The effect of light source on monomer conversion of dental adhesives

The most recent paper by Santini Miletic research group will be published in Journal of Adhesive Dentistry, hopefully in the next issue. The abstract is available on PubMed/MEDLINE.

J Adhes Dent. 2009 Nov 27. doi: 10.3290/j.jad.a17855. [Epub ahead of print]

Micro-Raman Assessment of the Ratio of Carbon-Carbon Double Bonds of Two Adhesive Systems Cured with LED or Halogen Light-curing Units.

Miletic V, Santini A.

Purpose: The purpose of the study was to compare the ratio of carbon-carbon double bonds (RDB) of two adhesive systems cured by five different light-curing units (LCUs) using micro-Raman spectroscopy.
Materials and Methods: Ten samples of an etch-and-rinse (Excite), a two-step self-etching adhesive system (AdheSE) - ie, primer and bond mixed - and AdheSE Bond only were prepared and cured with one of the following LEDs: Elipar Freelight2; Bluephase; SmartLite; Coltolux, each for 10 s; or a conventional halogen Prismetics Lite for 10 s or 20 s. Micro-Raman spectra were obtained from uncured and cured samples of all three groups to calculate the RDB. Data were statistically analyzed using ANOVA.  
Results: The mean RDB values were 62% to 76% (Excite), 36% to 50% (AdheSE Primer+Bond) and 58% to 63% (AdheSE Bond). At 20 s, Prismetics Lite produced significantly higher RDB in Excite than the other LCUs and Prismetics Lite at 10 s (p < 0.05). Prismetics Lite at 20 s and Elipar produced comparable RDB values of AdheSE Bond and AdheSE Primer+Bond (p > 0.05). Excite showed significantly higher RDB values than AdheSE (p < 0.05) whilst AdheSE Bond showed significantly higher RDB than AdheSE Primer+Bond (p < 0.05).  
Conclusion: The etch-and-rinse adhesive cured with the halogen LCU for 20 s gave higher conversion than LED LCUs or halogen for 10 s curing time. The highest intensity LED [Elipar] produced higher or comparable conversion compared to the lower intensity LED LCUs for the same curing time. The etch-and-rinse adhesive showed higher RDB than the self-etching adhesive system. The presence of the primer in the self-etching adhesive compromised polymerisation.

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News from jobs.ac.uk

PhD Studentship in Biophysical Chemistry

University of Bristol - Department of Oral & Dental Science 

Deadline: February 25, 2010

Salary: around £13,000  plus tuition fees paid.

NB: Applicants should be from the UK or EU. They are unable to support applications from outside the EU.

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Statistics in dental materials research: 2 things to plan ahead

A couple of statistical issues should be considered when designing a study in dental materials science.

(1) What statistical test(s) will be used to test the null hypothesis/hypotheses?

It is recommended to design the experiment in such a way that it is possible to test the data using one “global” test such as analysis of variance (ANOVA). The effect of one independent factor on one response variable is tested using one-way ANOVA in many papers on dental materials. Such study design includes e.g. the comparison of the degree of conversion (DC) of several materials cured under the same curing conditions (light, intensity, time, distance). The null hypothesis would be that there is no difference between the means for different materials. So, the independent factor is ‘material’ and the response variable is ‘DC’.

Two-way ANOVA is used to test the effect of two independent factors, e.g. material and light-curing unit (e.g. 3 materials are cured with either a halogen or an LED light-curing unit). Testing for interaction between the two factors shows whether or not the differences caused by one factor are consistent on different levels of the other factor. If so, the interaction is not significant (e.g. the DC may be higher in each material when cured with a halogen unit than an LED unit). Alternatively, if these differences are not consistent, then the interaction is significant (the DC may be higher in some materials when cured with a halogen and in others when cured with an LED unit). In this case, a series of one-way ANOVA must be used to examine this interaction more closely. This will, however, result in multiple testing which by default increases the chance of making the Type I Error (rejecting the null hypothesis when it is true) and some sort of correction is necessary to keep the overall significance level at the usual alpha=0.05. This correction most often means a decrease in the individual alpha value which also reduces the power of the statistical test.

