Engineering Bioactive M2 Macrophage-Polarized Anti-Inflammatory,Antioxidant, and Antibacterial Scaffolds for Rapid Angiogenesis and Diabetic Wound Repair

Diabetic wound healing still faces great challenges due to the excessive inflammation, easy infection, and impaired angiogenesis in wound beds. The immunoregulation of macrophages polarization toward M2 phenotype that facilitates the transition from inflammation to proliferation phase has been proved to be an effective way to improve diabetic wound healing. Herein, an M2 phenotype-enabled anti-inflammatory, antioxidant, and antibacterial conductive hydrogel scaffolds (GDFE) for producing rapid angiogenesis and diabetic wound repair are reported. The GDFE scaffolds are fabricated facilely through the dynamic crosslinking between polypeptide and polydopamine and graphene oxide. The GDFE scaffolds possess thermosensitivity, self-healing behavior, injectability, broad-spectrum antibacterial activity, antioxidant and anti-inflammatory ability, and electronic conductivity. GDFE effectively activates the polarization of macrophages toward M2 phenotype and significantly promotes the proliferation of dermal fibroblasts, the migration, and in vitro angiogenesis of endothelial cells through paracrine mechanisms. The in vivo results from a full-thickness diabetic wound model demonstrate that GDFE can rapidly promote the diabetic wound repair and skin regeneration, through fast anti-inflammation and angiogenesis and M2 macrophage polarization. This study provides highly efficient strategy for treating diabetic wound repair through designing the M2 polarization-enabled anti-inflammatory, antioxidant, and antibacterial bioactive materials.     

How scientific research incorporates policy: an examination using the case of China’s science and technology evaluation system

Scientometrics - Governments typically formulate sets of policies to guide the direction of scientific research. And the possible effects of these policies on scientific research have been...     

Effects of four multi-compound freezing medium on the quality of red drum (Sciaenops ocellatus) during frozen storage

Taking air freezing (AF) as the reference, the effects of four types of multi-compound freezing medium for cryogenic liquid quick-freezing (immersion freezing, IF) on the freezing rate, quality and myofibrillar protein (MP) denaturation of red drum (Sciaenops ocellatus) fillets during frozen storage (−18°C) from days 0 to 90 were studied. Samples were gathered on days 0, 15, 30, 45, 60 and 90 for analysis. The results showed that IF groups (IF-1: 20% ethanol, 30% propylene glycol, 10% sodium chloride aqueous solution; IF-2: 20% ethanol, 20% propylene glycol, 10% glycerol, 10% sodium chloride aqueous solution; IF-3: 20% ethanol, 20% propylene glycol, 10% polyethylene glycol, 10% sodium chloride aqueous solution; IF-4: 20% ethanol, 20% propylene glycol, 5% glycerol, 5% polyethylene glycol, 10% sodium chloride aqueous solution) significantly shortened the time to cross the formation zone of maximum ice crystals while the freezing rate was 6.13 times higher than the AF group after adding 30% propylene glycol as the freezing medium. Furthermore, compared with the AF group, the IF groups significantly reduced losses in the water-holding capacity, the myofibrillar fragmentation index, microstructure damage, texture characteristics, drip loss and water migration (P < 0.05). In addition, the MP of IF groups had higher maximum transition temperatures (T_max1 and T_max2), total sulfhydryl content, Ca²⁺-ATPase activity and relative α-helix content compared with the AF group. In conclusion, IF could significantly increase the freezing rate of red drum fillets, and slow down quality deterioration and denaturation of MP during frozen storage for 90 days (−18°C). In particular, IF-2 (20% ethanol, 20% propylene glycol, 10% glycerol, 10% sodium chloride aqueous solution) was found to be more suitable for the immersion freezing of marine fish among the four multi-compound freezing medium.     

Molecular Characteristics of Amyloid Precursor Protein (APP) and Its Effects in Cancer

Amyloid precursor protein (APP) is a type 1 transmembrane glycoprotein, and its homologs amyloid precursor-like protein 1 (APLP1) and amyloid precursor-like protein 2 (APLP2) are highly conserved in mammals. APP and APLP are known to be intimately involved in the pathogenesis and progression of Alzheimer’s disease and to play important roles in neuronal homeostasis and development and neural transmission. APP and APLP are also expressed in non-neuronal tissues and are overexpressed in cancer cells. Furthermore, research indicates they are involved in several cancers. In this review, we examine the biological characteristics of APP-related family members and their roles in cancer.     

