代理发论文说内内发,还没有消息

代理发论文说内内发,还没有消息

学习外语， 投外国，一般3个月就出刊了。

亲，你遇到骗子了报警是没有用的如果2000元以上或许可以立案

最好不要找代理，因为太忙不能决定出刊时间，而且价格上会比较贵，还是找杂志社的靠谱。如果你有发表论文的录用通知，可以试着找找相关部门解决。

只有跟法院起诉。首先，买论文可能被认定该类交易本身不合法，所以是否支持你的诉求不确定，其次，如果合法，你们交易后论文没有成功发表，只能算他违约，违约属于民事，一般只能通过仲裁，诉讼解决。一点小思路，仅供参考

信息口还没有消息

楼主加油，明年再来

PATPAT，没接到之前都还是有希望的

我也是口的，没中。 昨天感觉很郁闷，今天就想：明年再来吧。

谢谢了啊!!

现在还没有消息，这是好消息还是坏消息？

有关系的都知道了，没关系的只能等

@10楼:院长威武啊 今年院长还没吭声，不知何故

今年保密纪律严一些，你们不敢公，但可以私下问问他
、

看来，今年是管的严，各学部能打听到结果的，是极少数。

发现没有 代谢组学 内容 ，整理转发一下

终极“组学”--代谢组学
Metabolomics--A New Exciting Field within the "omics" Sciences  


Metabolomics can be regarded as the end point of the “omics” cascade.
Metabolomics is an emerging field in ytical biochemistry and can be regarded as the end point of the "omics" cascade. Whereas genomics deals with the ysis of the complete genome in order to understand the function of single genes, the majoty of functional genomics studies are currently based on the ysis of gene expression (transcptomics) and comprehensive ptein ysis (pteomics). As we are amassing knowledge of the genome, the transcptome, and the pteome, we have largely forgotten the metabolome. Howr, changes in the metabolome are the ultimate answer of an organism to genetic alterations, disease, or envinmental influences. The metabolome is therefore most predictive of phenotype (Fiehn 2002; Weckwerth 2003). Consequently, the comprehensive and quantitative study of metabolites, or metabolomics, is a desirable tool for either diagnosing disease or studying the effects of toxicants on phenotype.

One of course wonders why metabolomics has lagged behind other "omics" technologies. Possibly this is becse the number of metabolites vaes dramatically based on how they are counted. Investigators also debate about what compounds are considered metabolites; for example, should vitamins or smaller peptides be included? According to a and widely used definition, a metabolite is any substance involved in metabolism either as a pduct of metabolism or necessary for metabolism. In any case 3,000 major metabolites seem a reasonable number. If we attempt a global and quantitative evaluation, the technology involved is dnting becse the physical pperties of the compounds are so divergent and they vary dramatically in concentration. Moreover, the metabolome is a dynamic system subjected to significant envinmental influences, for example, temporal or dietary.

It is difficult to envision a single platform being dloped in the near future that is able to yze quantitatively all metabolites simultaneously. Thus with all metabolites as our goal, the technological hurdle seems to be the limiting step. At the other extreme, metabolomics can be seen as metabolite pfiling or "just" ytical chemistry. So it is nothing new, simply multi-yte chemistry that biochemists have been doing for decades. Of course metabolomics is simultaneously both and neither of these. Although an "omics" or global view of metabolism is a goal, by no means is universal coverage of all metabolites required for tremendous biological insight. Also whether we work on complete coverage of a single metabolic pathway or on a global appach to examine multiple metabolites, such multi-yte ysis is by no means tvial. Nrtheless, succesul implementation of metabolomics requires ytical instrumentation that offers high thughput, resolution, repducibility, and sensitivity, and only an assembly of different ytical platforms will currently pvide maximum coverage of the metabolome. To date, metabolomics-type studies rely pmaly on nuclear magnetic resonance (NMR) or mass spectmetry coupled to chmatography.