Three-way ANOVA is sometimes used in dental materials science to study the effect of three independent factors on a particular response variable (e.g. the effect of material, light-curing unit and curing time on the DC of resin-based composites). Researchers are often tempted to test more and more factors in order to make their experiments robust. However, one has to keep in mind that the interpretation of three-way ANOVA is more difficult that that of two-way ANOVA and the post-test corrections may significantly reduce the power of the test. These are by no means the only tests used and are only an indication of the type of studies carried out in dental materials science.

(2) Power and sample size – power is the probability of not making Type II Error (failing to reject the null hypothesis when is false) i.e. power is the probability of correctly rejecting the null hypothesis when the difference between the groups truely exists. In sample size calculation prior to an experiment, the power of 80% is generally used as the cut-off point. So, when calculating the number of samples for each group, we need to know the following: number of levels (groups) that we will be comparing; significance level; power; estimated standard deviation (determined in a pilot study or taken from the literature) and the difference between the groups that we consider clinically relevant and don’t want to miss in our statistical testing. This last one may be tricky, because we often don’t know what difference between the groups is clinically relevant in a way that might affect the clinical performance of the tested materials. In this case, we may base our decision on the literature data or we can do a pilot study to find out the likely difference in the response variable between our groups which we would then use in the sample size calculations. Alternatively, if we already have a pre-determined number of samples in each group, we may be able to determine the power of our statistical test (i.e. how certain we are that our conclusion is correct).

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Free live dental webcast/webinar @ 3M ESPE Espertise

Stress Free Predictable Restorations
Presenter: Dr. George Warga, D.D.S.

Wednesday, February 17th, 2010
Time: 8:00 ET (1 am GMT on Feb. 18)
CE Credits: 1

About 180 seats still available.

From 3M ESPE Espertise (http://www.espertiseinteractivelearning.com/):
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You have to create an account on 3M ESPE Espertise Interactive Learning site in order to claim a seat.

If you're unable to join live due to the time difference in your part of the world, this webcast will be available as one of On-Demand Classes. Unfortunately, there will be no possibility to interact with the presenter and ask questions.

Also, there are two more free webcasts on caries risk assessment. Click here for more information.

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Poll: What kind of dental information are you searching on the internet?

With the rapid expansion of the internet, there are all sorts of information available.

What is it that you are looking for most of the time?

Please, take a moment to vote and select one or more answers.



Thank you for voting :-)
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News from jobs.ac.uk

Postdoctoral Research Assistant

Centre for Oral Growth & Development
Barts and The London - Institute of Dentistry

Salary: £30,229 to £35,532

Application deadline: 26-February-2010.

Click here for more information.

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Statistics in dental research: A book review

In a addition to the previous post on statistics in dental research, I'd like to mention that Medical Statistics at a Glance is the best book on the subject I've seen. It contains all the basic things a dental materials scientist needs to know, from study designs, types of data and descriptive statistics to hypothesis testing, correlation and regression, survival analysis and Bayesian methods. The book is written in an exceptionally succinct and reader-friendly way, understandable to researchers with very little previous knowledge on statistics.

Theory is only given in the amount which is necessary to understand each concept. A very good feature of the book is that it explains most commonly used statistical tests in dental research: t-tests, analysis of variance (ANOVA), the non-parametric Mann-Whitney and Kruskal-Wallis test, chi-squared and McNemar's test. The assumptions for these tests are given but situations with departures from these assumptions are mentioned in terms of their effect and possible solutions.

Also, statistics for some more complex study designs is also presented, such as generalized linear models, multiple linear regression or methods for clustered data.

Medical Statistics at a Glance also serves as a fantastic reminder with an informative glossary and a detailed index of terms. It is an excellent value for money. I bought a new copy on eBay for about £20 but I'm sure it can be found elsewhere on the internet.

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