Synergy of Immunostimulatory Genetherapy with Immune Checkpoint Blockade Motivates Immune Response to Eliminate Cancer

Normalizing the tumor-induced immune deficiency in the immunosuppressive tumor microenvironment (TME) through increasing the efficient infiltration and activation of antitumoral immunity in TME is the core of promising immunotherapy. Herein, a Cyclo(Arg-Gly-Asp-d -Phe-Lys) (RGD) peptides-modified combinatorial immunotherapy system based on the self-assembly of the nanoparticles named RGD-DMA composed of RGD-PEG-PLA, methoxy poly(ethylene glycol)-poly(lactide) (MPEG-PLA) and 1,2-Dioleoyl-3-trimethylammonium-propane (DOTAP) is used to codeliver the immunostimulatory chemokine CCL19-encoding plasmid DNA (CCL19 pDNA) and immune checkpoint ligand PD-L1 inhibitor (BMS-1). The RGD-DMA/pCCL19-BMS-1 system not only exhibited significant inhibition of tumor progression but also induced locally high concentrations of immunostimulatory cytokines at tumor sites without causing an obviously systemic inflammatory response. The immunosuppressive TME is efficaciously reshaped by the coadministration of RGD-DMA/pCCL19 and BMS-1, as indicated by the activated T lymphocytes, increased intratumoral-infiltration of mature dendritic cells (DCs), and the repolarization of macrophages from pro-tumoral M2-phenotype toward tumoricidal M1-phenotype. The upregulated PD-L1 expression at tumor sites caused by the increased IFN-γ levels after immunostimulatory gene therapy further demonstrated the synergistic effects of BMS-1 in counteracting the inhibitory role of PD-L1 expression in antitumor immunity. Therefore, the combination of immunostimulating therapy and immune checkpoint inhibitor that synergistically target multiple immune regulatory pathways demonstrates significant potential as a novel immunotherapy approach.     

TiO2/MXene-Assisted LDI-MS for Urine Metabolic Profiling in Urinary Disease

Cancer remains an intractable medical problem. Rapid diagnosis and identification of cancer are critical to differentiate it from nonmalignant diseases. High-throughput biofluid metabolic analysis has potential for cancer diagnosis. Nevertheless, the present metabolite analysis method does not meet the demand for high-throughput screening of diseases. Herein, a high-throughput, cost-effective, and noninvasive urine metabolic profiling method based on TiO₂/MXene-assisted laser desorption/ionization mass spectrometry (LDI-MS) is presented for the efficient screening of bladder cancer (BC) and nonmalignant urinary disease. Combined with machine learning, TiO₂/MXene-assisted LDI-MS enables high diagnostic accuracy (96.8%) for the classification of patient groups (including 47 BC and 46 ureteral calculus (UC) patients) from healthy controls (113 cases). In addition, BC patients can also be identified from noncancerous UC individuals with an accuracy of 88.3% in the independent test cohort. Furthermore, metabolite variations between BC and UC individuals are investigated based on relative quantification, and related pathways are also discussed. These results suggest that this method, based on urine metabolic patterns, provides a potential tool for rapidly distinguishing urinary diseases and it may pave the way for precision medicine.     

基于分段加权的反向稀疏跟踪算法研究

为提高稀疏表示跟踪模型性能，提出一种分段加权的反向稀疏跟踪算法，将跟踪问题转化为在贝叶斯框架下寻找概率最高的候选对象问题，构造不同的分段权重函数来分别度量候选目标与正负模板的判别特征系数。通过池化来降低跟踪结果的不确定性干扰，选择正负模板加权系数差值最大的候选表示作为跟踪结果。实验表明，在光照变化、遮挡、快速运动、运动模糊情况下，所提出的算法可以确保跟踪结果的准确性和鲁棒性。     

基于视觉感知的智能扫地机器人的垃圾检测与分类

目的 为了提高扫地机器人的自主性和智能化程度，为扫地机器人配备视觉传感器，使其获得视觉感知能力，通过研究有效的垃圾检测分类模型与算法，实现对垃圾的定位与识别，引导扫地机器人对垃圾进行自动识别与按类处理，提高工作的目的性和效率，避免盲动和减少能耗。方法 选择检测速度较快的YOLOv2作为主网络模型，结合密集连接卷积网络，嵌入深层密集模块，对YOLOv2进行改进，提出一种YOLOv2-dense网络，该网络可以充分利用图像的高分辨率特征，实现图像浅层和深层特征的复用与融合。结果 测试结果表明，智能扫地机器人使用本文方法可以有效识别不同形态的常见垃圾类别，在真实场景中，测试识别准确率为84.98%，目标检测速度达到26帧/s。结论 实验结果表明，本文构建的YOLOv2-dense网络模型具有实时检测的速度，并且在处理具有不同背景、光照、视角与分辨率的图片时，表现出较强的适应和识别性能。在机器人移动过程中，可以保证以较高的准确率识别出垃圾的种类，整体性能优于原YOLOv2模型。     

A solution to measure traveler’s transfer tolerance for walking mode and dockless bike-sharing mode

The Journal of Supercomputing - Dockless bike-sharing is more and more popular all over the world, which enables passengers to choose destinations more flexibly, so that the transfer radius between...     

高效液相色谱法测定维生素K胶囊中维生素K2含量不确定度评估

目的评估高效液相色谱法测定维生素K胶囊中的维生素K_2含量的不确定度。方法采用高效液相色谱法测定维生素K胶囊中的维生素K_2含量,建立数学模型,对测定过程的主要不确定度分量进行计算,得出合成不确定度和扩展不确定度。结果维生素K胶囊中维生素K_2的含量为181.2μg/g时,在置信区间95%下,其扩展不确定度为7.5μg/g(k=2)。结论不确定度的主要来源为标准曲线配制的过程,该评估模型为维生素K胶囊中维生素K_2的含量测定的准确性提供了参考依据。