Adverse side effects of statins in the oral cavity

Increased plasma levels of cholesterol are high risk factors of cardiovascular disease. Statins are drugs that inhibit cholesterol synthesis at both pancreatic and extrahepathic levels, being the treatment of choice for hypercholesterolemia. Objective: To analyze the side effects of statins in the mouth cavity, and to analyze the symptoms after interruption of the treatment. Design: Observational study, preliminary. Material and methods: Patients aged 50-70, diagnosed with hypercholesterolemia and undergoing treatment with statins, referred from their primary care physician to the dentist"s office. Anamnesis over oral symptoms was performed in the first visit. Statin treatment was discontinued, followed by lab tests and control visits seven and fifteen days later. We monitored the improvement and/or remission of oral symptoms. Statin treatment was resumed, sending out a report of the patient evolution to the PCP. Symptoms were registered in sheet specially designed for the study. Exclusion criteria: patient refusal, use of drugs for dry mouth treatment, Sjögren"s syndrome. Results: n=26 patients. Dry mouth patients: improvement in 17 out of 23 patients (88.5%). Itchiness: 6 out of 15 cases improved (57.7%). Bitterness: improvement in 13 out of 14 patients (53.8%). Cough: improvement in 11 out of 12 patients (46.1%). Discussion: A high percentage of oral symptoms are associated to treatment with statins. There is a marked improvement after temporary interruption of the treatment. Little is known regarding the side effects of oral treatment with statins. This preliminary study includes a relatively small number of patients. The design of experimental treatments will be required to establish a true correlation between statin treatment and oral symptoms

The ager tarraconensis project (right side of river Francoli) (PAT): the application of geophysical survey to identify rural Roman settlement typologies

The methodology of the ager Tarraconensis project also included geophysical surveys aiming to distinguish different categories of rural settlements. Two geophysical techniques (resistivity and magnetometry) were combined to reveal traces of unearth structures from a selection of sites identified from the field survey. Results of geophysical surveys of these seven sites as well as conclusions obtained from this approach are discussed here.

Self-Organising Maps and Correlation Analysis as a Tool to Explore Patterns in Excitation-Emission Matrix Data Sets and to Discriminate Dissolved Organic Matter Fluorescence Components

Dissolved organic matter (DOM) is a complex mixture of organic compounds, ubiquitous in marine and freshwater systems. Fluorescence spectroscopy, by means of Excitation-Emission Matrices (EEM), has become an indispensable tool to study DOM sources, transport and fate in aquatic ecosystems. However the statistical treatment of large and heterogeneous EEM data sets still represents an important challenge for biogeochemists. Recently, Self-Organising Maps (SOM) has been proposed as a tool to explore patterns in large EEM data sets. SOM is a pattern recognition method which clusterizes and reduces the dimensionality of input EEMs without relying on any assumption about the data structure. In this paper, we show how SOM, coupled with a correlation analysis of the component planes, can be used both to explore patterns among samples, as well as to identify individual fluorescence components. We analysed a large and heterogeneous EEM data set, including samples from a river catchment collected under a range of hydrological conditions, along a 60-km downstream gradient, and under the influence of different degrees of anthropogenic impact. According to our results, chemical industry effluents appeared to have unique and distinctive spectral characteristics. On the other hand, river samples collected under flash flood conditions showed homogeneous EEM shapes. The correlation analysis of the component planes suggested the presence of four fluorescence components, consistent with DOM components previously described in the literature. A remarkable strength of this methodology was that outlier samples appeared naturally integrated in the analysis. We conclude that SOM coupled with a correlation analysis procedure is a promising tool for studying large and heterogeneous EEM data sets.

1,2-Dimethylindole-3-sulfonyl (MIS) as protecting group for the side chain of arginine

The protection of arginine (Arg) side chains is a crucial issue in peptide chemistry because of the propensity of the basic guanidinium group to produce side reactions. Currently, sulfonyl-type protecting groups, such as 2,2,5,7,8-pentamethylchroman (Pmc) and 2,2,4,6,7-pentamethyldihydrobenzofurane (Pbf), are the most widely used for this purpose. Nevertheless, Arg side chain protection remains problematic as a result of the acid stability of these two compounds. This issue is even more relevant in Arg-rich sequences, acid-sensitive peptides and large-scale syntheses. The 1,2-dimethylindole-3-sulfonyl (MIS) group is more acid-labile than Pmc and Pbf and can therefore be a better option for Arg side chain protection. In addition, MIS is compatible with tryptophan-containing peptides.

