Laboratory tests in patients treated with isotretinoin: occurrence of liver and muscle abnormalities and failure of AST and ALT to predict liver abnormality.

Current laboratory monitoring may not be optimal. A retrospective chart review was performed on thelaboratory results of 246 patients who were treated with isotretinoin for acne over a 9-year period. Tests obtained were CBC, lipid panel, AST, ALT, CK, GGT,and C-reactive protein. Thirty-five patients had an elevated AST and 35 of these had an elevated CK; 32 had an elevated ALT and 11 of these had an elevated CK. Thirteen patients had an elevated GGT; in 5 this was the only abnormality, whereas 8 had a GGT elevation accompanied by an elevated AST or ALT. Two had an elevated GGT and an elevated CK with normal AST and ALT. Fifty-two patients had a single episode of elevated CK, of which 22 were female. However, 57 had multiple CK elevations and only one was female. Thirty-five patients had CK elevationsnormal; 38 had levels between 2 and 3 times normal, 18 had levels between 3 and 4 times normal, and 18 had levels greater than 4 times normal. We suggest that ALT and AST are not useful for monitoring isotretinoin therapy and that GGT and CK may be of greater value in managing patients

Influenza: an emerging disease.

Because all known influenza A subtypes exist in the aquatic bird reservoir, influenza is not an eradicable disease; prevention and control are the only realistic goals. If people, pigs, and aquatic birds are the principal variables associated with interspecies transfer of influenza virus and the emergence of new human pandemic strains, influenza surveillance in these species is indicated. Live-bird markets housing a wide variety of avian species together (chickens, ducks, geese, pigeon, turkeys, pheasants, guinea fowl), occasionally with pigs, for sale directly to the public provide outstanding conditions for genetic mixing and spreading of influenza viruses; therefore, these birds should be monitored for influenza viruses. Moreover, if pigs are the mixing vessel for influenza viruses, surveillance in this population may also provide an early warning system for humans

Portraits of people with dementia : three case studies of creating portraits

Peer reviewedPreprin

Spatial statistics and soil mapping: A blossoming partnership under pressure

For the better part of the 20th century pedologists mapped soil by drawing boundaries between different classes of soil which they identified from survey on foot or by vehicle, supplemented by air-photo interpretation, and backed by an understanding of landscape and the processes by which soil is formed. Its limitations for representing gradual spatial variation and predicting conditions at unvisited sites became evident, and in the 1980s the introduction of geostatistics and specifically ordinary kriging revolutionized thinking and to a large extent practice. Ordinary kriging is based solely on sample data of the variable of interest—it takes no account of related covariates. The latter were incorporated from the 1990s onward as fixed effects and incorporated as regression predictors, giving rise to kriging with external drift and regression kriging. Simultaneous estimation of regression coefficients and variogram parameters is best done by residual maximum likelihood estimation. In recent years machine learning has become feasible for predicting soil conditions from huge sets of environmental data obtained from sensors aboard satellites and other sources to produce digital soil maps. The techniques are based on classification and regression, but they take no account of spatial correlations. Further, they are effectively ‘black boxes’; they lack transparency, and their output needs to be validated if they are to be trusted. They undoubtedly have merit; they are here to stay. They too, however, have their shortcomings when applied to spatial data, which spatial statisticians can help overcome. Spatial statisticians and pedometricians still have much to do to incorporate uncertainty into digital predictions, spatial averages and totals over regions, and to take into account errors in measurement and spatial positions of sample data. They must also communicate their understanding of these uncertainties to end users of soil maps, by whatever means they are made