Currently, two complementary appaches are used for metabolomic investigations. In one appach--metabolic pfiling--quantitative ytical methods are dloped for metabolites in a pathway or for a class of compounds. This appach pduces independent information that can be interpreted in terms of known biochemical pathways and physiological interactions. These data represent an independent legacy database since they are quantitative. The disadvantage is that the system is not a universal or "omics" appach. Howr, the tremendous advances in technology over the past years allow the constant expansion of the number of ytes quantified simultaneously. Technologically, we are at a point where it is often as to measure many compounds as to measure one. If we take one step further and assemble a suite of quantitative methods yzing key metabolites fm different biochemical pathways, we can tranorm metabolic pfiling into metabolomics.

The second appach is metabolic fingerpnting. In such metabolomic investigations, the intention is not to identify each observed compound but to compare patterns or fingerpnts of metabolites that change in response to disease or toxin exposure. Compason of fingerpnts, often NMR or mass spectra or chmatograms, is performed using statistical tools such as hierarchical cluster ysis or pncipal component ysis. If these types of yse results in sample segregation into unique metabolic clusters, further efforts can be made to elucidate the discminating compounds and subsequently to evaluate these monocytes as potential biomarkers. Being semiquantitative and simultaneously applicable to a wide range of metabolites--this is a true "omics" appach. Such methods are attractive, as they allow investigators to cast a wide net both generating and testing hypotheses. Howr, the nature of the data makes the observations instrument/platform dependent. The implementation of NMR-based metabolic fingerpnting has marked the beginning of a metabolomics appach as a tool in biochemistry and has pven to be a powerful technique (Nicholson et al. 2002). Howr, it will only detect high abundance metabolites. Complementary to NMR, mass spectmetry-based tools will pvide coverage for metabolic fingerpnting in a lower concentration range, and their use is increasing steadily (Plumb et al. 2003).

The combination of metabolic pfiling and fingerpnting will lead to the realization of metabolomics. In one appach, changes in fingerpnts correlating to metabolite pfiles will be linked to a physiological state, without exact knowledge of fingerpnt components. In another appach, discminating compounds identified in fingerpnts will become the focus for quantitative metabolite yses. Therefore, metabolomics will contbute to our biological understanding both in a mechanistic as well as a predictive manner. Howr, it could also assist us in impving human health and may be among the first of the "omics" technologies to reach the clinic. Thugh multiple metabolomics pjects, a powerful list of likely markers of vaations in health can evolve (Watkins and German 2002). Analyzing this set of biomarkers in a single high thughput assay will pvide the clinician with a powerful diagnostic tool.

In genomics and transcptomics we saw economies of scale as institutional support dloped generating infrastructure behind the technologies. Similar support will be necessary to advance metabolomics. For example, a centralized effort to pvide isotopic-labeled standards for a wide range of metabolites would tremendously accelerate work in metabolomics as would the dlopment of an integrated pathway map to aid in data interpretation. Such a map would intduce us also to the next ll of measung flux thugh pathways.

Although metabolomics is still in an early evolutionary stage, we can expect to see exciting new dlopments in the near future. As quantitative metabolomic databases evolve, we can integrate them with data sets fm the other "omics" technologies to enhance the data value and pvide greater biological insight than any one "omics" technique alone can offer.

Katja Dettmer
Bruce D. Hammock
Cancer Research Center
University of California, Davis
Davis, California
E-mail:

Katja Dettmer is a postdoctoral researcher in pfessor Bruce Hammock's laboratory at the University of California, Davis. She is conducting research in the field of metabolomics, focusing on the dlopment of mass spectmetry-based tools for metabolic fingerpnting in biofluids as well as metabolic pfiling methods.

Bruce Hammock is a Distinguished Pfessor of Entomology and a scientist in the Cancer Research Center at the University of California, Davis. He directs an ytical-metabolomics laboratory that pioneered the use of immunochemical diagnostics in the envinmental field. His research interests include dlopment of recombinant viral pesticides, mammalian xenobiotic metabolism, envinmental chemistry, and biosensor dlopment.

References

Fiehn O. 2002. Metabolomics--the link between genotypes and phenotypes. Plant Mol Biol 48:155-171.

