我院药物代谢酶和药物作用靶点基因检测与精准药学服务实践与总结

目的回顾我院药物代谢酶和药物作用靶点相关基因检测与精准药学服务实践过程,总结经验,与同行分享。方法从准备工作、相关检测项目的确定,学术推广、项目优化和开展情况等方面详细阐述我院基因检测开展过程与精准药学服务情况。结果根据临床需求,我院已开展了以心脑血管药和抗精神病药为主的27种药物,26项检测,2018年全年位1587名患者提供个体化用药建议,受到临床医生和患者欢迎。结论基于代谢酶和药物作用靶点相关基因检测的个体化用药建议是临床药师参与精准治疗的重要途径,有助于医生和药师之间的沟通,有利于提高药学服务水平。     

OSAHS患儿扁桃体和腺样体术后免疫功能的变化

目的 阻塞性睡眠呼吸暂停低通气综合征(OSAHS)是耳鼻咽喉科的一种常见病，儿童发病率呈上升趋势，调查显示儿童OSAHS发生率为4%~5%，2~6岁是发病的高峰期。OSAHS不仅严重影响儿童的睡眠质量，还会引起儿童生长发育迟缓、智力缺陷等一系列不良反应，OSAHS患儿的主要病因是扁桃体和/或腺样体肥大，因此最有效的治疗方法是扁桃体切除术和腺样体切除术。而扁桃体和腺样体是儿童淋巴组织系统的重要组成部分，在机体的防御和保护功能中发挥着重要作用。手术治疗OSAHS是否影响儿童的免疫功能目前存在争议。一些学者认为扁桃体切除和腺样体切除后儿童的免疫力降低，而另一些学者认为OSAHS儿童的免疫力在手术后保持不变，甚至比手术前有所提高。本文就儿童OSAHS手术前后免疫指标的变化作一综述。     

“透法”在外感热病中的运用

外感之邪从口鼻、皮毛侵袭人体,肺卫首先受邪。在外感病初期,肺卫失宣是病机的关键,治疗应重在宣通肺卫。外感风寒邪气,寒邪直接损伤卫阳,郁遏肺气,治当辛温解表。外感风热邪气,阳邪郁遏气机,损伤津液,易化燥化火,甚则内陷生变,治当辛凉解表。湿热邪气从口鼻、皮毛而入,初起即表现为卫气同病,治当宣化表里湿热。燥气为病,易伤肺脏、耗津液,与热相合则易化热化火,而成温燥,治宜辛凉甘润。外感热病初起,六淫邪气侵袭肺卫,气机郁滞,进而津停成饮、成痰;气有余便是火,郁滞之气化燥化火,甚则动血。治疗总则为宣通气机,此即"透法"之运用。故在外感热病初起的治疗中,贵在把握气机状态,让肺卫之气宣畅,正气宣布,则邪气潜消。     

贵阳地区儿童重症社区获得性肺炎肺泡灌洗液病原及药敏分析

目的分析贵阳地区儿童重症社区获得性肺炎支气管肺泡灌洗液（BALF）病原学分布及耐药特点。方法收集989例重症社区获得性肺炎患儿临床资料，将支气管肺泡灌液采用支气管镜取出进行细菌培养、病毒以及肺炎支原体(MP)检测。结果（1）989例重症社区获得性肺炎患儿病原检出阳性716例，阳性率72.40%，细菌、病毒、支原体检出率分别33.27% （329例）、22.45%（222例）、31.45%（311例）。（2）细菌感染中的肺炎链球菌、金黄色葡萄球菌为最为常见的革兰阳性菌株(G+)；而肺炎克雷伯菌、大肠埃希菌为最为常见的革兰阴性菌株(G-)。培养菌株对青霉素类、红霉素、第1、2、3代头孢类抗生素有较高的耐药性，对头孢吡肟、拉氧头孢、哌拉西林、左氧氟沙星有较高的敏感性，对亚胺培南、万古霉素、利奈唑烷均无耐药发生。（3）病毒感染检出222例，其中呼吸道合胞病毒131例，腺病毒检测49例，流感病毒6例（A型2例，B型4例），副流感病毒36例（1型3例、2型4例、3型29例），病毒检出率以0～12月龄组最高，RSV、ADV感染主要集中在冬春季节。（4）肺炎支原体检出阳性率31.45%（311例），肺炎支原体检出率以3～5岁组最高。结论贵阳地区重症肺炎中肺炎克雷伯杆菌、肺炎链球菌、大肠埃希菌、金黄色葡萄球菌为重要的细菌病原。重要病毒为腺病毒和呼吸道病毒为主，1～12月龄组的病毒感染检出率比较高。     

