珊瑚菜Gl4CL 基因克隆与生物信息学分析

目的：对动物药材DNA提取方法进行优化,利用优化方法提取市售动物药材DNA并进行DNA条形码鉴定。方法：基于SDS法DNA提取原理,比较裂解液中不同EDTA浓度（0.025、0.25、0.5 mol·L-1）、是否含NaCl和Triton X-100等因素对不同用药部位动物药材DNA提取质量的影响,筛选得到最佳裂解液配方;使用优化的裂解液配方提取121份市售动物药材DNA并进行基原物种鉴定。结果：裂解液配方为1 % SDS、0.03 mol·L-1 Tris-HCl、0.25 mol·L-1 EDTA、0.2 mol·L-1NaCl对不同用药部位动物药材DNA提取效果最佳,并可实现对蝉蜕等提取困难样本DNA的提取;利用优化裂解液提取的121份市售动物药材DNA满足中药材分子鉴定后续实验要求,所有市售动物药材均可准确鉴定到基原物种。结论：本研究优化的裂解液配方可用于除壳类、分泌物类、加工品外不同用药部位动物药材的DNA提取,为动物药材分子鉴定提供了技术支持。     

多指标正交试验法优选余甘子总酚提取工艺

目的：优选余甘子总酚提取工艺,为其工业化生产提供数据参考。方法：以总酚、诃黎勒酸、没食 子酸和粘酸-2-O-没食子酸酯提取量为综合评价指标,采用正交试验设计优选提取溶剂、溶剂用量、提取次数、 提取时间等并进行验证,确定最佳提取工艺。结果：余甘子总酚提取工艺为10倍量70%乙醇提取3次,每次 90 min。结论：本文优化获得的余甘子总酚提取工艺稳定可靠,简便易行,为余甘子产业化研究提供依据。     

微波帮助提取中药金银花中有效成分的分析

目的:分析微波帮助提取中药金银花中有效成分的可行性。方法:以中药金银花作为提取对象,采用微波帮助提取以及超声波提取法对中药金银花有效成分进行提取。采用紫外可见分光光度法进行两种方法提取效果的对比。结果:紫外可见光光度法检测结果显示微波帮助提取金银花有效成分的提取率要比超声波提取法高30.2%。微波帮助提取恒定时间为1 min、超声波提取时间为40 min。结论:微波帮助提取中药金银花中有效成分的提取效果较佳,且所需时间较短,可对中药有效成分进行快速分析。     

Position-aware deep multi-task learning for drug–drug interaction extraction

ObjectiveA drug–drug interaction (DDI) is a situation in which a drug affects the activity of another drug synergistically or antagonistically when being administered together. The information of DDIs is crucial for healthcare professionals to prevent adverse drug events. Although some known DDIs can be found in purposely-built databases such as DrugBank, most information is still buried in scientific publications. Therefore, automatically extracting DDIs from biomedical texts is sorely needed.Methods and materialIn this paper, we propose a novel position-aware deep multi-task learning approach for extracting DDIs from biomedical texts. In particular, sentences are represented as a sequence of word embeddings and position embeddings. An attention-based bidirectional long short-term memory (BiLSTM) network is used to encode each sentence. The relative position information of words with the target drugs in text is combined with the hidden states of BiLSTM to generate the position-aware attention weights. Moreover, the tasks of predicting whether or not two drugs interact with each other and further distinguishing the types of interactions are learned jointly in multi-task learning framework.ResultsThe proposed approach has been evaluated on the DDIExtraction challenge 2013 corpus and the results show that with the position-aware attention only, our proposed approach outperforms the state-of-the-art method by 0.99% for binary DDI classification, and with both position-aware attention and multi-task learning, our approach achieves a micro F-score of 72.99% on interaction type identification, outperforming the state-of-the-art approach by 1.51%, which demonstrates the effectiveness of the proposed approach.     