Plumb RS, Stumpf CL, Granger JH, Cast-Perez J, Haselden JN, Dear GJ. 2003. Use of liquid chmatography/time-of-flight mass spectmetry and multivaate statistical ysis shows pmise for the detection of drug metabolites in biological fluids. Rapid Commun Mass Spectm 17:2632-2638.

Nicholson JK, Connelly J, Lindon JC, Holmes E. 2002. Metabonomics: a platform for studying drug toxicity and gene function. Nat Rev Drug Discov 1:153-161.

Watkins , German J B. 2002. Toward the implementation of metabolomic assessments of human health and nuttion. Curr Opin Biotechnol 13:512-516.

Weckwerth W. 2003. Metabolomics in systems biology. Annu Rev Plant Biol 54:669-689.

近年来有关代谢组学研究进展
The Populus Genome Science Plan
Panel on Metabolic Charactezation and Metabolomics
Panel Members: Tim Tschaplinski, Thomas Motz, Andrea Polle, Scott Harding,

Janice Cooke, and Reinhard Jetter

BACKGROUND AND SCOPE

The emerging science of metabolomics couples metabolite pfiling with the ysis of mutant and transgenic lines to elucidate ptein function, the structure of metabolic pathways, and offers tremendous potential to discover and assign function to novel genes.  Stated explicitly, metabolic pfiling is the unbiased, relative quantification of the bad array of cellular metabolites, and their fluxes.  As such, metabolic pfiling can pvide information on how gene function affects the complex biochemical network, and the lls of regulation of biochemical networks that are not raled by DNA micarray technology.  A comprehensive functional genomics research platform, that links metabolite pfiling to gene expression arrays and ptein pfiles, will facilitate the cataloguing of genes.  About 500-1000 metabolites may be expected to accumulate to detectable lls in a typical eukaryotic genome, which codes for >10,000 pteins.  Therefore, it is unlikely that a single gene knockout or up-regulation nt will often lead to direct relationships of a single gene completely regulating the pduction and accumulation of a single metabolite.  Some examples indicate that genetic mutations can lead to changes that are highly pleiotpic, depending on where the mutation is operating in the metabolic networks.  Howr, the ability to detect a wide array of metabolites (and their fluxes) will permit determination of how biochemical networks, with their distbuted contl (regulation), have been perturbed.  

Succesul deployment of metabolite pfiling requires the dlopment of rapid, reliable, and efficient assays for detecting phenotypes that are metabolic vaants within natural or mutated populations.  Assays need to be dloped which will allow the detection of as many metabolites as possible and preferably at high-thughput rates.  Although the desire is to have a single ysis that captures all metabolites in a short time, there is, as yet, no single “silver bullet” ysis that will be apppate for all metabolites with a high degree of sensitivity and resolution.  The varying chemical charactestics of the different classes of compounds will necessitate sral yses, but it is possible to standardize the use of a limited number of ptocols that rapidly captures the bulk of small molecules.  A number of ytical appaches are currently available that can image a large number of metabolites, but they need to address the pblems of co-eluting interference, and be able to accurately identify as many of the peaks as possible.  The current status of pmising ytical appaches and what is needed to forward these appaches will be the focus of this plan.  Included are sample preparation needs, descption of the advantages and disadvantages of a given ytical appach and how a combination of multiple appaches can circumvent limitations.  Overall, the current-best ptocols need to be modified for high thughput, while simultaneously dloping the next generation of high-thughput ptocols that are scalable and address difficult-to-measure metabolites.  Classes of challenging metabolites include intermediate-sized molecules (1000-2500 Da), and charged molecules, such as phosphoated compounds.  In addition to determination of the steady-state concentrations of large numbers of metabolites, in many cases, it is the flux of these metabolites that will pvide key insight in to which gene was perturbed.  The concentrations and fluxes of metabolites will need to be assessed in biochemical pathway inference models to pbe pathway linkages.  Recognizing that the greatest gains in functional genomics ysis will be dd fm the integration of the different data streams, tools and appaches that combine the different classes of genomic data that are available, including DNA sequence data, mRNA expression pfiles, ptein pfiles, and metabolite pfiles, need to be dloped.

