Mycosis fungoides shows concurrent deregulation of multiple genes involved in the TNF signaling pathway:an expression profile study

Mycosis fungoides (MF) is the most frequent type of cutaneous T-cell lymphoma, whose diagnosis and study is hampered by its morphologic similarity to inflammatory dermatoses (ID) and the low proportion of tumoral cells, which often account for only 5% to 10% of the total tissue cells. cDNA microarray studies using the CNIO OncoChip of 29 MF and 11 ID cases revealed a signature of 27 genes implicated in the tumorigenesis of MF, including tumor necrosis factor receptor (TNFR)-dependent apoptosis regulators, STAT4, CD40L, and other oncogenes and apoptosis inhibitors. Subsequently a 6-gene prediction model was constructed that is capable of distinguishing MF and ID cases with unprecedented accuracy. This model correctly predicted the class of 97% of cases in a blind test validation using 24 MF patients with low clinical stages. Unsupervised hierarchic clustering has revealed 2 major subclasses of MF, one of which tends to include more aggressive-type MF cases including tumoral MF forms. Furthermore, signatures associated with abnormal immunophenotype (11 genes) and tumor stage disease (5 genes) were identified.

Dynamic axle force and road profile identification using a moving vehicle

The axle forces applied by a vehicle through its wheels are a critical part of the interaction between vehicles, pavements and bridges. Therefore, the minimisation of these forces is important in order to promote long pavement life spans and ensure that bridge loads are small. Moreover, as the road surface roughness affects the vehicle dynamic forces, the monitoring of pavements for highways and bridges is an important task. This paper presents a novel algorithm to identify these dynamic interaction forces which involves direct instrumentation of a vehicle with accelerometers. The ability of this approach to predict the pavement roughness is also presented. Moving force identification theory is applied to a vehicle model in theoretical simulations in order to obtain the interaction forces and pavement roughness from the measured accelerations. The method is tested for a range of bridge spans in simulations and the influence of road roughness level on the accuracy of the results is investigated. Finally, the challenge for the real-world problem is addressed in a laboratory experiment.

Predicting conversion to dementia in a memory clinic: A standard clinical approach compared with an empirically defined clustering method (latent profile analysis) for mild cognitive impairment subtyping

Introduction
Mild cognitive impairment (MCI) has clinical value in its ability to predict later dementia. A better understanding of cognitive profiles can further help delineate who is most at risk of conversion to dementia. We aimed to (1) examine to what extent the usual MCI subtyping using core criteria corresponds to empirically defined clusters of patients (latent profile analysis [LPA] of continuous neuropsychological data) and (2) compare the two methods of subtyping memory clinic participants in their prediction of conversion to dementia.

Methods
Memory clinic participants (MCI, n = 139) and age-matched controls (n = 98) were recruited. Participants had a full cognitive assessment, and results were grouped (1) according to traditional MCI subtypes and (2) using LPA. MCI participants were followed over approximately 2 years after their initial assessment to monitor for conversion to dementia.

Results
Groups were well matched for age and education. Controls performed significantly better than MCI participants on all cognitive measures. With the traditional analysis, most MCI participants were in the amnestic multidomain subgroup (46.8%) and this group was most at risk of conversion to dementia (63%). From the LPA, a three-profile solution fit the data best. Profile 3 was the largest group (40.3%), the most cognitively impaired, and most at risk of conversion to dementia (68% of the group).

Discussion
LPA provides a useful adjunct in delineating MCI participants most at risk of conversion to dementia and adds confidence to standard categories of clinical inference.

Microbiological Profile of Aggressive Periodontitis in a Sudanese Population

Background: Several bacterial species have been identified as being associated with aggressive periodontitis (AgP) notably Aggregatibacter actinomycetemcomitans (Aa) and Porphyromonas gingivalis (Pg). There are limited data on bacterial associations with AgP in African populations. Objective: To investigate possible associations between specific bacteria and AgP in a Sudanese population. Methods: Subgingival plaque samples were collected from 93 (20 male, 73 female) Sudanese patients diagnosed with AgP and from 72 (23 male, 48 female) periodontally healthy Sudanese controls. Quantitative PCR was used to identify Aa, Pg, Treponema denticola (Td) and Fusobacterium nucleatum (Fn). The prevalence of these bacterial species was compared using Chi-square analysis. Odds ratios (OR) were calculated using standard methods. Results: The cases with AgP were well matched in age with the controls: 24.8 (SD 5.1) compared with 23.5 (SD 3.7) years, p=0.07. There was a significantly higher prevalence of Pg in AgP (73%) than in the controls (33%), p<0.0001. The OR for Pg to be associated with AgP was 5.44 (95% confidence intervals 2.78-10.64). In 26 (38%) of the AgP cases positive for Pg there were low levels of this bacterium (<100 copies). Both Td and Fn were identified in virtually all (>95%) the plaque samples studied from both AgP and controls. Aa was the least frequently identified species and was present in only 28% of AgP and 18% of controls, p=0.14. The OR for Aa to be associated with AgP was slightly increased at 1.76 (95% CI 0.83-3.74), however, this was not significant (p=0.14). Conclusion: In the Sudanese subjects studied Pg but not Aa was associated with AgP. There were very low levels of Pg in many of the plaque samples from AgP.

Hα Line Profile Asymmetries and the Chromospheric Flare Velocity Field

The asymmetries observed in the line profiles of solar flares can provide important diagnostics of the properties and dynamics of the flaring atmosphere. In this paper the evolution of the Hα and Ca ii λ8542 lines are studied using high spatial, temporal, and spectral resolution ground-based observations of an M1.1 flare obtained with the Swedish 1 m Solar Telescope. The temporal evolution of the Hα line profiles from the flare kernel shows excess emission in the red wing (red asymmetry) before flare maximum and excess in the blue wing (blue asymmetry) after maximum. However, the Ca ii λ8542 line does not follow the same pattern, showing only a weak red asymmetry during the flare. RADYN simulations are used to synthesize spectral line profiles for the flaring atmosphere, and good agreement is found with the observations. We show that the red asymmetry observed in Hα is not necessarily associated with plasma downflows, and the blue asymmetry may not be related to plasma upflows. Indeed, we conclude that the steep velocity gradients in the flaring chromosphere modify the wavelength of the central reversal in the Hα line profile. The shift in the wavelength of maximum opacity to shorter and longer wavelengths generates the red and blue asymmetries, respectively.