Patent Application
20080183101
The present invention relates to the spectroscopic analysis of saliva, in particular the multivariate analysis of salival spectra. Such analysis is useful for estimating the oral health of an individual or group of individuals or for characterising the effect of treatment products, such as toothpastes or mouth rinses, on the oral environment.
Humans and other animals are susceptible to a range of undesirable oral conditions, such as dental caries, gingivitis and bad breath. Many of these conditions are caused or mediated by bacteria or other micro-organisms within the oral cavity. A wide range of bacteria are normally present in the oral cavity, typically residing as a biofilm on the surfaces of the oral cavity, in particular on the teeth, gums and tongue. Some bacteria or micro-organisms are more harmful than others.
Typically, the undesirable oral conditions start as a low grade, barely detectable disorder which, if left untreated, progresses to a more serious condition. It can be difficult to detect such disorders in their early stages. Whilst doctors and dental professionals are trained in such detection a proper examination is time consuming. Furthermore, even for a trained professional, quantification of the degree of disorder is difficult and an element of subjectivity in the assessment can lead to poor reproducibility. It is particularly a problem for assessing the progression or remission of the disorder within an individual over time. As a consequence, when evaluating products for treating such disorders, reliable clinical trials typically require large base sizes and may need to be run for several months in order to be able to detect differences between products, even though such differences may be clinically important. Other factors affecting such evaluations include a high degree of variability between subjects, relative scarcity of individuals suitable for participating in trials and, whilst the trial is being run, deviation from the desired protocol by individual participants, such as omitting to use, or incorrectly using a treatment product. All of this makes clinical trials very expensive to run which in turn acts as a brake upon the development of improved treatment products.
Much effort has been put into improved methods for assessing oral health. A simple and well know example of assessing the state of the oral cavity is the use of a plaque disclosing table for dyeing, and thereby revealing the extent of, bacterial plaque on the teeth. Whilst the test is simple to perform it does not discriminate well between harmful bacteria and others and is not a reliable indicator of disease state.
It has long been recognised that bacterial metabolites can be implicated in oral diseases. For example, Singer and Bruckner reported, in Infection and Immunity, May 1981, pp. 458-463, the cytotoxic properties of butyrate and propionate, both of which are excreted by dental plaque bacteria. Singer also describes, in U.S. Pat. No. 5,376,532, the spectrophotometric analysis of betaglucoronidase levels in gingival crevicular fluid (GCF) as a means of detecting patients at risk of periodontal disease.
Russian patent no. 2 229 130, published 20 May 2004, uses similar findings as a basis for determining oral-cavity microflora disturbances by quantifying short-chain fatty acids (especially acetic, propionic and butyric) in saliva. The disclosed methods promise a more detailed analysis of the various bacterial species populations.
The use of salivary analysis also has a long history. EP 158 796 (Shah et al.) described the use of a colorimetric test for determining peroxidase in saliva samples as a means of detecting the presence of inflammation due to periodontal disease. More recently, JP 2002/181815 described the use of a strip coated with anti-human hemoglobin monoclonal antibody for detecting occult blood in human saliva as a screening test for periodontal disease. In the method described an individual provides a saliva sample by rinsing with a mouthwash and expectorating. The invention of WO 03/083472 also uses the saliva of a subject to assess the risk of periodontal disease, in this case by examining for the presence/absence of a particular protein by gel electrophoresis, and WO 2005/050204 diagnoses periodontal disease risk, using saliva as a specimen, by detecting lactoferrin polypeptide. Further, Denny et al., in US 2003/0040009, report the use of salivary analysis to predict disease risk, particularly dental caries risk, by quantifying the mucins in saliva.
1H and 13C NMR spectroscopy of human saliva has been reported by Silwood et al. in J. Dent. Res. 81(6):422-427, 2002. The authors report the identification of several biomolecules and a high degree of both inter- and intra-variability between subjects in the pattern of biomolecules. Concluding that ‘NMR spectroscopy serves as a powerful technique for the multicomponent analysis of human saliva’ the authors suggest that the technique may be used for tracking the effects of oral health care products on patients with periodontal diseases.
The foregoing disclosures primarily relate to the analysis of specific chemicals in saliva. A technique using small molecule profiles obtained through a variety of analyses, including spectral and chromatographic analysis, is described as ‘metabolomics’ by the authors of WO 01/78652. Here the emphasis is on use of the whole profile, rather than of individual chemical signals, for diagnosing and predicting disease states, predicting an individual' response to a therapeutic agent and for monitoring the effectiveness of a therapeutic agent in clinical trials.