3- Itemized list of goalsSCIENTIFIC OBJECTIVES

Sampling and Sample Preparation

Given that many compounds are unstable, have very high turnover rates, or exhibit diurnal vaation in concentration, etc., sampling is important.  Sample extraction por the chemical ysis must be adapted for each type of sample (e.g., extraction ptocols for leaf tissue fm Populus might be different fm extraction ptocols for dloping xylem tissue).  The metabolites represent many different classes of compounds, and therefore the chemical pperties of the metabolites are highly vaable.  Depending on the extraction ptocol, different classes of compounds show different extraction efficiency under specific conditions.  It is unlikely that a single extraction pcedure for plant tissues allows accurate quantification for all compounds, but the goal is to capture as many metabolites as possible.

Short-term goal:  To establish standardized extraction ptocols that are tailored to each tissue-type of Populus with high recovery and repducibility.  The extraction ptocols will include addition of internal standards representing all major classes of compounds (e.g. carbohydrates, organic acids, steids, amines, etc.) por to extraction to increase accuracy and precision of the ysis.

Long-term goal:  To establish methods for repducible fractionation of extracts using solid phase extraction (SPE) columns or other methods.  The goal of fractionation is to concentrate metabolites, permitting of the extract to be yzed by the gas chmatography (GC)- or liquid chmatography-mass spectmetry (LC-MS), and lessening the pbability of saturating columns or MS-detectors.

Devatization for GC-MS ysis

GC-MS ysis of extracts containing such vaed metabolites as organic acids, sugars, sugar alcohols, amino acids and steids is complicated.  Many of the metabolites are not volatile and must be devatized por ysis by GC.  Methoxymation in combination with tmethylsilylation (methoxy-TMS) is widely used as the main devatization ptocol.  By first ptecting the carbonyl gup(s), the coupled devatizations are efficient (than silylation alone) for low molecular weight organic acids, but the relatively low temperature of the ptocol may limit the devatization of the difficult to devatize metabolites, including secondary carbon compounds that are typical of Populus.  The organic acids that are most vulnerable to TMS devatization alone can also be captured in other separations and yses.  

Short-term goal:  Confirm the method(s) that minimizes sample preparatory time and maximizes spectral output (i.e., maximum metabolites observed) of the Populus species under investigation.  The ptocols must ensure the stability of the devatized compounds and repducibility of the data generated.  

Long-term goal:  Dlop standardized extraction and devatization ptocols that are suitable for complete tomation.  

Analytical Techniques for Steady-State Metabolite Analyses
High-thughput GC-MS

Compason of GC-MS techniques:  The “oldest” and best-established coupling of methods to MS for metabolite ysis is GC-MS.  The thermo-stable samples are vapozed and then ionized by either electn-impact (EI) or chemical-ionization (CI).  For metabolomics yses there are in practice two types of instruments used: 1) quadrupole (single stage MS and two stage MS/MS (ion trap)) and 2) time-of-flight (TOF) MS instruments.  Both instruments have advantages and disadvantages, e.g. the quadrupole instruments have large dynamic range, are bust and easy to use. GC-TOF instruments have the capability to perform rapid spectral acquisition (up to 4-500 spectra/s over the full mass range), which results in possibility to speed up the yses (high thughput), as narw, short GC columns are used. The deconvolution of overlapping peaks is also greatly impved becse of spectral continuity acss a peak (no skewing of different masses).  A disadvantage with GC-TOF instruments has been a reduced dynamic range (in practice) compared to quadrupole instruments, which can be a pblem when yzing complex samples with high vaation in concentrations of compounds.  GC-TOF MS is the appach of choice for high-thughput GC yses, but as instrument manufacturers pduce new quadrupole instruments with much higher scan rates, their ease-of-use may make them versatile.