Uncertainty assessment of spatial soil information

Uncertainty is present in our daily lives. It affects our decisions on what to do. The weather forecast might tell us that there is a 60% chance that it will rain: we take umbrellas. If it says that the chance of rain is only 10% we might decide to leave our umbrellas at home and risk getting wet. More seriously, farmers want to know the likelihood of disease in their crops and the deficiencies in plant nutrients in the soil. These are matters that affect profit and loss of farm business. Agencies responsible for public health and environmental protection need to weigh the risk of doing nothing in the face of uncertain threats against the cost of acting unnecessarily to counter them when the threats are almost non-existent. There are many examples of decision making problems involving uncertain soil information. They include the remediation of polluted soil, the prevention of soil erosion, and the mitigation of pesticide leaching. They are practical matters, not purely academic exercises in statistics. All measurements of soil properties (and other environmental variables) contain error in the sense that they depart from the true values. That error arises from imperfections in the analytical instruments, from the people who use them and from errors that occur during the processing of the recorded data to make them suitable for storage in information databases. Short-range spatial variation is another source of error, given that soil samples are never returned to where they were taken and sampling locations have positional error. Soil taken from location s and analysed in the laboratory might differ substantially from the soil at location s + h, even if |h| is as small as a few decimeters. Composite soil sampling can diminish these differences, but some error inevitably persists because even such a composite is still only a sample of all the soil at that site. All this means that we can never be sure about the true state of the soil: we, the producers and users of soil information, are to some extent uncertain. Uncertainty tends to increase when measurements of basic soil properties are used to obtain derived ones via pedotransfer functions or mechanistic models of dynamic soil processes, for example. Interpolation from measurements to create maps of soil properties adds to the errors of measurement and so too increases uncertainties. We must conclude that considerable uncertainty is often associated with the information that is stored in soil databases and presented in various forms, including maps. This does not mean that the information is of no value; uncertainty is not the same as ignorance. In many cases we do know a great deal about the soil, but we must also acknowledge that the information is not perfect. Some numerical expression of the uncertainty is important because it is needed to determine whether the information is sufficiently accurate for the purpose that a user has in mind. Soil data of too poor a quality might lead to flawed decisions with serious undesirable consequences, both economic and environmental. For instance, the European legislation on the use of pesticides in agriculture depends crucially on the leaching potential of these substances to the ground- and surface-water, which in turn depends importantly on soil properties. In these circumstances users should be aware of the quality of the soil information so that they can be sure that it is sufficiently reliable for their purposes. Ideally they should account for the uncertainty of the information when making their decisions. This chapter (i) provides a statistical definition of uncertainty in soil information; (ii) extends this definition to uncertainty in spatial soil information; (iii) reviews methods that are used to quantify uncertainty in soil information, while paying attention to different sources of uncertainty; (iv) shows how uncertainty in soil information propagates through subsequent analyses; and (v) explains how uncertainty information can be used in decision making. It focuses on the quantification of uncertainty of soil properties that are measured and recorded on continuous scales: properties such as pH, particle-size distribution, and soil organic matter content. The chapter also addresses uncertainty of categorical variables, such as soil type and diagnostic properties recorded as present or absent, i.e. binary variables. It begins with defining uncertainty in a single soil measuremen

Pharmacological activation of endogenous protective pathways against oxidative stress under conditions of sepsis

Funding The study was funded entirely by institutional funds.Peer reviewedPostprin

Role of the exchange and correlation potential into calculating the x-ray absorption spectra of half-metallic alloys: the case of Mn and Cu K-edge XANES in Cu2_2MnM (M = Al, Sn, In) Heusler alloys

This work reports a theoretical study of the x-ray absorption near-edge structure spectra at both the Cu and the Mn K-edge in several Cu$_2$MnM (M= Al, Sn and In) Heusler alloys. Our results show that {\it ab-initio} single-channel multiple-scattering calculations are able of reproducing the experimental spectra. Moreover, an extensive discussion is presented concerning the role of the final state potential needed to reproduce the experimental data of these half-metallic alloys. In particular, the effects of the cluster-size and of the exchange and correlation potential needed in reproducing all the experimental XANES features are discussed.Comment: 15 pages, 5 figure