In the past several years the use of ‘metabonomics’, a technique involving multivariate analysis of spectral data, has also received much attention for assessing disease states, notably from Nicholson and co-workers. For example, WO 02/086478 provides a detailed disclosure of spectral analysis, in particular principal components analysis of 1H NMR spectra, and its use as a diagnostic technique. The publication discloses a long list of disorders to which the technique might be applied, including dental disorders, such as dental caries, gum disease, and gingivitis. The publication further discloses many fluid sample types to which the technique can be applied, including saliva.
WO 03/107270 builds on the metabonomics approach for the metabolic phenotyping of subjects: This patent application describes the application of metabonomics for, inter alia, predicting responses to dosing, selecting a phenotypically homogeneous set of subjects and for facilitating the identification of biomarkers. WO 2004/038602 further describes generalised techniques for data mining in relation to metabonomics data sets. US 2007/0043518 (Nicholson et al.) expands upon the statistical analyses that can be performed upon metabonomic data sets and their use for identifying components of complex systems, such as identifying biomarkers in biological fluids.
Despite the foregoing there remains the need for further improvement in the management of clinical trials, for the development of improved treatment products, particularly for oral care, and for a more structured approach to characterising the effect of treatment products upon the oral environment.
The present invention relates to methods of analysing saliva samples, in particular by using spectroscopic, metabonomic analysis of saliva to get a complete picture of an individual's oral biochemistry. For convenience, the methodology will also be referred to herein as ‘Salivary Metabonomics’. The taking of saliva samples is non-invasive and can be done by an individual at home at a convenient time. The samples are easily stabilised and transported and the spectroscopic technique is capable of producing a large amount of data in a form which is amenable to productive further analysis. Without needing to identify particular compounds the technique is able, for example, to differentiate individuals and to track their responses to treatments. Further, by correlating such analysis to a physician's assessment of the oral health of the same individuals a model can be constructed which can be used to obtain an oral health measure for further individuals. The analyses can be conducted with high throughput and low cost. For example, the analysis enables the management of a clinical trial by screening potential participants and tracking, on a daily basis, actual participants. Used as a screening step to identify potential participants the method enables the selection of a more homogeneous group of relevant participants, or selection of individuals with the most consistent day-to-day saliva composition, thereby improving the power of the trial to detect differences between treatment products. Alternatively or additionally, used as a monitoring step during the trial the method enables a more convenient or more sensitive and objective evaluation of product effects as well as detecting whether trial participants are failing to adhere to the prescribed trial protocol. The ability to provide an oral health measure for a particular individual also makes it possible for the technique to be used as a diagnostic aid. Furthermore, the wealth of data provided can, through multivariate analyses such as principle components analysis, be summarised across individuals to provide a product measure which can provide insight into the mechanism of action of treatment products.
The methods herein can be used e.g.
Details about specific changes in salivary metabolites can be provided e.g. propionic acid, butyric acid, or trimethylamine, which are key metabolites which can be used to compare product efficacies.
The saliva analyses used herein, which can be described as ‘salivary metabonomics’, can also be used to understand consumer perception. For example, some consumers experience “morning mouth”, an unpleasant range of tastes and textures upon wake-up. Metabonomic assessment of these subjects will determine whether their perceptions have a real biochemical basis, or exist simply in their minds. In turn, this learning can be used to develop better products (e.g. utilising actives to target the biochemical basis of the consumer perception, where found).
Unless specified otherwise, all percentages and ratios herein are by weight of the total composition and all measurements are made at 25° C.
As used herein ‘physician’ means any trained professional who is qualified to assess oral health, such as a doctor, a dentist or a dental clinician.
As used herein, oral health measures can be used to estimate diseases or conditions directly affecting the oral cavity such as a plaque, calculus, gingivitis, periodontitis or lingual furring or bad breath or they can be indirect measures of diseases or conditions which primarily affect another part of the body but are nevertheless reflected in some change in oral chemistry, such as a gastric disease or diabetes. In the case of indirect measures the reference model against which the saliva samples are evaluated may be constructed by correlating chemical or biochemical analyses of members of a reference population to reference spectra derived from saliva samples from the reference population members.