1D vs 2D GCxGC-MS

Rapid advancement in two-dimensional (2D) gas chmatography (GCxGC) makes it a powerful tool when coupled with high-speed TOF-MS for the deconvolution of metabolites that co-elute in traditional one-dimensional (1D) GC-MS.  The GCxGC-TOF-MS appach couples columns of different polaty and operated at different temperatures to shift retention times of co-eluting metabolites.  The peak capacity is appx. equal to the pduct of the separation capacities of the individual columns.  The eluent fm the first column is pulsed into a second column, generating an array of high-speed secondary chmatograms that can be detected by the high speed, high capacity time-array detector of TOF-MS.  All of the eluent fm the first dimension is subjected to separation in the second dimension, not just a single congested area of the chmatogram.  The appach can be used for deconvolution to ensure the pper assignment of fragments to the metabolites being deconvolved, which can then be incorporated into the data extraction algothms of the 1D yses.  As a deconvolution tool, it can be applied with the intduction of each novel hetegeneous matx (e.g., a new poplar species/tissue).  Given that the appach requires the second column to function much faster than the first column, it is unlikely that GCxGC-TOF-MS will be deployed (in the near-term) as the standard data acquisition appach for high thughput pfiling, until detectors with n rapid acquisition rates become available.

代谢组学在毒理学中的角色[转帖]

以下是引用longer在2003-6-2 4:10:43的发言：
Contemporary issues in toxicology the le of metabonomics in toxicology and its evaluation by the COMET pject
John C. Lindon, , a, Jeremy K. Nicholsona, Elaine Holmesa, Henk Anttia, 1, Mary E. Bollarda, Hector Keuna, Olaf Beckonerta, Timothy M. Ebbelsa, Michael D. Reilyb, Donald Robertsonb, Gregory J. Stnsc, Peter Luked, Alan P. Bree, Glenn H. Cantorf, Roy H. Biblee, Urs Niederhserg, Hans Senng, Goetz Schlotterbeckg, Ulla G. Sidelmannh, S M. Lrsenh, Adenne Tymiaki, Bruce D. Ca, Lois Lehman-McKeemani, Jean-Mae Coletj, Ali Loukacij and Craig Thomasj, 2
a Biological Chemistry, Biomedical Sciences Division, Faculty of Medicine, Impeal College of Science, Technology and Medicine, Sir Alexander Fleming Building, South Kensington, London SW7 2AZ, UK
b Pfizer Global R&D, 2800 Plymouth Road, Ann Arbor, MI 48105, USA
c Pfizer Global R&D, La Jolla, CA, 92121, USA
d Pfizer Global R&D, Sandwich, Kent CT13 9NJ, UK
e The Pharmacia Corporation, 4901 Searle Parkway, Skokie, IL 60077, USA
f The Pharmacia Corporation, 301 Henetta St., Kalamazoo, MI 49007, USA
g Hoffmann-La Roche AG, Grenzacherstrasse, CH-4070, Basel, Switzerland
h Novo Nordisk, Novo Nordisk Park, 2760, Maaloev, Denmark
i Bstol-Myers-Squibb Company, PO Box 4000, Pnceton, NJ 08543, USA
j Lilly Research Laboratoes, Eli Lilly and Co., Lilly Dlopment Centre S.A., 1348, Mont-Saint-Guibert, Belgium
Received 19 September 2002;  accepted 9 December 2002. ; Available online 6 March 2003.

