8种寄生虫病例分析

本文报道并分析8例寄生虫病例,其中包括脑裂头蚴病、眼部裂头蚴病、肺吸虫幼虫移行症、肺吸虫病、钩虫病、肝吸虫病、包虫病和广州管圆线虫病各1例。8例寄生虫病均有不同程度的误诊,其中6例在寄生虫抗体筛查检测阳性后得以确诊。因此,寄生虫病应引起临床医生的重视,抗体筛查有助于发现寄生虫病例。     

癌症患者心理韧性质性研究的系统评价

目的 系统评价有关癌症患者心理韧性发展的真实体验,为更好地制定心理韧性干预方案提供理论依据。方法 计算机检索Cochrane Library、Medline、CINAHL、Embase、PubMed、Web of Science、Psyc INFO以及中国生物医学文献数据库、中国知网、维普、万方数据库从建库至2019年6月相关的质性研究。采用澳大利亚乔安娜布里格斯研究所循证卫生保健中心质性研究质量评价标准（2016）对文献进行评价,采用汇集性整合方法进行结果整合。结果 共纳入10篇文献,提炼42个明确的结果,形成8个类别,汇总成3个整合结果。①心理韧性发展是动态循环的过程：危机负性期,调节成长期,积极转化期,循环强化期;②心理韧性的阻碍因素：个体（消极应对,错误认知,症状和体象困扰）,环境（传统社会文化,医疗环境,家庭负担）;③心理韧性的保护因素：个体（积极心理与应对方式,复原经历,宗教信仰,自我表露,利他主义与行为）,环境（家庭因素,医疗照护,人际支持）。结论 心理韧性的发展是动态循环过程,受个体和环境因素影响,医护人员应根据不同阶段的影响因素给予针对性护理措施,促进患者心理韧性的形成和发展,提高患者的生活质量。     

自然周期与促排卵周期联合宫腔内人工授精临床疗效的meta分析

目的系统评价自然周期和促排卵周期联合宫腔内人工授精(intrauterine insemination,IUI)对不孕患者治疗的有效性,旨在更合理有效的指导临床治疗。方法计算机检索2000年1月至2013年6月中国生物医学文献数据库(CBMDisc)、万方数据库、中国学术期刊网专题全文数据库(CNKI)、维普数据库、Pubmed、外文生物医学期刊文献数据(FMJS)中自然周期和促排卵周期联合IUI治疗不孕患者的随机对照试验(RCT)或临床对照试验。由2位评价员根据纳入与排除标准独立进行文献筛选、资料提取和质量评价后,采用Rev Man 5.0软件进行Meta分析。结果最终纳入9个研究,共8814个周期。Meta分析结果显示:对行IUI的不孕症患者,促排卵周期组与自然周期组相比,妊娠率[OR=1.47,95%CI(1.26,1.72),P0.00001]、流产率[OR=2.49,95%CI(1.49,4.16),P=0.0005]、多胎率[OR=6.94,95%CI(1.94,24.83),P=0.003]均大于自然周期组,且差异有统计学意义;OHSS发生率[OR=4.17,95%CI(0.74,23.49),P=0.11]和宫外孕发生率[OR=2.22,95%CI(0.92,5.37),P=0.08]无明显差异。结论对自然周期和促排卵周期联合IUI治疗不孕患者的有效性而言,促排卵周期组能更好的改善其妊娠率,但其流产率和多胎率的发生较高,因此促排卵方案用于IUI时,其疗效及安全性需要进行更多的临床研究。由于纳入文献存在质量和数量不足以及方法学差异,本研究结论仅作为临床分析的参考,尚需后效评价和不断更新。     

