液相色谱-串联质谱法监测不同负荷方案替考拉宁治疗骨髓炎患者的血药浓度比较

目的建立并验证液相色谱-串联质谱法(LC-MS/MS)检测替考拉宁血浆浓度,并分析我院不同负荷方案骨髓炎患者的替考拉宁谷浓度(C_(min))达标情况。方法建立LC-MS/MS法测定替考拉宁的C_(min),考察方法的专属性、标准曲线、精密度与回收率、基质效应和稳定性。收集43例接受替考拉宁治疗的骨髓炎患者,根据给药方案不同分为2组:试验组A患者接受3次负荷剂量400 mg,q12h,试验组B患者接受6次负荷剂量400 mg,q12h,同时进行治疗药物监测(TDM)。两组均于第4天维持剂量给药前30 min采样,检测患者替考拉宁C_(min)值,比较两组替考拉宁C_(min)达标情况。结果血浆中替考拉宁标准曲线方程为Y=0.0324X+0.000886(r=0.9964),在0.20~50.00μg·m L^(-1)内线性关系良好;质控样本的准确度为94.71%~112.50%内;日内、日间RSD为3.17%~15.19%;提取回收率为81.22%~91.26%;基质效应为93.54%~119.78%。临床应用中,试验组A负荷剂量后替考拉宁平均C_(min)为(8.32±4.76μg·mL^(-1),C_(min)≥10μg·mL^(-1)比例仅为25.90%;试验组B负荷剂量后替考拉宁平均C_(min)为(19.58±7.78)μg·mL^(-1),16例患者C_(min)均≥10μg·mL^(-1);两组平均C_(min)差异有统计学意义(P<0.0001)。结论建立的LC-MS/MS方法适用于替考拉宁常规TDM。临床应用中,低频次负荷方案下替考拉宁C_(min)达标情况较差,而TDM指导的高频次负荷方案下替考拉宁C_(min)达标情况更优。研究表明替考拉宁TDM的重要性,尤其是针对感染高危患者人群。

Using Sr isotopes to trace the geographic origins of Chinese mitten crabs

Identifying the geographic origins of Chinese mitten crabs is important for food safety and fair market competition.In this study,we used strontium(Sr) isotopes as a tool to trace the geographic origins of Chinese mitten crabs.Chinese mitten crabs,water,and different types of crab feed were collected from four different lakes for Sr isotope analyses.The results showed that the Sr isotope compositions of the different parts from one single crab were consistent within error,indicating that any piece of a crab could be used to represent the Sr isotope characteristics of the whole crab.The Sr isotope compositions of Chinese mitten crabs from the same lake were consistent within the analytical error,and the values were similar to the Sr isotope composition of the water from the same lake.However,the Sr isotope compositions of water and crabs from different lakes are significantly different.Therefore,the Sr isotope compositions of Chinese mitten crabs are mainly controlled by lake water composition,while the impact of feed is limited.This study provides an effective method for tracing the geographic origins of Chinese mitten crabs.

The correlation between rat retinal nerve fiber layer thickness around optic disc by using optical coherence tomography and histological measurements

AIM:To explore the correlation between the retinal nerve fiber layer(RNFL) thickness by using optical coherence tomography(OCT) and by histological measurements in normal adult rats and optic nerve transected rats.·METHODS:The RNFL thickness of 36 rats was scanned in a circle 3.46mm far from the optic disc by OCT.The two experimental groups were the normal group(n =20 rats) and the optic nerve transected group(n =16 rats).The latter group included 4 groups(n =4 /group)surviving for 1 day,3,5 and 7 days.Then the RNFL thickness of the same retina area was also measured by NF-200 immunohistochemical staining method.Linear regression was used to analyze the correlation between the data obtained from these two methods.·RESULTS:The RNFL thickness of normal right eyes around optic disc by OCT was 72.35 ±5.71μm and that ofthe left eyes was 72.65 ±5.88μm(P =0.074).The RNFL thickness of the corresponding histological section by immunohistochemistry was 37.54 ±4.05μm(right eyes)and 37.38 ±4.23μm(left eyes)(P =0.059).There was a good correlation between the RNFL thickness measured by OCT and that measured by histology(R2=0.8131).After optic nerve transection,the trend of the RNFL thickness was thinner with the prolonged survival time.The correlation of the thickness detected by the above two methods was approximately(R2=0.8265).Value of the RNFL thickness in rats around optic disc measured by OCT was obviously higher than that measured by common histological measurement in normal adult rats and optic nerve transected rats.·CONCLUSION:The RNFL thickness measured by OCT has a strong correlation with that measured by histological method.Through OCT scanning,we found that the thickness of RNFL gradually becomes thinner in a time-dependent manner.

Topic discovery and evolution in scientific literature based on content and citations

Researchers across the globe have been increasingly interested in the manner in which important research topics evolve over time within the corpus of scientific literature. In a dataset of scientific articles, each document can be considered to comprise both the words of the document itself and its citations of other documents. In this paper, we propose a citationcontent-latent Dirichlet allocation(LDA) topic discovery method that accounts for both document citation relations and the content of the document itself via a probabilistic generative model. The citation-content-LDA topic model exploits a two-level topic model that includes the citation information for ‘father' topics and text information for sub-topics. The model parameters are estimated by a collapsed Gibbs sampling algorithm. We also propose a topic evolution algorithm that runs in two steps: topic segmentation and topic dependency relation calculation. We have tested the proposed citation-content-LDA model and topic evolution algorithm on two online datasets, IEEE Transactions on Pattern Analysis and Machine Intelligence(PAMI) and IEEE Computer Society(CS), to demonstrate that our algorithm effectively discovers important topics and reflects the topic evolution of important research themes. According to our evaluation metrics, citation-content-LDA outperforms both content-LDA and citation-LDA.

Probabilistic hypergraph based hash codes for social image search

With the rapid development of the Internet, recent years have seen the explosive growth of social media. This brings great challenges in performing efficient and accurate image retrieval on a large scale. Recent work shows that using hashing methods to embed high-dimensional image features and tag information into Hamming space provides a powerful way to index large collections of social images. By learning hash codes through a spectral graph partitioning algorithm, spectral hashing(SH) has shown promising performance among various hashing approaches. However, it is incomplete to model the relations among images only by pairwise simple graphs which ignore the relationship in a higher order. In this paper, we utilize a probabilistic hypergraph model to learn hash codes for social image retrieval. A probabilistic hypergraph model offers a higher order repre-sentation among social images by connecting more than two images in one hyperedge. Unlike a normal hypergraph model, a probabilistic hypergraph model considers not only the grouping information, but also the similarities between vertices in hy-peredges. Experiments on Flickr image datasets verify the performance of our proposed approach.

Large Time Behavior of Entropy Solutions to Some Hyperbolic System with Dissipative Structure

In this paper the large time behavior of the global L∞ entropy solutions to the hyperbolic system with dissipative structure is investigated. It is proved that as t →∞ the entropy solutions tend to a constant equilibrium state in L2 norm with exponential decay even when the initial values are arbitrarily large. As an illustration, a class of 2 × 2 system is studied.

Convex relaxation for a 3D spatiotemporal segmentation model using the primal-dual method

A method based on 3D videos is proposed for multi-target segmentation and tracking with a moving viewing system. A spatiotemporal energy functional is built up to perform motion segmentation and estimation simultaneously. To overcome the limitation of the local minimum problem with the level set method, a convex relaxation method is applied to the 3D spatiotemporal segmentation model. The relaxed convex model is independent of the initial condition. A primal-dual algorithm is used to improve computational efficiency. Several indoor experiments show the validity of the proposed method.