Blood flow is no longer measured at a single site but between an area and the LDI due to being non-contact cannot interfere with the final results. LDI is a 1mm laser beam that uses a mirror to scan in two dimensions. A small amount of light penetrates the skin; the depth depends on wavelength and absorption, of area scanned and interacts with cells and tissues. Speed and density of moving cells determine the signal sent to detector. Discovery Technology International defines the amount of tissue measured as:
we have estimated that for well-perfused tissue such as muscle, the mean sampling depth for our probes is in the region 0.5-1.0 mm with a concomitant sampling volume in the region 0.3-0.5 mm3. For cutaneous measurements, the sampling depth is likely to be in the range 1.0 -- 1.5 mm. These estimates have been obtained heuristically through many years of experience and are based on both in vitro observations and mathematical modeling of photon diffusion through 'imaginary tissues' using Monte-Carlo techniques.
Acetylcholine (Ach) and Sodium Nitroprusside (SNP)
Acetylcholine and also Sodium Nitroprusside injected in to the site to be scanned by the LDI caused vasodililation and allowed for scanning of the areas more accurately and studies prove the two injects are reproducible. One group of researchers used the following technique:
Drugs used: 2.5 ml of 1% acetylcholine chloride (Sigma Chemical Co., St. Louis, MO, U.S.A.) was introduced into the anodal chamber. 2.5 ml of 1% sodium nitroprusside (Sigma) was introduced into the cathodal chamber. The vehicle for both drugs was 0.5% sodium chloride solution. (Balmain et al., 2007)
Results
All statistical analyses were performed using SPSS 15.0 for Microsoft Windows. The data was checked for normality using the Shapiro-Wilk test, as the number of subjects was less than fifty. Those data that were not normally distributed were transformed and reassessed. A repeated measure ANOVA was used to determine significant differences between time points for repeated LDI, PWV and PWA measurements. A value of P (0.050 was used to define significance and a 95% confidence interval. The data presented in tables and graphs is displayed as mean ± SD (standard deviation), unless otherwise stated.
Outliers have been checked but not eliminated, although some of them are not within the range of 2 SD, they are not eliminated due to the consistency between all 4 visits as per subject.
Statistical results from studies
From Patti LDI ACH 2003 we can see the dramatic difference between using Area under Curve vs. Incremental Area under Curve. The values for AUC are almost always half the values gotten from the IAUC.
ACH AUC
SEM
Std Dev
0
6
12
18
ACH IAUC
SEM
Std Dev
0
6
12
79.54888
18
52.00063
ANOVA / Bland and Altman
The Bland & Altman plot (Bland & Altman, 1986 and 1999) is a statistical method to compare two measurements techniques. In this graphical method the differences (or alternatively the ratios) between the two techniques are plotted against the averages of the two techniques. Horizontal lines are drawn at the mean difference, and at the limits of agreement, which are defined as the mean difference plus and minus 1.96 times the standard deviation of the differences. ( as quoted from Medcalc.be, 2010) ANOVA, or analysis of variance, represents several statistical models and methods and it correlates all the different variables in to components. ANOVA determines whether or not the means of several groups are related and converts t-test two sample results into generalized groups. ANOVA can compare and analysis the data from data containing more than three means. An example from the Handbook of Biological Statistics in regards to using ANOVA; you could measure the amount of transcript of a particular gene for multiple samples taken from arm muscle, heart muscle, brain, liver, and lung. The transcript amount would be the measurement variable, and the tissue type would be the nominal variable.
Microbiologybytes.com (2010) breaks down the ANOVA process and terminology as:
ANOVA jargon:
Way = an independent variable, so a one-way ANOVA has one independent variable, two-way ANOVA has two independent variables, etc. Simple ANOVA tests the hypothesis that means from two or more samples are equal (drawn from populations with the same mean). Student's t-test is actually a particular application of one-way ANOVA (two groups compared).
Factor = a test or measurement. Single-factor ANOVA tests whether the means of the groups being compared are equal and returns a yes/no answer, two-factor ANOVA simultaneously tests two or more factors, e.g. tumour size after treatment with different drugs and/or radiotherapy (drug treatment is one factor and radiotherapy is another). So, "factor" and "way" are alternative terms for the same thing (inpependent variables).
Repeated measures: Used when members of a sample are measured under different conditions. As the sample is exposed to each condition, the measurement of the dependent variable is repeated. Using standard ANOVA is not appropriate because it fails to take into account correlation between the repeated measures, violating the assumption of independence. This approach...
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