Biomedical subjects

Anders S Johansson

Publications and source records attributed to Anders S Johansson.

4 recordsLinked to original sources

PubMed2006

Impaired masticatory behavior in subjects with reduced periodontal tissue support.

BACKGROUND: Mechanoreceptors situated in the periodontal ligament provide detailed information about intensive and spatial aspects of tooth loads, which support the neural control of masticatory forces. We asked whether a reduced periodontal ligament due to periodontitis, and, thus, an altered mechanoreceptive innervation of the teeth, would affect masticatory behavior when subjects used incisors to hold and split food. METHODS: We tested 11 subjects with reduced periodontal tissue support that rendered 30% to 70% alveolar bone loss for at least one pair of opposing anterior incisors. Forces were recorded when subjects used their affected incisors to hold half of a peanut for approximately 4 seconds and then split it. Age- and gender-matched healthy subjects served as the control group. None of the participants showed acute oral symptoms or massive periodontal inflammation. RESULTS: The test group used greater force when holding food between the teeth (1.1+/-0.4 N [ mean+/-1 SD]) compared to the control group (0.4+/-0.2 N). Hold forces used by subjects in the test group were also more variable, both within and between trials. The increase in bite force applied to split the peanut was slower and more hesitant for subjects in the test group compared to the control group. CONCLUSIONS: Reduced periodontal tissue support accompanies impaired regulation of masticatory forces. Faulty mechanoreceptive innervation of the periodontal ligament explains the elevated hold force, whereas a change in biting strategy due to the weakened support of the teeth may account for the more defensive food-splitting behavior.

Adult↗

PubMed2005

Sampling uncertainties for the detection of chemical agents in complex food matrices.

Using uncertainty associated with detection of aflatoxin in shelled corn as a model, the uncertainty associated with detecting chemical agents intentionally added to food products was evaluated. Accuracy and precision are two types of uncertainties generally associated with sampling plans. Sources of variability that affect precision were the primary focus of this investigation. Test procedures used to detect chemical agents generally include sampling, sample preparation, and analytical steps. The uncertainty of each step contributes to the total uncertainty of the test procedure. Using variance as a statistical measure of uncertainty, the variance associated with each step of the test procedure used to detect aflatoxin in shelled corn was determined for both low and high levels of contamination. For example, when using a 1-kg sample, Romer mill, 50-g subsample, and high-performance liquid chromatography to test a lot of shelled corn contaminated with aflatoxin at 10 ng/g, the total variance associated with the test procedure was 149.2 (coefficient of variation of 122.1%). The sampling, sample preparation, and analytical steps accounted for 83.0, 15.6, and 1.4% of the total variance, respectively. A variance of 149.2 suggests that repeated test results will vary from 0 to 33.9 ng/g. Using the same test procedure to detect aflatoxin at 10,000 ng/g, the total variance was 264,719 (coefficient of variation of 5.1%). The sampling, sample preparation, and analytical steps accounted for 41, 57, and 2% of the total variance, respectively. A variance of 264,719 suggests that repeated test results will vary from 8,992 to 11,008 ng/g. Foods contaminated at low levels reflect a situation in which a small percentage of particles is contaminated and sampling becomes the largest source of uncertainty. Large samples are required to overcome the "needle-in-the-haystack" problem. Aflatoxin is easier to detect and identify in foods intentionally contaminated at high levels than in foods with low levels of contamination because the relative standard deviation (coefficient of variation) decreases and the percentage of contaminated kernels increases with an increase in concentration.

Aflatoxins↗

PubMed2002

Sampling wheat for deoxynivalenol.

The variability associated with testing wheat for deoxynivalenol (DON) was measured using a 0.454 kg sample, a Romer mill, 25 g of comminuted subsample and the Romer Fluoroquant analytical method. The total variability was partitioned into sampling, sample preparation, and analytical variability components. Each variance component was found to be a function of the DON concentration and equations were developed to predict each variance component using regression techniques. The effects of sample size, subsample size, and number of aliquots on reducing the variability of the DON test procedure were also determined. Using the test procedure described above, the coefficient of variation (CV) associated with testing wheat at 5 ppm DON was found to be 13.4%. The CVs associated with sampling, sample preparation, and analysis were 6.3, 10.0, and 6.3%, respectively. The sample variations associated with testing wheat are relatively small when compared to CVs associated with testing other commodities for other mycotoxins such as aflatoxin in peanuts. Even with the use of a small sample size (0.454 kg), the sampling variation was not the largest source of error as found in other mycotoxin test procedures.

Algorithms↗

PubMedDate not supplied

Predicting aflatoxin and fumonisin in shelled corn lots using poor-quality grade components.

A study was conducted to determine if aflatoxin and fumonisin are concentrated in the poor-quality grade components of shelled corn. Four 1.0 kg test samples were each taken from 23 lots of shelled corn marketed in North Carolina. Inspectors from the Federal Grain Inspection Service divided each test sample into 3 grade components: (1) damaged kernels (DM), (2) broken corn and foreign material (BCFM), and )3) whole kernels (WH). The aflatoxin and fumonisin concentration was measured in each component and a mass balance equation was used to calculate the total concentration of each mycotoxin in each test sample. Averaged across all test samples, the aflatoxin concentrations in the DM, BCFM, and WH components were 1300.3, 455.2, and 37.3 ppb, respectively. Averaged across all test samples, the fumonisin concentrations in the DM, BCFM, and WH components were 148.3, 51.3, and 1.8 ppm, respectively. The DM and BCFM components combined accounted for only 5.0% of the test sample mass, but accounted for 59.8 and 77.5% of the total aflatoxin and fumonisin mass in the test sample, respectively. Both aflatoxin mass (ng) and aflatoxin concentration (ng/g) in the combined DM and BCFM components had high correlations with aflatoxin concentration in the lot. The highest correlation occurred when aflatoxin mass (ng) in the combined DM and BCFM components was related to aflatoxin concentration in the lot (0.964). Similar results were obtained for fumonisin. This study indicated that measuring either aflatoxin or fumonisin in the combined DM and BCFM grade components could be used as a screening method to predict either aflatoxin or fumonisin in a bulk lot of shelled corn.

Aflatoxins↗