In a preferred embodiment herein the invention relates to computing a proxy oral health measure for an individual comprising the steps of:
By a “direct” oral health measure is meant an observation that is generally accepted as being capable of supporting diagnosis of an underlying oral health condition (such as gingivitis or caries). By a “proxy” oral health measure is meant an observation that is not necessarily diagnostic of the condition but is associated with it and can be used in place of the direct measure, albeit with acceptance of a greater degree of error in a resulting diagnosis. Saliva samples can be easily generated by individuals themselves, in the comfort and privacy of their own homes, thus avoiding the need to visit a clinician. Saliva samples can be frozen for storage and, with suitable stabilisation may be delivered by post or courier to a central facility for analysis. As a result, the proxy measure may be easier or less costly to derive than a direct measure and/or may be more readily repeated over several days to improve confidence in the measure. The methods herein can provide a basis for a personalised health assessment. The direct oral health measures herein are preferably selected from: a physician's quantitative assessment of oral health; gingival images; dental images; and machine readings or expert assessment of breath malodour; in each case for each of the members of the reference population. A preferred method of collecting gingival image data, based upon analysis of the gingival margin is disclosed in U.S. application Ser. No. 11/880,908 (Gerlach et al.) and the equivalent PCT application IB2007/052965. Similar imaging methods can be used for the teeth. US 2007/0092061 discloses an image capture device, system and method for use in capturing digital, dental images and WO 97/06505 discloses a caries detection system based upon digital x-ray images. All of these measures can be reduced to digital form for further analysis on a computer, particularly a multivariate analysis.
In another preferred embodiment the invention relates a method of characterising a treatment product comprising the steps of:
As used herein, the term “spectrum” refers to a set of linked data obtained by a machine measurement upon a single sample and capable of being captured in digital form as an array of data. The plural “spectra” refers to two or more sets of such data. The terms encompass, in addition to nuclear magnetic resonance, infra-red, ultra-violet and mass (NMR, IR, UV and MS) spectra, chromatograms such as those obtained by liquid or gas chromatography or capillary zone electrophoresis. Preferred are NMR spectra and, in particular, 1H NMR spectra. The methods herein further include running clinical studies with sets of individuals and determining salivary metabolite levels from samples of the individuals' saliva via spectra obtained from the saliva samples. An advantage of the ‘metabonomics’ methods herein is that, though it is possible to identify and measure particular metabolites, an overall picture of the sample can be obtained by analysing data from the spectra without identifying particular metabolites. Indeed better measures can be obtained by using substantially the whole of, or a large proportion of, the information from the spectra. By correlating the spectral data for the saliva of individuals to a physician's quantitative assessment of the oral health, selected aspects thereof, or other direct oral health measures for the same individuals, reference models can be constructed against which further saliva spectra can be compared to derive proxy oral health measures. The physician's quantitative assessments of the individuals can include one or more indices selected from a plaque index, a calculus index, a gingival index, a periodontal index and a lingual furring index. Even without the correlation to the physician's oral health assessments or other direct oral health measures, analysis of the spectra can reveal important information relating to e.g., the effect of treatment products on the oral environment which is typically replete with a complex variety of bacteria and other organisms and their associated metabolites.
Steps in the taking and analysing of saliva samples and of deriving proxy oral health measures, which can be used for estimating a subject's susceptibility to, or degree of, oral disease typically include the following, though it will be appreciated that many variations are possible.
As mentioned above, the oral health measure and histories derived from spectral measures of saliva samples can be use to improve running and management of clinical studies. For example, subjects can be selected for a clinical trial based upon the day-to-day consistency of their saliva composition. By choosing subjects with lower day to day variation in saliva composition, that is, by identifying a subset of the subjects with lower day-to-day variation in saliva composition than the average day-to-day variation in saliva composition taken across the set of subjects as a whole, the power of a clinical trial to differentiate between different product treatments can be increased.
Alternate criteria for selecting subjects from amongst a set of candidate subjects can be:
As well as selecting subjects for a clinical trial, the oral health measures or other salival spectra derived measures described above can be useful in trials comprising two or more legs, in that subjects within each leg can be chosen in order to balance the oral health measures or metabolite levels of subjects across each of the legs.
A particular advantage of the methodologies herein is that by examining the oral health histories of subjects on the trial, which can be done on a daily basis, indications of non-compliance with the clinical trial protocol, such as using a non-prescribed treatment product or missing a treatment, can be detected. An objective decision can then be taken as to whether to exclude a subject from the trial for non-compliance, thus helping to produce a more valid or more powerful trial.