The influence of processed total non-forward and non-motile sperm count on the outcome of artificial insemination with the husband’s semen

BackgroundArtificial insemination with the husband’s semen (AIH) is an economical and noninvasive method of infertility treatment. However, AIH’s pregnancy rate is much lower than in vitro fertilization (IVF) as its multiple and complex uncertainty factors. Semen quality has been one of the main factors which affect the pregnancy outcome of AIH.MethodsThe relevant parameters of 1,142 AIH cycles were retrospectively studied, including the general parameters and the semen quality parameters among clinical pregnancy, biochemical pregnancy, non-pregnancy group, age, infertility duration, infertility type, body mass index (BMI), cycle count, morphology in previously semen examination, and semen quality parameters on the day of AIH.ResultsThe statistically significant difference was only found on processed total non-forward and non-motile sperm count (N-TFMSC). The mean processed N-TFMSC in the biochemical pregnancy group was 6.37±4.27 million, significantly higher than the other two groups (vs. 4.40±3.15 million or vs. 4.48±3.60 million, P<0.05). The study was then divided into two groups according to processed N-TFMSC, Group 1 ≤5.0 million, and Group 2 >5.0 million. A statistical increase in biochemical pregnancy rate was observed when the processed N-TFMSC was >5.0 million (2.72% vs. 0.90%).ConclusionsProcessed N-TFMSC may be one of the independent factors on AIH’s outcome; it should be given equal attention the same as processed total forward motile sperm count (TFMSC).     

Geographical Origin and Level Identification of Frankincense Based on Hyperspectral Image

Background: As the demand for traditional Chinese medicinal materials increases in China and even the world, there is an urgent need for an effective and simple identification technology to identify the origin and quality of the latter and ensure the safety of clinical medication. Mineral element analysis and isotope finger-printing are the two commonly used techniques in traditional origin identification. Both of these techniques require the use of stoichiometric methods in the identification process. Although they have high accuracy and sensitivity, they are expensive and inefficient. In addition, near-infrared spectroscopy is a fast, nondestructive, and widely used identification technique developed in recent years, but its identification results are susceptible to samples’ states and environmental conditions, and its sensitivity is low. Hyperspectral imaging combines the advantages of imaging technology and optical technology, which can simultaneously access the image information and spectral information which reflect the external characteristics, internal physical structure, and chemical composition of the samples. Hyperspectral imaging is widely applied to agricultural product inspection, but research into its application in origin and quality identification of TCM materials is rare. Methods: In this study, the algorithm framework discriminative marginalized least squares regression (DMLSR) was used for feature extraction of frankincense hyperspectral data. The DMLSR with intraclass compactness graph and manifold regularization can efficiently learn the projective samples with higher separability and less redundant information than the original samples. Then, the discriminative collaborative representation with Tikhonov regularization (DCRT) was applied for classifying the geographical origin and level of frankincense. DCRT introducesthe discriminant regularization term and incorporates SID, which is more sensitive to the spectrum as the measurement method and is more suitable for the frankincense spectral data compared with SVM. Results: For the origin classification task, samples of all levels from each origin were, respectively, selected for three?way classification. We used 10-fold cross-validation to select a model parameter in the experiment. When obtaining the optimal parameters, we randomly selected the training set and testing set, where the training set accounts for 70% and the training set for 30%. After repeating this random process 10 times, we obtained the final average classification accuracy, which is higher than 90%, and the standard deviation fluctuation is usually small. For the level classification task, samples of each level from three origins were separately selected for multiclassification. We randomly selected the training set and testing set from each origin. The level classification results of the three origins are good on D4350 data, and the classification accuracy of each level is basically above 80%. Conclusion: Experiments and analysis show that our algorithm framework has excellent classification performance, which is stable in origin classification and has potential for generalization. In addition, the experiments show that in our algorithm framework, different classification tasks need to combine different data sources to achieve better classification and recognition, as the origin classification task uses frankincense’s D3000 data, and level classification task uses frankincense’s D4350 data.