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 12,436 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Bites by the brown spider (Loxosceles spp.) are an important health problem in South America, where three species predominate (Loxosceles laeta, Loxosceles gaucho, Loxosceles intermedia). Brown spider bites (loxoscelism) induce a block of cutaneous necrosis and, less commonly, may cause fatal systemic poisoning. A variety of controversial protocols are used to treat loxoscelism, while treatment with antivenin is the only venom specific treatment. Here we studied the action of the venom as well as the response to the antivenin for Loxosceles through an experimental study that simulates bites of L. intermedia (bites of this species are the most common in Brazil). Beneficial effects are known for antivenin applied quickly (within 4 h) after envenomation. Here we wished to examine the temporal development of the brown spider bite as well as the temporal patterns of the action of the antivenin to determine the time limits for beneficial use of the antivenin after envenomation. This information is important since most patients only appear for treatment several hours after being bitten. New Zealand rabbits were experimentally exposed to the venom from brown spiders by the injection of venom from L. intermedia (2x minimum necrotic dose), followed at regular time intervals by antivenin. The use of the loxoscelic antivenin--CPPI (4 mL per animal)--minimized the effects of envenomation when applied for up to 12 h after the injection of the venom, as evaluated by cutaneous (erythrema, edema, ecchymosis and necrosis) and systemic (blood cell and platelet counts, hematimetrics and fibrinogen dosage) criteria. Also, antivenin reduced the size of the necrotic area when applied up to 48 h after envenomation. Thus, therapy with loxoscelic antivenin, CPPI, may provide beneficial results by interfering with envenomation well after the bite occurred and therefore may become an important tool for medical treatment of brown spider bites.
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PMID:Analysis of therapeutic benefits of antivenin at different time intervals after experimental envenomation in rabbits by venom of the brown spider (Loxosceles intermedia). 1967 81

The aim of this study was to assess the performance of Bayesian models commonly used for genomic selection to predict "difficult-to-predict" dairy traits, such as milk fatty acid (FA) expressed as percentage of total fatty acids, and technological properties, such as fresh cheese yield and protein recovery, using Fourier-transform infrared (FTIR) spectral data. Our main hypothesis was that Bayesian models that can estimate shrinkage and perform variable selection may improve our ability to predict FA traits and technological traits above and beyond what can be achieved using the current calibration models (e.g., partial least squares, PLS). To this end, we assessed a series of Bayesian methods and compared their prediction performance with that of PLS. The comparison between models was done using the same sets of data (i.e., same samples, same variability, same spectral treatment) for each trait. Data consisted of 1,264 individual milk samples collected from Brown Swiss cows for which gas chromatographic FA composition, milk coagulation properties, and cheese-yield traits were available. For each sample, 2 spectra in the infrared region from 5,011 to 925 cm(-1) were available and averaged before data analysis. Three Bayesian models: Bayesian ridge regression (Bayes RR), Bayes A, and Bayes B, and 2 reference models: PLS and modified PLS (MPLS) procedures, were used to calibrate equations for each of the traits. The Bayesian models used were implemented in the R package BGLR (http://cran.r-project.org/web/packages/BGLR/index.html), whereas the PLS and MPLS were those implemented in the WinISI II software (Infrasoft International LLC, State College, PA). Prediction accuracy was estimated for each trait and model using 25 replicates of a training-testing validation procedure. Compared with PLS, which is currently the most widely used calibration method, MPLS and the 3 Bayesian methods showed significantly greater prediction accuracy. Accuracy increased in moving from calibration to external validation methods, and in moving from PLS and MPLS to Bayesian methods, particularly Bayes A and Bayes B. The maximum R(2) value of validation was obtained with Bayes B and Bayes A. For the FA, C10:0 (% of each FA on total FA basis) had the highest R(2) (0.75, achieved with Bayes A and Bayes B), and among the technological traits, fresh cheese yield R(2) of 0.82 (achieved with Bayes B). These 2 methods have proven to be useful instruments in shrinking and selecting very informative wavelengths and inferring the structure and functions of the analyzed traits. We conclude that Bayesian models are powerful tools for deriving calibration equations, and, importantly, these equations can be easily developed using existing open-source software. As part of our study, we provide scripts based on the open source R software BGLR, which can be used to train customized prediction equations for other traits or populations.
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PMID:Bayesian regression models outperform partial least squares methods for predicting milk components and technological properties using infrared spectral data. 2638 15