A particular advantage of the methods herein is that the saliva samples can be taken by subjects themselves at home and delivered to a central collection point relatively quickly and easily. The subsequent analysis of the saliva samples can be done in a high throughput manner at relatively low cost. One aspect of the invention herein therefore is a method of managing a clinical trial comprising the steps of:
Beyond the uses for improving the management of clinical trials, the methods described herein can be used to improve the management of an individual's health. For example an individual could take a sample of saliva as described herein and have it sent to a laboratory for spectral analysis as herein described to generate an oral health measure or oral health history. The oral health measure or history could then, for example, be provided to the individual's physician as an aid to diagnosis of oral health or other disease state reflected in a change in oral chemistry. The information might for example, be used to assist in the prescription of a treatment product for the individual by examining the individual's oral health measure or history as provided herein. The methodology could also be used in a follow up manner by e.g. treating the individual with a treatment product and assessing the individual's oral health history before and after treatment with the product.
The methods herein are certainly useful for measuring the efficacy or mechanism of action of treatment products and therefore have value in product development. Such measurement can include computing a product efficacy measure for the product from the oral health histories of subjects taking part in a clinical trial, or computing a product efficacy measure from product induced compositional changes in the saliva as determined from the saliva spectra, for a set of subjects taking part in a trial. The measurement may include comparing a test product to a reference product. Product efficacy measures thus obtained could of course be useful for generating advertising indicia for a product by associating the product efficacy measure with the product. Such indicia may include differentiating the mode of action of a product from that of a reference product by showing different product-induced compositional shifts in saliva between the tested product and the reference product.
Salivary Metabonomics (SM) employing 1H NMR was used to investigate the Mode of Action (MoA) of two test toothpastes, A and B, relative to a standard, commercial product, C. Product A included triclosan as an antimicrobial agent and Product B included an antimicrobial system comprising both zinc and stannous salts. Product C did not contain an antimicrobial agent. A group of 30 panellists was selected and instructed to use Product C twice a day for a ‘wash out’ period of four weeks. Over the last two weeks of the wash out period (reference phase) the panellists submitted up to 10 lavage saliva samples each, all taken on wake-up on different days. On each sampling day the panellists used a pipette to pour 2 ml of tap water into their mouth; they rinsed for 30 seconds and then expectorated into a fresh centrifuge tube. The tubes contained 1 ml of 0.9% w/v NaF as a preservative and once filled were stored below 0° C. until submission for analysis.
After the reference phase the group was divided into three legs, individuals being balanced across the legs according to the average % propionic acid found in reference phase saliva (determined from the reference phase NMR spectra). One leg was issued with a new tube of Product C as a placebo, a second leg was issued with Product A and the third received Product B. The panellists used their new products for three weeks (intervention phase) and then for a further two weeks (recovery phase) reverted to the Product C used during the wash-out (baseline) period. During these five weeks the panellists continued to provide up to 5 samples a week. Each leg comprised 8-9 panellists and whilst the link between the panellists and the legs was known throughout, during the data acquisition and processing phase the link between product leg and product was not known.
Submitted saliva samples were logged, labelled with a unique identifier and stored in a freezer. When the samples were prepared for analysis they were taken out of the freezer in approximately the order in which they were submitted (independent of leg) and allowed to thaw for 2 hours. When fully melted, the sample volume was recorded and the samples centrifuged for 10 minutes at 8000 rpm and 20° C. The supernatant was then decanted and stored in a new vial labelled with the same identifier.
The NMR sample was prepared by adding 800 μl of the sample and 80 μl of a buffer solution which contained pyridazine as a reference to a new 18 cm long, 5 mm diameter NMR tube. The sample tube was labelled with the same identifier and submitted for 1H NMR analysis on a 400 MHz Bruker spectrometer. Samples were racked in a 120 place autosampler, in the order in which they were submitted and were run overnight or over a weekend. Typically 30 would be run per night, with about 40 minutes allowed for each loading, locking, shimming and acquisition cycle. Before running the first sample, the machine was calibrated and a standard shim setting selected. The pyridazine triplet at 9.2 was used to assess the quality of the acquisition and, if necessary, sample acquisitions were repeated at the end of the run and the old spectrum file over-written. The spectra obtained were acquired using water suppression.
NMR pre-processing was carried out using Bruker's XWIN-NMR™ software, all samples in a batch were referenced roughly to the acetate peak at 1.95 ppm. Each spectrum then had the same spectral processing macro applied to it (Scheme 1.1).
