Mechanisms of repigmentation induced by photobiomodulation therapy in vitiligo

Photobiomodulation (PBM) therapy is based on the exposure of biological tissues to low‐level laser light (coherent light) or light‐emitting diodes (LEDs; noncoherent light), leading to the modulation of cellular functions, such as proliferation and migration, which result in tissue regeneration. PBM therapy has important clinical applications in regenerative medicine. Vitiligo is an acquired depigmentary disorder resulting from disappearance of functional melanocytes in the involved skin. Vitiligo repigmentation depends on available melanocytes derived from (a) melanocyte stem cells located in the bulge area of hair follicles and (b) the epidermis at the lesional borders, which contains a pool of functional melanocytes. Since follicular melanoblasts (MBs) are derived from the melanocyte stem cells residing at the bulge area of hair follicle, the process of vitiligo repigmentation presents a research model for studying the regenerative effect of PBM therapy. Previous reports have shown favourable response for treatment of vitiligo with a low‐energy helium‐neon (He‐Ne) laser. This review focuses on the molecular events that took place during the repigmentation process of vitiligo triggered by He‐Ne laser (632.8 nm, red light). Monochromatic radiation in the visible and infrared A (IRA) range sustains matrix metalloproteinase (MMP), improves mitochondrial function, and increases adenosine triphosphate (ATP) synthesis and O₂ consumption, which lead to cellular regenerative pathways. Cytochrome c oxidase in the mitochondria was reported to be the photoacceptor upon which He‐Ne laser exerts its effects. Mitochondrial retrograde signalling is responsible for the cellular events by red light. This review shows that He‐Ne laser initiated mitochondrial retrograde signalling via a Ca²⁺‐dependent cascade. The impact on cytochrome c oxidase within the mitochondria, an event that results in activation of CREB (cyclic‐AMP response element binding protein)‐related cascade, is responsible for the He‐Ne laser promoting functional development at different stages of MBs and boosting functional melanocytes. He‐Ne laser irradiation induced (a) melanocyte stem cell differentiation; (b) immature outer root sheath MB migration; (c) differentiated outer root sheath MB melanogenesis and migration; and (d) perilesional melanocyte migration and proliferation. These photobiomodulation effects result in perifollocular and marginal repigmentation in vitiligo.     

单抗N糖分析系统适用性对照品的建立

目的建立单抗N糖分析方法的系统适用性对照品,并设定相应的系统适用性要求。方法利用液质联用(LC-MS)仪对N糖系统适用性对照品进行N糖型的表征鉴别,并对对照品进行稳定性评价。结合方法特点和验证数据,对系统适用性要求进行设定。结果建立的系统适用性对照品具有良好的稳定性,其糖型涵盖了单抗主要的N糖型种类。针对3种药典拟收录的单抗N糖分析方法,设定了以下系统适用性要求,包括:图谱与典型图谱相似、G1F(1,6)和G1F(1,3)的分离度应满足具体要求、G0F%应在规定的范围内、G0F保留时间的RSD应≤4%。结论建立了单抗N糖系统适用性对照品,可配合3种2020年版《中国药典》拟收录的N糖分析方法使用。     

乳腺弥漫大B细胞淋巴瘤临床特点及预后分析

目的:探讨乳腺弥漫大B细胞淋巴瘤（DLBCL）患者的临床特征及预后。方法:回顾性分析2014年5月起至2018年12月我院收治的18例乳腺DLBCL患者的临床特点及预后。结果:在18例患者中，17例为女性，中位年龄为57岁，病变主要累及右侧乳腺（11/18，61.1%）。将其分为原发性乳腺DLBCL（PB-DLBCL）和继发性乳腺DLBCL（SB-DLBCL）两大类。11例（57.6%）PB-DLBCL与7例（42.4%）SB-DLBCL相比，其具有Ann Arbor分期多为Ⅰ-Ⅱ期（P＜0.01）、B症状少（P=0.013）、相对更高的白细胞计数（P=0.041）、骨髓未累及（P=0.043）、完全缓解（CR）率高（P=0.049）等特点。生存分析发现PB-DLBCL患者5年总生存期（OS）显著长于SB-DLBCL患者（P=0.013）。本研究以非生发中心B细胞型（non-GCB）居多，生发中心型（GCB）与non-GCB型之间OS无显著差异（P=0.885）。所有获得CR患者的生存期均显著延长（P=0.008）。结论:乳腺DLBCL多见于中年女性，以右侧乳腺肿块为主要临床表现，分子分型多为non-GCB型。与SB-DLBCL患者相比，PB-DLBCL患者具有Ann Arbor分期早、B症状少、相对更高的白细胞计数、骨髓未累及、CR率高等特点，并且生存期显著延长。无论是PB-DLBCL和SB-DLBCL，还是GCB型和non-GCB型，获得CR提示预后良好。     

