Preventive effect of ozone on the development of white spot lesions during multibracket appliance therapy

OBJECTIVE: To test the null hypotheses: (1) there is no difference in the caries protective effect of ozone and Cervitec/Fluor Protector during multibracket (MB) appliance therapy, and (2) DIAGNOdent and quantitative light-induced fluorescence (QLF) are not superior to a visual evaluation of initial caries lesions. MATERIALS AND METHODS: Twenty right-handed patients with a very poor oral hygiene who required full MB appliance therapy were analyzed during 26 months. In a split-mouth-design, the four quadrants of each patient were either treated with ozone, a combination of Cervitec and Fluor Protector, or served as untreated controls. The visible plaque index (VPI) and white spot formation were analyzed clinically. DIAGNOdent and QLF were used for a quantitative assessment of white spot formation. RESULTS: The average VPI in all four dental arch quadrants amounted to 55.6% and was independent of the preventive measure undertaken. In the quadrants treated with Cervitec/Fluor Protector, only 0.7% of the areas developed new, clinically visible white spots. This was significantly (P < .05) less than in the quadrants treated with ozone (3.2%). The lesions detected with QLF only partially corresponded to the clinically detected white spots, while DIAGNOdent proved to be unable to detect any changes at all. CONCLUSIONS: The caries protective effect of Cervitec/Fluor Protector during MB therapy was superior to ozone, and a visual evaluation of initial caries lesions was superior to both DIAGNOdent and QLF.

Assessing vegetation cover and biomass in restored erosion areas in Iceland using SPOT satellite data

Due to highly erodible volcanic soils and a harsh climate, livestock grazing in Iceland has led to serious soil erosion on about 40% of the country's surface. Over the last 100 years, various revegetation and restoration measures were taken on large areas distributed all over Iceland in an attempt to counteract this problem. The present research aimed to develop models for estimating percent vegetation cover (VC) and aboveground biomass (AGB) based on satellite data, as this would make it possible to assess and monitor the effectiveness of restoration measures over large areas at a fairly low cost. Models were developed based on 203 vegetation cover samples and 114 aboveground biomass samples distributed over five SPOT satellite datasets. All satellite datasets were atmospherically corrected, and digital numbers were converted into ground reflectance. Then a selection of vegetation indices (VIs) was calculated, followed by simple and multiple linear regression analysis of the relations between the field data and the calculated VIs. Best results were achieved using multiple linear regression models for both %VC and AGB. The model calibration and validation results showed that R2 and RMSE values for most VIs do not vary very much. For percent VC, R2 values range between 0.789 and 0.822, leading to RMSEs ranging between 15.89% and 16.72%. For AGB, R2 values for low-biomass areas (AGB < 800 g/m2) range between 0.607 and 0.650, leading to RMSEs ranging between 126.08 g/m2 and 136.38 g/m2. The AGB model developed for all areas, including those with high biomass coverage (AGB > 800 g/m2), achieved R2 values between 0.487 and 0.510, resulting in RMSEs ranging from 234 g/m2 to 259.20 g/m2. The models predicting percent VC generally overestimate observed low percent VC and slightly underestimate observed high percent VC. The estimation models for AGB behave in a similar way, but over- and underestimation are much more pronounced. These results show that it is possible to estimate percent VC with high accuracy based on various VIs derived from SPOT satellite data. AGB of restoration areas with low-biomass values of up to 800 g/m2 can likewise be estimated with high accuracy based on various VIs derived from SPOT satellite data, whereas in the case of high biomass coverage, estimation accuracy decreases with increasing biomass values. Accordingly, percent VC can be estimated with high accuracy anywhere in Iceland, whereas AGB is much more difficult to estimate, particularly for areas with high-AGB variability.

"Hot spot" in the PROP1 gene responsible for combined pituitary hormone deficiency.

As pituitary function depends on the integrity of the hypothalamic-pituitary axis, any defect in the development and organogenesis of this gland may account for a form of combined pituitary hormone deficiency (CPHD). Although pit-1 was 1 of the first factors identified as a cause of CPHD in mice, many other homeodomain and transcription factors have been characterized as being involved in different developmental stages of pituitary gland development, such as prophet of pit-1 (prop-1), P-Lim, ETS-1, and Brn 4. The aims of the present study were first to screen families and patients suffering from different forms of CPHD for PROP1 gene alterations, and second to define possible hot spots and the frequency of the different gene alterations found. Of 73 subjects (36 families) analyzed, we found 35 patients, belonging to 18 unrelated families, with CPHD caused by a PROP1 gene defect. The PROP1 gene alterations included 3 missense mutations, 2 frameshift mutations, and 1 splice site mutation. The 2 reported frameshift mutations could be caused by any 2-bp GA or AG deletion at either the 148-GGA-GGG-153 or 295-CGA-GAG-AGT-303 position. As any combination of a GA or AG deletion yields the same sequencing data, the frameshift mutations were called 149delGA and 296delGA, respectively. All but 1 mutation were located in the PROP1 gene encoding the homeodomain. Importantly, 3 tandem repeats of the dinucleotides GA at location 296-302 in the PROP1 gene represent a hot spot for CPHD. In conclusion, the PROP1 gene seems to be a major candidate gene for CPHD; however, further studies are needed to evaluate other genetic defects involved in pituitary development.

Are sample sizes clear and justified in RCTs published in dental journals?