The macro (the commands of which will be understood by users of the software) performs line broadening and a Fourier transform on the spectrum, takes the magnitude of the first derivative of the spectrum and then performs a spectrum base line correction. It has been found that by taking the derivative of the spectra, overall processing speeds are significantly improved which helps in handling large numbers of samples. The technique reduces the likelihood of finding a statistical break based upon broad signals but gives better resolution for small, sharp peaks. It will be understood that as a result it reduces the validity of comparing one peak with another in a spectrum but it is possible to compare the same peak across several spectra.
The processed spectra were then exported to Bruker's AMIX program where they were referenced more accurately to the acetate peak at 1.95 ppm and then binned using the parameters listed in Scheme 1.2.
The bin file was then exported and the bin lists were linked to the data recorded about the particular sample and the person who submitted it. All the samples from the entire trial were binned in the same operation.
Data analysis started by normalising the area under the curve between 3.1 and 0.7 ppm to 100 and the bins from 1.995 to 1.905 (attributed to the CH2 protons in acetate) were then removed to prevent any variations in acetate levels dominating the model. Bins within some ranges were combined to prevent peak shift reducing the power of the models formed. The particular regions are listed in Scheme 1.3. All samples from the same product leg were given an integer identifier in the sample information. All samples were given a second identifier (a phase identifier) which for reference phase samples was equal to the first integer less 0.1, for intervention samples was equal to the first integer and for recovery samples it was the original integer plus 0.1.
All spectra submitted by an individual subject had the average of reference phase spectra for that person deducted from each of their samples, i.e. the spectra were reference phase standardized on a person by person basis, thus presenting only the change which had occurred for each person since the start of intervention. It has been found that this reduces noise in the data and improves the models formed.
Principal components analysis (PCA) was then run on all of the NMR data to find outliers (centred scaling applied to all bins). Samples which were significantly over 3 standard deviations in the DModX or were abnormally high on the Hotelling's T2 were removed as were those with levels of ethanol (shown by the methyl group at 1.2 ppm) significantly above reference phase levels (see
The result of the PCA analysis can be shown as a distribution along the first two principle components (first shown on the horizontal axis and second on the vertical) as shown in
Once the data had been pruned for outliers each of the product legs (A, B, and C) was analysed separately to identify a ‘Mode of Action’ vector which distinguished the reference phase spectra from the intervention phase spectra. This was done by removing all of the recovery phase data and setting each product leg as a different class. An Orthogonal Partial Least Squares (O-PLS) analysis was then run for all classes using the difference of 0.1 in the phase identifier as the Y variable.
The Mode of Action vector for each product was taken as the loadings of the O-PLS first component. This was used qualitatively to determine what metabolites were increased or reduced by the intervention of each product. In the case of Product C, it was found that lactate levels tended to increase whilst propionate and butyrate levels tended to decrease. Product B was found to increase lactate and succinate but reductions in propionic or butyric were not significant. Product A showed little of significance; though lactate appeared to increase, the error was large and the change was not statistically significant.
Building upon the work from Example 1, to determine the Velocity of Action (VoA) of a product the scores plot from the O-PLS was used. Data were batched by week for each phase (reference, intervention and recovery) and a box plot drawn for each batch in order on the same axes. Recovery phase data were obtained by projecting recovery samples into the model built in order to see the return to reference phase levels from the end of intervention. The plots for Product C and a further test product are shown in
Such plots could be used to support e.g., comparative advertising but can also be used to design better studies where panellists are re-used (e.g. in a crossover study) so that a sufficiently long wash-out period is allowed between treatments.
In order to link salivary metabonomics to clinical effects a number of the panellists on a trial were graded for signs of gingivitis, periodontitis and other symptoms (see Scheme 3.1 below). The result was a series of indices and one overall health score calculated in accordance with Scheme 3.1.
The overall health score was correlated to bacterial metabolites as follows. Pre-processing and removal of outliers was carried out as in Example 1 but in this case only those samples which had been received in the same week as gradings were performed were taken. Each patient's samples for the grading week had the same clinical information attached and this was used as the set of y variables. Models were built to link metabolite levels to particular indices or to overall health. It was found that a correlation could be made to total health.
