水分去除的方法及其在药物合成中的应用

化学药合成过程中水分残留不利于反应的进行,还影响药物及制剂的稳定性、理化性质、溶出及药理作用等,因此水分几乎是大多数药物合成中的必测项目,通过有效除水严格控制反应体系中水分的含量至关重要。本文综述了水分的存在形式,常见除水剂和脱水剂的种类、作用原理与除水能力,以及在药物合成的应用,为药物合成反应中水分的去除提供参考。     

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.

     

PBL与思维导图相结合教学模式在临床教学中的应用

目的探讨PBL与思维导图相结合教学模式在临床教学中的应用。方法本文研究对象为在我院见习的临床本科生,为大理大学2015级一个班级,共55名学生,数据收集时间为2019年1月;按照授课方式的差异将学生分为实验班与对照班,实验班27名学生,对照班28名学生;对照班实施常规教学法,实验班实施PBL与思维导图相结合教学法。结果实验班教学结束测试得分为(85.60±3.46)分,对照班教学结束测试得分为(77.48±3.12)分,差异具有统计学意义(P<0.05);实验班教学方式满意度评分为(90.35±4.12)分,对照班教学方式满意度评分为(80.25±4.05)分,差异具有统计学意义(P<0.05)。结论在临床教学中采用PBL与思维导图相结合教学模式,能够提高学生的专业知识掌握水平与对教学模式的认可度。     

补肾壮骨汤对骨质疏松型胸腰椎压缩骨折患者椎体成形术治疗效果影响的Meta分析

[目的]采用Meta分析法系统评价补肾壮骨汤对椎体成形术治疗骨质疏松型胸腰椎压缩骨折(OTCF)临床的影响。[方法]以"补肾壮骨汤"为首输关键词,以"椎体成形术""骨质疏松""胸腰椎压缩骨折"等为次输关键词,全面搜索补肾壮骨汤对OTCF患者椎体成形术治疗效果影响的随机对照研究文献,评价纳入研究文献的一般特征,对文献资料所提取的治疗有效率、骨密度、Oswestry功能障碍(ODI)指数、治疗安全性(新发骨折率)等观察指标进行Meta分析处理与分析。[结果]共13篇文献纳入Meta分析范畴,文献总样本量1 037例。分析显示,与对照组相比,治疗组可明显提高临床有效率[OR=4.63,95%CI[2.92,7.36],P0.000 01]、改善骨密度[MD=0.08,95%CI[0.06,0.1],P0.000 01]、降低ODI指数[MD=-2.34,95%CI[-3.49,-1.19],P0.000 01]、降低新发骨折率[OR=0.08,95%CI[0.11,0.44],P0.000 01]。[结论]与单纯椎体成形术治疗相比,补肾壮骨汤辅助椎体成形术在OTCF患者治疗中,可提升患者术后骨密度、改善胸腰椎功能障碍和降低术后新发骨折率,治疗疗效确切。     

平卧菊三七提取物抗氧化活性研究与抗氧化特征成分的HPLC-MS/MS分析

目的:采用DPPH体系测定平卧菊三七地上部分不同极性部位提取物的抗氧化活性,并利用液质联用(HPLC-MS/MS)技术分析了活性较高的乙酸乙酯部位的抗氧化特征性成分。方法:对平卧菊三七地上部分提取物用石油醚、乙酸乙酯、正丁醇进行萃取得到石油醚部位、乙酸乙酯部位、正丁醇部位及水部位。考察平卧菊三七地上部分不同极性部位提取物对清除1,1-二苯基-2-三硝基苯肼(DPPH)自由基的能力以及通过液质联用电喷雾四级杆飞行时间质谱仪(HPLC-ESI-Q-TOF-MS)技术对其活性部位中的化学成分进行定性分析。采用液质联用(LC-MS/MS),C_(18)色谱柱(150 mm×4.6 mm,5μm)以甲醇-0.1%甲酸溶液为流动相梯度洗脱,使用ESI负离子模式下采集数据。通过质谱数据分析,并与对照品及文献对照,对其进行成分鉴定。结果:平卧菊三七地上部分不同极性提取物对DPPH自由基都具有清除力,其中乙酸乙酯部位的清除能力最强。通过其质谱数据分析,并与对照品及相关文献对照,共鉴定出11个化合物。结论:应用DPPH抗氧化活性筛选及HPLC-ESI-Q-TOF-MS技术能够快速准确地发现平卧菊三七地上部分抗氧化活性成分,为进一步研究平卧菊三七的药效物质基础提供参考。     

Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning

Conservation laws are considered to be fundamental laws of nature. It has broad applications in many fields, including physics, chemistry, biology, geology, and engineering. Solving the differential equations associated with conservation laws is a major branch in computational mathematics. The recent success of machine learning, especially deep learning in areas such as computer vision and natural language processing, has attracted a lot of attention from the community of computational mathematics and inspired many intriguing works in combining machine learning with traditional methods. In this paper, we are the first to view numerical PDE solvers as an MDP and to use (deep) RL to learn new solvers. As proof of concept, we focus on 1-dimensional scalar conservation laws. We deploy the machinery of deep reinforcement learning to train a policy network that can decide on how the numerical solutions should be approximated in a sequential and spatial-temporal adaptive manner. We will show that the problem of solving conservation laws can be naturally viewed as a sequential decision-making process, and the numerical schemes learned in such a way can easily enforce long-term accuracy. Furthermore, the learned policy network is carefully designed to determine a good local discrete approximation based on the current state of the solution, which essentially makes the proposed method a meta-learning approach. In other words, the proposed method is capable of learning how to discretize for a given situation mimicking human experts. Finally, we will provide details on how the policy network is trained, how well it performs compared with some state-of-the-art numerical solvers such as WENO schemes, and supervised learning based approach L3D and PINN, and how well it generalizes.     