Lung sounds classification using convolutional neural networks

Lung sounds convey relevant information related to pulmonary disorders, and to evaluate patients with pulmonary conditions, the physician or the doctor uses the traditional auscultation technique. However, this technique suffers from limitations. For example, if the physician is not well trained, this may lead to a wrong diagnosis. Moreover, lung sounds are non-stationary, complicating the tasks of analysis, recognition, and distinction. This is why developing automatic recognition systems can help to deal with these limitations. In this paper, we compare three machine learning approaches for lung sounds classification. The first two approaches are based on the extraction of a set of handcrafted features trained by three different classifiers (support vector machines, k-nearest neighbor, and Gaussian mixture models) while the third approach is based on the design of convolutional neural networks (CNN). In the first approach, we extracted the 12 MFCC coefficients from the audio files then calculated six MFCCs statistics. We also experimented normalization using zero mean and unity variance to enhance accuracy. In the second approach, the local binary pattern (LBP) features are extracted from the visual representation of the audio files (spectrograms). The features are normalized using whitening. The dataset used in this work consists of seven classes (normal, coarse crackle, fine crackle, monophonic wheeze, polyphonic wheeze, squawk, and stridor). We have also experimentally tested dataset augmentation techniques on the spectrograms to enhance the ultimate accuracy of the CNN. The results show that CNN outperformed the handcrafted feature based classifiers.     

Characterizing alleles with large deletions using region specific extraction

Two novel HLA class II alleles, DRB4*03:01N and DQB1*03:276N, containing large deletions were identified during routine typing. Extraction of DNA encompassing the deletions was carried out with a panel of capture oligonucleotides followed by whole genome amplification. Next generation DNA sequencing was then used to characterize the sequences. DRB4*03:01N has a 16 kilobase pair deletion stretching upstream from intron 2 toward centromeric DRB8. DQB1*03:276N has two deletions separated by 844 nucleotides. The first deletion (3.7 kilobase pairs) is upstream of intron 1 and the second deletion removes 3.3 kilobase pairs further upstream towards centromeric DQA2.     

Autophagy deficiency in myeloid cells exacerbates eosinophilic inflammation in chronic rhinosinusitis

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Crestal Bone Level Changes Around Immediately Placed Implants: A Systematic Review and Meta‐Analyses With at Least 12 Months’ Follow‐Up After Functional Loading

Background: Immediate implant placement (IIP) is a successful treatment and has the advantages of reducing time and increasing patient satisfaction. However, achieving predictable esthetic results with IIP presents a challenge because of naturally occurring bone loss postextraction. Therefore, the focused question of this systematic review is: What is the effect of IIP on crestal bone level (CBL) changes after at least 12 months of functional loading? Methods: Extensive literature review of the Cochrane and MEDLINE electronic databases and a manual search up to November 2012 identified eligible studies. Two reviewers independently assessed the study data and methodologic quality using data extraction and assessment forms. Results: Electronic and manual searches identified 648 relevant publications. A total of 57 articles satisfied the inclusion criteria. Sixteen studies had test and control groups; therefore, meta‐analyses could be performed. The results demonstrated better CBL preservation around IIP compared with implant placement in healed/native bone at 12 months [CBL difference of ?0.242 (95% confidence interval [CI], ?0.403 to ?0.080; P = 0.003)]. Similarly, platform switching around IIP showed better results compared with non–platform switching (CBL difference of ?0.770 [95% CI, ?1.153 to ?0.387; P <0.001]). There was no difference in mean CBL changes with regard to one‐stage or two‐stage IIP protocol (?0.017 [95% CI, ?0.249 to 0.216; P = 0.85]) or the use of immediate or delayed immediate implant loading (0.002 [95% CI, ?0.269 to 0.272; P = 0.99]). Conclusions: Meta‐analyses showed less CBL loss around IIP compared with implant placement in healed bone. Platform‐switched implants showed greater crestal bone preservation than non–platform‐switched implants. There was no significant difference in CBL with one‐ versus two‐stage placement or use of immediate versus delayed IIP loading. Although there were statistically significant differences favoring IIP, the small differences may not be clinically relevant. Although IIP showed favorable outcomes for CBL changes, these results should be interpreted with caution because of high heterogeneity among studies.