In order to correctly validate a model of this kind it is necessary to perform a similar prediction routine to that described in Example 1. Individuals are randomly assigned to one of three classes. In turn, the data from each one of the classes are set aside as a prediction set and a model is built from the remaining two classes. The prediction set is then placed into the model and, for these data points, an observed vs. predicted overall health scores plot drawn, as shown in
In order to convert Mode of Action (MoA), as discussed in Example 1, to Extent of Action (EoA) it was necessary to scale the MoA vectors to represent the magnitude of the change that had occurred. The loadings chart from the O-PLS model is produced as a particular type of unit vector known as an eigenvector. The corresponding eigenvalue of the eigenvector describes the magnitude of the vector or transformation. By multiplying each eigenvector by the corresponding eigenvalue from the model it is possible to scale them comparatively.
The eigenvalue from an O-PLS model is dependent on e.g., the separation displayed by the data, the dispersion of the points in each group being separated and the number of points in each group. It is also dependent on the number of components fitted to the data and this can vary greatly. As an O-PLS model is formed, successive additional components remove data not deemed to be explanatory and the amount of information on which the model is built decreases. Typically though, with each additional component the proportion of data that is removed decreases. The eigenvalue decreases with additional components but the differences between successive eigenvalues become progressively smaller. A dataset deriving from an underlying complex behaviour, but with little noise, may deliver a strong model including many components, each justified for inclusion but with decreasing additional explanatory value. Conversely, a dataset reflecting a lot of random noise may deliver a weak model having few components since the first few components remove a lot of data and successive components appear to make little improvement to the model. This has the effect that some of the weakest models appear to be the strongest, i.e. include fewer components, if the software is allowed to run unchecked.
For the methods herein the O-PLS models would generally be run until the difference between eigenvaluen and eigenvaluen+1 was less than 0.1 (typical scale running from around 100 to 2) to ensure a stable eigenvalue. A result of this requirement is that a many components are fitted but the later ones are progressively less and less of the model. The important aspect though is not what has been removed but what has been kept. The information kept is only that which correlates to a difference between the reference and intervention phases. Three different approaches to the analysis were tried out:
Once the models above had been built and scaled they were used as inputs into a PCA plot in five dimensions. The health correlation was scaled according to the average size of the other eigenvalues and was inserted in the positive (poor health) and negative (good health) form. The co-ordinates of the scores plot were taken and projected onto the health line so that each person or product had a score to show the amount of improvement, or deterioration, in overall oral health when moving from the reference phase to the intervention phase. The averages of these values by product, with 95% confidence intervals, are shown in
It was also found that grouping people together at all (approaches 1 and 2) was undesirable as it assumed all people would behave in a similar way. Even when the reference phase standardisation is applied there is still a great difference in the effect experienced during intervention, perhaps from the different extents to which the panellists brush or conduct themselves in the intervention period. Best results were obtained when individual models were formed for each person and compared; this delivered the best statistical analysis and allowed t-tests of the groups to identify when a difference was statistically significant. In this example all the reference phase samples and all the intervention phase samples were included within the model with equal weights, with no differentiation applied as to when an intervention phase sample was taken. The net change is therefore a composite of the changes taking place throughout the whole of the three week intervention period. A more targeted estimate of the changes taking place after about three weeks product usage could be obtained by only including the third week's samples in the analysis. Of course an intervention period could be even longer, such as from 4 to 12 weeks, with sampling at the end of the intervention period.
Each point on the plot of
Though the product usage in the foregoing examples involved systematic use of one product at a time only, the methodology also permits following a system of products involving flossing, brushes, mouthwashes and pastes and comparison between different systems of the same products using this method.
The dimensions and values disclosed herein are not to be understood as being strictly limited to the exact numerical values recited. Instead, unless otherwise specified, each such dimension is intended to mean both the recited value and a functionally equivalent range surrounding that value. For example, a dimension disclosed as “40 mm” is intended to mean “about 40 mm”.
All documents cited in the Detailed Description of the Invention are, in relevant part, incorporated herein by reference; the citation of any document is not to be construed as an admission that it is prior art with respect to the present invention. To the extent that any meaning or definition of a term in this document conflicts with any meaning or definition of the same term in a document incorporated by reference, the meaning or definition assigned to that term in this document shall govern.
While particular embodiments of the present invention have been illustrated and described, it would be obvious to those skilled in the art that various other changes and modifications can be made without departing from the spirit and scope of the invention. It is therefore intended to cover in the appended claims all such changes and modifications that are within the scope of this invention.
This application claims the benefit of U.S. Provisional Application No. 60/838,221, filed Aug. 17, 2006.
| Number | Date | Country | |
|---|---|---|---|
| 60838221 | Aug 2006 | US |