Definition of linear color models in the RGB vector color space to detect red peaches in orchard images taken under natural illumination

This work proposes the detection of red peaches in orchard images based on the definition of different linear color models in the RGB vector color space. The classification and segmentation of the pixels of the image is then performed by comparing the color distance from each pixel to the different previously defined linear color models. The methodology proposed has been tested with images obtained in a real orchard under natural light. The peach variety in the orchard was the paraguayo (Prunus persica var. platycarpa) peach with red skin. The segmentation results showed that the area of the red peaches in the images was detected with an average error of 11.6%; 19.7% in the case of bright illumination; 8.2% in the case of low illumination; 8.6% for occlusion up to 33%; 12.2% in the case of occlusion between 34 and 66%; and 23% for occlusion above 66%. Finally, a methodology was proposed to estimate the diameter of the fruits based on an ellipsoidal fitting. A first diameter was obtained by using all the contour pixels and a second diameter was obtained by rejecting some pixels of the contour. This approach enables a rough estimate of the fruit occlusion percentage range by comparing the two diameter estimates.

Technical Note: Dissolved organic matter fluorescence a finite mixture approach to deconvolve excitation-emission matrices

The analysis of the shape of excitation-emission matrices (EEMs) is a relevant tool for exploring the origin, transport and fate of dissolved organic matter (DOM) in aquatic ecosystems. Within this context, the decomposition of EEMs is acquiring a notable relevance. A simple mathematical algorithm that automatically deconvolves individual EEMs is described, creating new possibilities for the comparison of DOM fluorescence properties and EEMs that are very different from each other. A mixture model approach is adopted to decompose complex surfaces into sub-peaks. The laplacian operator and the Nelder-Mead optimisation algorithm are implemented to individuate and automatically locate potential peaks in the EEM landscape. The EEMs of a simple artificial mixture of fluorophores and DOM samples collected in a Mediterranean river are used to describe the model application and to illustrate a strategy that optimises the search for the optimal output.

Fmoc-2-Mercaptobenzothiazole (MBT), for the Introduction of the Fmoc Moiety Free of Side-Reactions

A double side-reaction, consisting in the formation of Fmoc--Ala-OH and Fmoc--Ala-AA-OH, during the preparation of Fmoc protected amino acids (Fmoc-AA-OH) with Fmoc-OSu is discussed. Furthermore, the new Fmoc-2-MBT reagent is proposed for avoiding these side-reactions as well as the formation of the Fmoc-dipeptides (Fmoc-AA-AA-OH) and even tripeptides, which is another important side-reaction when chloroformates such as Fmoc-Cl is used for the protection of the -amino function of the amino acids.

Self-Organising Maps and Correlation Analysis as a Tool to Explore Patterns in Excitation-Emission Matrix Data Sets and to Discriminate Dissolved Organic Matter Fluorescence Components

Dissolved organic matter (DOM) is a complex mixture of organic compounds, ubiquitous in marine and freshwater systems. Fluorescence spectroscopy, by means of Excitation-Emission Matrices (EEM), has become an indispensable tool to study DOM sources, transport and fate in aquatic ecosystems. However the statistical treatment of large and heterogeneous EEM data sets still represents an important challenge for biogeochemists. Recently, Self-Organising Maps (SOM) has been proposed as a tool to explore patterns in large EEM data sets. SOM is a pattern recognition method which clusterizes and reduces the dimensionality of input EEMs without relying on any assumption about the data structure. In this paper, we show how SOM, coupled with a correlation analysis of the component planes, can be used both to explore patterns among samples, as well as to identify individual fluorescence components. We analysed a large and heterogeneous EEM data set, including samples from a river catchment collected under a range of hydrological conditions, along a 60-km downstream gradient, and under the influence of different degrees of anthropogenic impact. According to our results, chemical industry effluents appeared to have unique and distinctive spectral characteristics. On the other hand, river samples collected under flash flood conditions showed homogeneous EEM shapes. The correlation analysis of the component planes suggested the presence of four fluorescence components, consistent with DOM components previously described in the literature. A remarkable strength of this methodology was that outlier samples appeared naturally integrated in the analysis. We conclude that SOM coupled with a correlation analysis procedure is a promising tool for studying large and heterogeneous EEM data sets.

Classification of textures seen from different distances and under varying illumination direction

Changes in the angle of illumination incident upon a 3D surface texture can significantly alter its appearance, implying variations in the image texture. These texture variations produce displacements of class members in the feature space, increasing the failure rates of texture classifiers. To avoid this problem, a model-based texture recognition system which classifies textures seen from different distances and under different illumination directions is presented in this paper. The system works on the basis of a surface model obtained by means of 4-source colour photometric stereo, used to generate 2D image textures under different illumination directions. The recognition system combines coocurrence matrices for feature extraction with a Nearest Neighbour classifier. Moreover, the recognition allows one to guess the approximate direction of the illumination used to capture the test image