前产量的（2 864.83 mg/kg）1.45 倍。     

Neoagarooligosaccharide Protects against Hepatic Fibrosis via Inhibition of TGF-β/Smad Signaling Pathway

Hepatic fibrosis occurs when liver tissue becomes scarred from repetitive liver injury and inflammatory responses; it can progress to cirrhosis and eventually to hepatocellular carcinoma. Previously, we reported that neoagarooligosaccharides (NAOs), produced by the hydrolysis of agar by β-agarases, have hepatoprotective effects against acetaminophen overdose-induced acute liver injury. However, the effect of NAOs on chronic liver injury, including hepatic fibrosis, has not yet been elucidated. Therefore, we examined whether NAOs protect against fibrogenesis in vitro and in vivo. NAOs ameliorated PAI-1, α-SMA, CTGF and fibronectin protein expression and decreased mRNA levels of fibrogenic genes in TGF-β-treated LX-2 cells. Furthermore, downstream of TGF-β, the Smad signaling pathway was inhibited by NAOs in LX-2 cells. Treatment with NAOs diminished the severity of hepatic injury, as evidenced by reduction in serum alanine aminotransferase and aspartate aminotransferase levels, in carbon tetrachloride (CCl₄)-induced liver fibrosis mouse models. Moreover, NAOs markedly blocked histopathological changes and collagen accumulation, as shown by H&E and Sirius red staining, respectively. Finally, NAOs antagonized the CCl₄-induced upregulation of the protein and mRNA levels of fibrogenic genes in the liver. In conclusion, our findings suggest that NAOs may be a promising candidate for the prevention and treatment of chronic liver injury via inhibition of the TGF-β/Smad signaling pathway.     

低压静电场辅助冷冻对竹笋品质的影响

为研究低压静电场辅助冷冻对竹笋冻结特性的影响，以冻结曲线、硬度、水分损失率、水分迁移、冰晶形态和组织微观结构为指标，探究低压静电场辅助冷冻（－35 ℃）和普通冷冻（－35 ℃）条件下竹笋品质的变化规律。结果表明：低压静电场辅助冷冻提高了冻结效率，改变了冰晶形态及分布，减轻了组织微观结构破损程度，改善了解冻汁液流失情况。与静电板间距10、20、30、40 cm处的冷冻竹笋解冻后水分损失率分别为14.16%、12.58%、9.73%、10.44%，显著低于对照组（21.01%）（P＜0.05），硬度分别为461.19、507.48、496.65 g和455.31 g，显著高于对照组（350.70 g）（P＜0.05）。低场核磁共振分析结果表明，在低压静电场辅助冷冻下竹笋解冻后汁液流失减少，扫描电子显微镜观察结果显示，竹笋纤维排列整齐，组织微观结构保持较好。低压静电场辅助冷冻可有效改善竹笋品质，可为利用低压静电场进行果蔬的冷冻贮藏和冷链运输提供参考。     

纳米药物非临床药代动力学的研究策略及关注要点

随着纳米技术的迅速发展，纳米药物的研发已成为目前药物创新的发展方向之一。纳米药物具有基于纳米结构的尺度效应，其药代动力学特征与普通药物相比存在明显差异，其药代动力学研究与普通药物相比也有其特殊性。本文着重探讨纳米药物的非临床药代动力学的研究策略及关注要点，包括受试物、体内/外试验、生物样本分析、数据评价分析等，期望为研发者提供参考。     

Data fusion and transfer learning empowered granular trust evaluation for Internet of Things

In the Internet of Things (IoT), a huge amount of valuable data is generated by various IoT applications. As the IoT technologies become more complex, the attack methods are more diversified and can cause serious damages. Thus, establishing a secure IoT network based on user trust evaluation to defend against security threats and ensure the reliability of data source of collected data have become urgent issues, in this paper, a Data Fusion and transfer learning empowered granular Trust Evaluation mechanism (DFTE) is proposed to address the above challenges. Specifically, to meet the granularity demands of trust evaluation, time–space empowered fine/coarse grained trust evaluation models are built utilizing deep transfer learning algorithms based on data fusion. Moreover, to prevent privacy leakage and task sabotage, a dynamic reward and punishment mechanism is developed to encourage honest users by dynamically adjusting the scale of reward or punishment and accurately evaluating users’ trusts. The extensive experiments show that: (i) the proposed DFTE achieves high accuracy of trust evaluation under different granular demands through efficient data fusion; (ii) DFTE performs excellently in participation rate and data reliability.     

Combinations of Piperine with Hydroxypropyl-β-Cyclodextrin as a Multifunctional System

Piperine is an alkaloid that has extensive pharmacological activity and impacts other active substances bioavailability due to inhibition of CYP450 enzymes, stimulation of amino acid transporters and P-glycoprotein inhibition. Low solubility and the associated low bioavailability of piperine limit its potential. The combination of piperine with 2-hydroxypropyl-β-cyclodextrin (HP-β-CD) causes a significant increase in its solubility and, consequently, an increase in permeability through gastrointestinal tract membranes and the blood–brain barrier. X-ray powder diffraction (XRPD), differential scanning calorimetry (DSC), Fourier-transform infrared spectroscopy (FT-IR), nuclear magnetic resonance (NMR) were used to characterize interactions between piperine and HP-β-CD. The observed physicochemical changes should be combined with the process of piperine and CD system formation. Importantly, with an increase in solubility and permeability of piperine as a result of interaction with CD, it was proven to maintain its biological activity concerning the antioxidant potential (2,2-diphenyl-1-picryl-hydrazyl-hydrate assay), inhibition of enzymes essential for the inflammatory process and for neurodegenerative changes (hyaluronidase, acetylcholinesterase, butyrylcholinesterase).     

A data-driven digital-twin prognostics method for proton exchange membrane fuel cell remaining useful life prediction

Prognostics and health management of proton exchange membrane fuel cell (PEMFC) systems have driven increasing research attention in recent years as the durability of PEMFC stack remains as a technical barrier for its large-scale commercialization. To monitor the health state during PEMFC operation, digital twin (DT), as a smart manufacturing technique, is applied in this paper to establish an ensemble remaining useful life prediction system. A data-driven DT is constructed to integrate the physical knowledge of the system and a deep transfer learning model based on stacked denoising autoencoder is used to update the DT with online measurement. A case study with experimental PEMFC degradation data is presented where the proposed data-driven DT prognostics method has applied and reached a high prediction accuracy. Furthermore, the predicted results are proved to be less affected even with limited measurement data.