Abstract
The le that metabonomics has in the evaluation of xenobiotic toxicity studies is presented here together with a bef summary of published studies. To pvide a comprehensive assessment of this appach, the Consortium for Metabonomic Toxicology (COMET) has been formed between six pharmaceutical companies and Impeal College of Science, Technology and Medicine (IC), London, UK. The objective of this gup is to define methodologies and to apply metabonomic data generated using 1H NMR spectscopy of une and blood serum for preclinical toxicological screening of candidate drugs. This is being achid by generating databases of results for a wide range of model toxins which serve as the raw mateal for comr-based expert systems for toxicity prediction. The pject pgress on the generation of comprehensive metabonomic databases and multivaate statistical models for prediction of toxicity, initially for liver and kidney toxicity in the rat and mouse, is reported. Additionally, both the ytical and biological vaation which might ase thugh the use of metabonomics has been evaluated. An evaluation of intersite NMR ytical repducibility has raled a high degree of bustness. Second, a detailed compason has been made of the ability of the six companies to pvide consistent une and serum samples using a study of the toxicity of hydrazine at two doses in the male rat, this study showing a high degree of consistency between samples fm the vaous companies in terms of spectral patterns and biochemical composition. Differences between samples fm the vaous companies were small compared to the biochemical effects of the toxin. A metabonomic model has been constructed for une fm contl rats, enabling identification of outlier samples and the metabolic reasons for the deviation. Building on this success, and with the completion of studies on appximately 80 model toxins, first expert systems for prediction of liver and kidney toxicity have been generated.
Author Keywords: Metabonomics; Toxin; Pharmaceutical; NMR; Chemometcs; Expert system
  
Article Outline
  Intduction
  Metabonomics backgund
  NMR spectscopy of biofluids
  MAS NMR of tissues
  Chemometc ysis of metabolic NMR data
  Identification of novel biomarkers of toxicity
  Expert system dlopment
  The COMET pject
  Expemental methods used in COMET
  Animal expements and sample collection
  Sample preparation and NMR spectscopy
  Overview of results to date
  Ctical issues requing resolution
  A statistical model for contl rat and mouse une
  Intersite consistency of biofluid sample pvision
  Consistency of NMR measurement study
  Overview of toxicity results
  The future: a perspective on metabonomics
  References
  