MicroRNA‐375‐3p enhances chemosensitivity to 5‐fluorouracil by targeting thymidylate synthase in colorectal cancer

Resistance to chemotherapy is a major challenge for the treatment of patients with colorectal cancer (CRC). Previous studies have found that microRNAs (miRNAs) play key roles in drug resistance; however, the role of miRNA‐373‐3p (miR‐375‐3p) in CRC remains unclear. The current study aimed to explore the potential function of miR‐375‐3p in 5‐fluorouracil (5‐FU) resistance. MicroRNA‐375‐3p was found to be widely downregulated in human CRC cell lines and tissues and to promote the sensitivity of CRC cells to 5‐FU by inducing colon cancer cell apoptosis and cycle arrest and by inhibiting cell growth, migration, and invasion in vitro. Thymidylate synthase (TYMS) was found to be a direct target of miR‐375‐3p, and TYMS knockdown exerted similar effects as miR‐375‐3p overexpression on the CRC cellular response to 5‐FU. Lipid‐coated calcium carbonate nanoparticles (NPs) were designed to cotransport 5‐FU and miR‐375‐3p into cells efficiently and rapidly and to release the drugs in a weakly acidic tumor microenvironment. The therapeutic effect of combined miR‐375 + 5‐FU/NPs was significantly higher than that of the individual treatments in mouse s.c. xenografts derived from HCT116 cells. Our results suggest that restoring miR‐375‐3p levels could be a future novel therapeutic strategy to enhance chemosensitivity to 5‐FU.     

马齿苋“成分-活性-中药功效-疾病”研究进展及关联分析

马齿苋是一种药食同源品,具有清热解毒、凉血止血、止痢的功效,为常见中药,作为药物安全性高。马齿苋具有多种活性成分及药理作用,为了充分开发利用马齿苋,加快马齿苋研究的现代化进程,综述马齿苋的研究进展并在此基础上对于其"成分-活性-中药功效-疾病"进行关联分析,为马齿苋的现代化研究提供思路。     

Graph Convolutional Network Based on Manifold Similarity Learning

In the area of large-scale graph data representation and semi-supervised learning, deep graph-based convolutional neural networks have been widely applied. However, typical graph convolutional network (GCN) aggregates information of neighbor nodes based on binary neighborhood similarity (adjacency matrix). It treats all neighbor nodes of one node equally, which does not suppress the influence of dissimilar neighbor nodes. In this paper, we investigate GCN based on similarity matrix instead of adjacency matrix of graph nodes. Gaussian heat kernel similarity in Euclidean space is first adopted, which is named EGCN. Then biologically inspired manifold similarity is trained in reproducing kernel Hilbert space (RKHS), based on which a manifold GCN (named MGCN) is proposed for graph data representation and semi-supervised learning with four different kernel types. The proposed method is evaluated with extensive experiments on four benchmark document citation network datasets. The objective function of manifold similarity learning converges very quickly on different datasets using various kernel functions. Compared with state-of-the-art methods, our method is very competitive in terms of graph node recognition accuracy. In particular, the recognition rates of MGCN (Gaussian kernel) and MGCN (Polynomial Kernel) outperform that of typical GCN about 3.8% on Cora dataset, 3.5% on Citeseer dataset, 1.3% on Pubmed dataset and 4% on Cora_ML dataset, respectively. Although the proposed MGCN is relatively simple and easy to implement, it can discover local manifold structure by manifold similarity learning and suppress the influence of dissimilar neighbor nodes, which shows the effectiveness of the proposed MGCN.