Sample size calculations are advocated by the CONSORT group to justify sample sizes in randomized controlled trials (RCTs). The aim of this study was primarily to evaluate the reporting of sample size calculations, to establish the accuracy of these calculations in dental RCTs and to explore potential predictors associated with adequate reporting. Electronic searching was undertaken in eight leading specific and general dental journals. Replication of sample size calculations was undertaken where possible. Assumed variances or odds for control and intervention groups were also compared against those observed. The relationship between parameters including journal type, number of authors, trial design, involvement of methodologist, single-/multi-center study and region and year of publication, and the accuracy of sample size reporting was assessed using univariable and multivariable logistic regression. Of 413 RCTs identified, sufficient information to allow replication of sample size calculations was provided in only 121 studies (29.3%). Recalculations demonstrated an overall median overestimation of sample size of 15.2% after provisions for losses to follow-up. There was evidence that journal, methodologist involvement (OR = 1.97, CI: 1.10, 3.53), multi-center settings (OR = 1.86, CI: 1.01, 3.43) and time since publication (OR = 1.24, CI: 1.12, 1.38) were significant predictors of adequate description of sample size assumptions. Among journals JCP had the highest odds of adequately reporting sufficient data to permit sample size recalculation, followed by AJODO and JDR, with 61% (OR = 0.39, CI: 0.19, 0.80) and 66% (OR = 0.34, CI: 0.15, 0.75) lower odds, respectively. Both assumed variances and odds were found to underestimate the observed values. Presentation of sample size calculations in the dental literature is suboptimal; incorrect assumptions may have a bearing on the power of RCTs.

Identification of a new genomic hot spot of evolutionary diversification of protein function.

Establishment of phylogenetic relationships remains a challenging task because it is based on computational analysis of genomic hot spots that display species-specific sequence variations. Here, we identify a species-specific thymine-to-guanine sequence variation in the Glrb gene which gives rise to species-specific splice donor sites in the Glrb genes of mouse and bushbaby. The resulting splice insert in the receptor for the inhibitory neurotransmitter glycine (GlyR) conveys synaptic receptor clustering and specific association with a particular synaptic plasticity-related splice variant of the postsynaptic scaffold protein gephyrin. This study identifies a new genomic hot spot which contributes to phylogenetic diversification of protein function and advances our understanding of phylogenetic relationships.

Effect sizes and standardization in neighbourhood models of forest stands: potential biases and misinterpretations

Effects of conspecific neighbours on survival and growth of trees have been found to be related to species abundance. Both positive and negative relationships may explain observed abundance patterns. Surprisingly, it is rarely tested whether such relationships could be biased or even spurious due to transforming neighbourhood variables or influences of spatial aggregation, distance decay of neighbour effects and standardization of effect sizes. To investigate potential biases, communities of 20 identical species were simulated with log-series abundances but without species-specific interactions. No relationship of conspecific neighbour effects on survival or growth with species abundance was expected. Survival and growth of individuals was simulated in random and aggregated spatial patterns using no, linear, or squared distance decay of neighbour effects. Regression coefficients of statistical neighbourhood models were unbiased and unrelated to species abundance. However, variation in the number of conspecific neighbours was positively or negatively related to species abundance depending on transformations of neighbourhood variables, spatial pattern and distance decay. Consequently, effect sizes and standardized regression coefficients, often used in model fitting across large numbers of species, were also positively or negatively related to species abundance depending on transformation of neighbourhood variables, spatial pattern and distance decay. Tests using randomized tree positions and identities provide the best benchmarks by which to critically evaluate relationships of effect sizes or standardized regression coefficients with tree species abundance. This will better guard against potential misinterpretations.

Spinal Spot Sign.

INTRODUCTION A marker predictive of hematoma expansion in the central nervous system could aid the selection of patients for hemostatic or surgical treatment. CASE REPORT Here, we present a 83-year-old patient with acute spinal subdural hematoma with paraparesis progressing to paraplegia. A contrast extravasation within the intraspinal hematoma was visualized on spinal MR indicating active bleeding (spinal spot sign). A second acquisition of contrast-enhanced MR images showed progression of contrast extravasation helping to different active bleeding from spinal arteriovenous malformations/fistula. CONCLUSIONS A "spinal spot sign" may be important for treatment decisions, notably in patients with incomplete neurological deficits at the time of imaging.

Gestation-specific reference intervals for comprehensive spot urinary steroid hormone metabolite analysis in normal singleton pregnancy and 6 weeks postpartum

BACKGROUND: Normal pregnancy depends on pronounced adaptations in steroid hormone concentrations. Although in recent years, the understanding of these hormones in pregnancy has improved, the interpretation is hampered by insufficient reference values. Our aim was to establish gestation-specific reference intervals for spot urinary steroid hormone levels in normal singleton pregnancies and 6 weeks postpartum. METHODS: Cross-sectional multicentre observational study. Women recruited between 2008 and 2013 at 3 University Hospitals in Switzerland (Bern), Scotland (Glasgow) and Austria (Graz). Spot urine was collected from healthy women undergoing a normal pregnancy (age, 16-45 years; mean, 31 years) attending routine antenatal clinics at gestation weeks 11, 20, and 28 and approximately 6 weeks postpartum. Urine steroid hormone levels were analysed using gas-chromatography mass spectrometry. Creatinine was also measured by routine analysis and used for normalisation. RESULTS: From the results, a reference interval was calculated for each hormone metabolite at each trimester and 6 weeks postpartum. Changes in these concentrations between trimesters and postpartum were also observed for several steroid hormones and followed changes proposed for index steroid hormones. CONCLUSIONS: Normal gestation-specific reference values for spot urinary steroid hormones throughout pregnancy and early postpartum are now available to facilitate clinical management and research approaches to steroid hormone metabolism in pregnancy and the early postpartum period.