Intduction
The importance of postgenomic technologies for impving the understanding of drug adverse effects has been highlighted recently (Aardema and MacGregor 2002 and Cockerell et al 2002) and these appaches have been recognized to include metabonomics. While there is a comprehensive literature on the use of metabonomics to investigate xenobiotic toxicity and this has been reviewed recently (Nicholson et al., 2002), a gous and comprehensive evaluation would be of considerable value. To this end, a consortium has been formed to investigate the utility of NMR-based metabonomic appaches to the toxicological assessment of drug candidates. The main aim of the consortium is to use 1H NMR spectscopy of biofluids (and, in selected cases, tissues), with the application of comr-based pattern recognition and expert system methods to classify the biofluids in terms of known pathological effects csed by administration of substances csing toxic effects. The pject is hosted at Impeal College of Science, Technology and Medicine (IC), University of London, UK, and involves funding by six pharmaceutical companies, namely, Bstol-Myers-Squibb, Eli Lilly and Co., Hoffman–La Roche, NovoNordisk, Pfizer Incorporated, and The Pharmacia Corporation.
The main objectives of the pject are (1) pvision of a detailed multivaate descption of normal physiological and biochemical vaation of metabolites in une, blood serum, and selected tissues, for pmaly selected male rat and mouse strains, based on 1H NMR spectra; (2) dlopment of a database of 1H NMR spectra fm animals dosed with model toxins, initially concentrating on liver and kidney effects; (3) dlopment of expert systems for the detection of the toxic effects of xenobiotics based on a chemometc ysis of their NMR-detected changes in biofluid metabolite pfiles; (4) identification of combination biomarkers of the vaous defined classes; (5) testing of the methods to assess the ability of metabonomics to distinguish between toxic and nontoxic ogues and to assess the specificity of the predictive expert systems. The classes of chemicals used and the types of toxicity investigated are as diverse as possible to assist the validation of NMR methods for use in early "bad" screening of candidates for toxicity.
In this concise review, the backgund to metabonomics is presented together with a survey of literature results where metabonomics has been used to pbe xenobiotic toxicity based on target organ, regions within target organs, and biochemical mechanisms of action. The methods used in COMET are befly summazed and results are used to exemplify the appach. Finally, a perspective on the future uses of metabonomics in drug safety assessment is given.
Metabonomics backgund
NMR spectscopy of biofluids
When toxins interact with cells and tissues they disturb the ratios, concentrations, and fluxes of endogenous biochemicals in key intermediary cellular metabolic pathways. Under mild toxic stress, cells attempt to maintain homeostasis and metabolic contl by varying the composition of the body fluids that either perfuse them or are secreted by them. In sre toxicity states, cell death leads to loss of organ function and marked biochemical changes occur in biofluids due to loss of whole body homeostasis and metabolite leakage fm damaged cells. Consequently, following either scenao there are charactestic organ-specific and mechanism-specific alterations in biofluid composition. Clearly, the detection of toxic lesions via biochemical effects is most difficult close to the toxic threshold, yet these are often the most important effects to define. Previously, the detection of novel biomarkers of toxic effect has mainly been serendipitous. Howr, it is now possible to use a combined NMR–expert system appach to systematically explore the relationships between biofluid composition and toxicity and to generate novel combination biomarkers of toxicity. The appach of charactezing the metabolic pfile of a specific tissue or biofluid has been termed "metabonomics" by ogy with genomics and pteomics. Metabonomics has been defined as the study of the time-related quantitative multivaate metabolic response to pathophysiological pcesses or genetic modification in cells, tissues, and whole organisms (Nicholson et al., 1999).
The succesul application of 1H NMR spectscopy of biofluids to study a vaety of metabolic diseases and toxic pcesses has now been well established and many novel metabolic markers of organ-specific toxicity have been discovered (Nicholson and Wilson, ). 1H NMR spectscopy is well suited to the study of toxic nts, as multicomponent yses on biological mateals can be made simultaneously, without bias imposed by expectations of the type of toxin-induced metabolic changes. This is particularly true for NMR spectra of une in situations where damage has occurred to the kidney or liver. Quantitative changes in NMR spectscopic metabolite patterns have also been shown to give information on the location and sty of toxic lesions, as well as insights into the underlying molecular mechanisms of toxicity. (Nicholson et al 1985 and Nicholson and Wilson ).
The first studies of using PR to classify biofluid samples used a scong system used to descbe the lls of 18 endogenous metabolites in une fm rats which either were in a contl gup or had received a specific organ toxin which affected the liver, the testes, the renal cortex, or the renal medulla (Gartland et al 1990 and Gartland et al 1991). This study showed that samples corresponding to different organ toxins mapped into distinctly different regions. Vaous refinements in the data ysis were investigated, including taking scored data at three time points after the toxin exposure for the nephtoxins only (this used only 16 metabolites, as tne and creatine were not altered in this data subset) as well as using a dual scong system (the time and magnitude of the greatest change fm contl). The maps dd fm the full time course information pvided the best discmination between toxin classes. This study was further extended (Anthony et al., 1994b) to incorporate actual metabolite NMR resonance intensities rather than scores. This was cared out for the nephtoxins in the earlier gup plus additional nephtoxic compounds. A good separation of renal medullary fm renal cortical toxins was achid. In addition, it was possible to differentiate cortical toxins according to the region of the pximal tubule which was affected and also by the biochemical mechanism of the toxic effect.
The time course of metabolic unary changes induced by two renal toxins was first investigated in detail by metabonomics using Fisher 344 rats administered a single acute dose of the renal cortical toxin mercuc chlode and the medullary toxin 2-bmoethanamine (Holmes et al., 1992). The rat une was collected for up to 9 days after dosing and was yzed using 1H NMR spectscopy. The onset, pgression, and recovery of the lesions were also followed using histopathology to pvide a definitive classification of the toxic state relating to each une sample. The concentrations of 20 endogenous unary metabolites were measured at eight time points after dosing and mapping methods were used to reduce the data dimensionality. These showed that the points on the plot can be related to the dlopment of, and recovery fm, the lesions.
A wide range of toxins has now been investigated using this metabonomics appach, including the kidney cortical toxins mercury chlode (Nicholson et al., 1985), p-aminophenol (Gartland et al and Sanins et al 1990b), uranyl nitrate (Anthony et al., 1994a), ifoamide (Foxall et al., 19), cephalodine (Anthony et al., 1992), the kidney medullary toxins ppylene imine and 2-bmoethanamine hydchlode (Holmes et al., 1992; Robertson et al., 2000), and the liver toxins hydrazine, allyl alcohol, thioacetamide, -naphthylisothiocyanate, and carbon tetrachlode Sanins et al 1990a and Sanins et al 1990b; Nicholls et al., 2001; Robertson et al., 2000). The testicular toxin cadmium chlode has also been investigated in detail (Nicholson et al, ), including the effects of chnic exposure at envinmentally realistic lls (Gffin et al., 2001). The incidence of phospholipidosis csed by amiodane, chloquine, DMP-777 (a neutphil elastase inhibitor), and a number of Glaxo Wellcome compounds has been evaluated using metabonomics (Espina et al 2001 and Nicholls et al 2000), as has the effect of dexamethasone on vascular lesions (Slim et al., 2002). Other studies include the toxicity of the aldose reductase inhibitor HOE-843 (Hoyle et al., 1992) and lanthanum nitrate (Feng et al., 2002). Toxic stress in earthworms has also been investigated using metabonomics (Bundy et al., 2001).
Extensions to the earlier chemometc appaches include a toxicological assessment appach based on neural network software to ascertain whether the methods pvide a bust appach which could lead to tomatic toxin classification (Anthony et al., 1995). The neural network appach to sample classification, based on 1H NMR spectra of une, was in general predictive of the sample class. It appears to be reasonably bust and once the network is trained, the prediction of new samples is rapid and tomatic. Howr, the pncipal disadvantage is common to all neural network studies in that it is difficult to ascertain fm the network which of the oginal sample descptors are responsible for the classification. Nrtheless as shown recently, pbabilistic neural networks appear to be a useful and effective method for sample classification (Holmes et al., 2001). The use of statistical batch pcessing has been explored as a means of charactezing the biochemical changes associated with hydrazine-induced toxicity (Antti et al., 2002). Recently, comprehensive studies have been published using pattern recognition to predict and classify drug toxicity effects, including lesions in the liver (Beckwith-Hall et al., 1998) and kidney (Holmes et al., 1998a), and using supervised methods as an appach to an expert system (Holmes et al 1998a; Holmes et al 1998b and Holmes et al 2000).