补骨脂素抗增生性瘢痕作用机制研究

目的探讨补骨脂素抗增生性瘢痕的作用机制。方法体外培养成纤维细胞,按随机数字表法分为正常组(培养正常成纤维细胞)、瘢痕组(培养增生性瘢痕成纤维细胞)、TGF-β1组(10 ng/ml TGF-β1处理增生性瘢痕成纤维细胞5 min^12 h)、Smurf2 RNA干扰组[Smad泛素化调节因子2(Smad ubiquitin regulatory factor2,Smurf2)siRNA转染增生性瘢痕成纤维细胞72 h]、补骨脂素组(10μmol/L补骨脂素处理增生性瘢痕成纤维细胞继续培养72 h)、补骨脂素+TGF-β1组(增生性瘢痕成纤维细胞加入补骨脂素培养72 h后加入TGF-β1培养6 h)。采用Western blot法检测Smurf2、α-平滑肌肌动蛋白(α-actin SMA,α-SMA)蛋白表达;RT-PCR法检测Ⅰ型胶原蛋白mRNA表达;ELISA法检测TGF-β1蛋白分泌。结果与正常组比较,瘢痕组Smurf2蛋白[(0.83±0.08)比(0.38±0.07)]表达增加(P<0.05);与瘢痕组比较,Smurf2 RNA干扰组TGF-β1[(2.2±0.18)比(4.2±0.47)]表达降低(P<0.05);TGF-β1组Smurf2[(0.71±0.06)比(0.42±0.04)]、α-SMA[(1.42±0.12)比(0.91±0.09)]蛋白表达增加(P<0.05),Ⅰ型胶原蛋白mRNA[(0.72±0.09)比(0.41±0.07)]表达增加(P<0.05);补骨脂素组Smurf2[(0.05±0.01)比(0.42±0.04)]、α-SMA[(0.71±0.07)比(0.91±0.09)]蛋白表达降低(P<0.05),Ⅰ型胶原蛋白mRNA表达[(0.12±0.04)比(0.41±0.07)]降低(P<0.05)。结论补骨脂素可能通过TGF-β1/Smurf2信号通路抑制α-SMA蛋白表达,从而降低Ⅰ型胶原蛋白表达,起到抑制瘢痕形成的作用。     

反复右美托咪定联合丙泊酚麻醉对成年大鼠学习记忆能力的影响

目的观察反复右美托咪定联合丙泊酚麻醉对成年大鼠学习记忆能力的影响。方法选取8周龄SD大鼠40只,雌雄不分,随机分为四组各10只:丙泊酚组(P组),右美托咪定组(D组),右美托咪定联合丙泊酚组(DP组),生理盐水组(NS组)。P组给予30 mg/kg的丙泊酚,D组给予30μg/kg的右美托咪定,DP组给予30 mg/kg的丙泊酚和30μg/kg的右美托咪定,NS组给予等量生理盐水,所有药物均采用尾静脉注射法,每日1次,连续5天,第6天起,选用Morris水迷宫进行行为学实验。结果在定位航行实验中,D组、DP组、NS组与P组比较,前4天的潜伏期差异有统计学意义(P<0.05),其中P组的潜伏期均明显长于DP组、D组、NS组(P<0.05);DP组、NS组与D组比较,前4天的潜伏期差异无统计学意义(P>0.05),且第2、3、4天三组的潜伏期均明显短于第1天(P<0.05);在空间探索实验中,各组间各项数据差异均无统计学意义(P>0.05)。结论右美托咪定对于在镇静剂量下丙泊酚麻醉所导致的成年大鼠的学习记忆功能的短暂损伤具有保护作用。     

改良俯卧位对俯卧位通气临床疗效及并发症的影响

目的观察改良俯卧位对俯卧位通气(PPV)临床疗效及并发症的影响。方法将该院呼吸和危重症医学科行PPV的52例患者随机分为对照组和实验组各26例,对照组应用常规方法进行PPV,实验组采用改良俯卧位进行PPV。比较两组患者PPV的临床效果和并发症发生情况。结果两组间俯卧位前和俯卧位后12 h的氧合指数无明显差异(P>0.05);两组患者俯卧位后12 h的氧合指数均较各组俯卧位前明显改善(P<0.05);两组患者气管插管脱出、血流动力学显著波动、误吸和面部水肿的发生率无显著差异(P>0.05);实验组皮肤压疮的发生率明显低于对照组(P<0.05)。结论改良俯卧位对PPV的临床疗效无显著影响,但可以降低皮肤压疮的发生率。     

Evidence of impaired pain modulation in adolescents with idiopathic scoliosis and chronic back pain

BACKGROUND CONTEXT

Although 40% of adolescent idiopathic scoliosis (AIS) patients present with chronic back pain, the pathophysiology and underlying pain mechanisms remain poorly understood. We hypothesized that development of chronic pain syndrome in AIS is associated with alterations in pain modulatory mechanisms.