地氯雷他定联合雷公藤多苷片对慢性特发性荨麻疹患者的临床疗效及情绪的影响

目的 探讨分析地氯雷他定联合雷公藤多苷片对慢性特发性荨麻疹(CIU)患者的临床疗效及负面情绪的影响.方法 选取本院2018年2月～2020年6月住院治疗的80例CIU患者作为主要研究对象,按随机数字表法将研究对象随机分为观察组和对照组,对照组40例患者予以地氯雷他定治疗,观察组40例患者予以地氯雷他定联合雷公藤多苷片治疗.观察比较两组的临床疗效、不良反应发生率、复发率,及治疗前后的风团数目、风团大小、瘙痒评分、汉密尔顿焦虑量表(Hamilton anxiety scale,HAMA)评分和汉密尔顿抑郁量表(Hamilton depression scale,HAMD)评分.结果 (1)观察组患者临床总有效率(95.00％)高于对照组(72.50％)(P＜0.05).(2)观察组患者的风团数目减少、风团变小、瘙痒评分降低,改善优于对照组(P＜0.05).(3)观察组患者的不良反应发生率(5.00％)低于对照组(7.50％)(P＜0.05),观察组患者的复发率(2.50％)低于对照组(15.00％)(P＜0.05).(4)观察组患者的HAMA评分和HAMD评分低于对照组(P＜0.05).结论 地氯雷他定联合雷公藤多苷片可有效缓解CIU患者的瘙痒症状,减少风团数目,缓解患者负面情绪,且不良反应较少,患者复发率较低,临床疗效确切,值得临床推广应用.     

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.     

Host–microbiome interactions: the aryl hydrocarbon receptor as a critical node in tryptophan metabolites to brain signaling

ABSTRACT

Tryptophan (Trp) is not only a nutrient enhancer but also has systemic effects. Trp metabolites signaling through the well-known aryl hydrocarbon receptor (AhR) constitute the interface of microbiome-gut-brain axis. However, the pathway through which Trp metabolites affect central nervous system (CNS) function have not been fully elucidated. AhR participates in a broad variety of physiological and pathological processes that also highly relevant to intestinal homeostasis and CNS diseases. Via the AhR-dependent mechanism, Trp metabolites connect bidirectional signaling between the gut microbiome and the brain, mediated via immune, metabolic, and neural (vagal) signaling mechanisms, with downstream effects on behavior and CNS function. These findings shed light on the complex Trp regulation of microbiome-gut-brain axis and add another facet to our understanding that dietary Trp is expected to be a promising noninvasive approach for alleviating systemic diseases.     

铜绿假单胞菌噬菌体PaP3生物学特性的研究

目的测定铜绿假单胞菌噬菌体PaP3的最佳感染复数、一步生长曲线和吸附K值及交叉吸附K值等基本生物学特性。方法按照感染复数（MOI）分别为0．0001、0．001、0．01、0．1、1和10加入噬菌体纯培养液和宿主菌，充分裂解细菌后，测定噬菌体滴度；以MOI=10的比例加入噬菌体及宿主菌，进行一步生长实验；纯化PaP3颗粒，免疫家兔，获得抗血清，通过中和反应实验测定PaP3和其抗血清之间的吸附反应常数K值。同时，利用抗血清交叉中和试验确定本室分离的三株噬菌体之间的血清学关系。结果当MOI=0．001时PaP3感染其宿主菌产生的子代噬菌体滴度最高；根据一步生长实验结果绘制一步生长曲线；通过血清交叉中和反应得出不同的吸附常数。结论PaP3最佳感染复数为0．001，感染宿主菌的潜伏期是20min，爆发期是60min，平均爆发量约为31，其抗血清反应的吸附常数K值为262。     

影响颅脑外伤术后颅内感染的危险因素分析

目的探讨影响颅脑外伤开颅术后颅内感染的危险因素。方法采用回顾性研究比较分析了912例颅脑损伤术后出现颅内感染与未出现颅内感染组间的差异因素。结果非感染 770例,颅内感染142例(15．6％)。感染类型有无菌性脑膜炎、细菌性脑膜炎、脑室炎及脑室积脓、脑脓肿、硬膜下腔积脓、术区皮下或(和)骨瓣下化脓或合并骨髓炎、切口感染。细菌检出率占感染的27．5％。颅脑外伤术后感染与高温季节、高龄、重度以上损伤、短期内两次以上手术、连续两侧开颅术、长时间 (>5 h)手术、显微外科手术、颅底与后颅凹手术、脑室外引流、皮下或硬膜外积液以及急诊手术等因素相关(P<0．05)。结论对具有上述危险冈素的易感患者应给予更密切的关注和预防性的处理。