It appears that significant ytical advantages may be conferred by using 1H NMR spectscopy to follow the biochemical responses of animals or cells to foreign compounds. Currently, in order to evaluate the toxicity of a drug candidate by conventional toxicological pcedures the subjective selection of a range of biochemical methods is required. This is necessaly a complex and time-consuming pcess and if an inapppate range of biochemical methods or metabolic parameters are used important metabolic distces may be overlooked. The le of NMR spectscopy in ytical toxicology is thus essentially one of biochemical exploration, i.e., determining the range of biochemical perturbations csed by exposure to a toxin and whether these are biologically significant.

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准备复试不写论文直接和老师说论文常用来指进行各个学术领域的研究和描述学术研究成果的文章，简称之为论文。它既是探讨问题进行学术研究的一种手段，又是描述学术研究成果进行学术交流的一种工具。它包括学年论文、毕业论文、学位论文、科技论文、成果论文等。两种常见的论文①学位论文。学位申请者为申请学位而提出撰写的学术论文叫学位论文。这种论文是考核申请者能否被授予学位的重要条件。学位申请者如果能通过规定的课程考试，而论文的审查和答辩合格，那么就给予学位。如果说学位申请者的课程考试通过了，但论文在答辩时被评为不合格，那么就不会授予他学位。有资格申请学位并为申请学位所写的那篇毕业论文就称为学位论文，学士学位论文。学士学位论文既是学位论文又是毕业论文。②学术论文。中华人民共和国国家标准VDC 81、CB 7713-87号文件给学术论文的定义为：学术论文是某一学术课题在实验性、理论性或观测性上具有新的科学研究成果或创新见解的知识和科学记录；或是某种已知原理应用于实际中取得新进展的科学总结，用以提供学术会议上宣读、交流或讨论；或在学术刊物上发表；或作其他用途的书面文件。在社会科学领域，人们通常把表达科研成果的论文称为学术论文。

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