Yellow crystalline powder
Scutellaria baicalensis Georgi
5-Hydroxy-4-oxo-2-phenyl-4H-chromen-7-yl β-D-glucopyranosiduronic acid/Chrysin-7-O-beta-D-glucuronide/Chrysin-7-glucoronide/4H-1-Benzopyran-4-one, 7-(β-D-glucopyranuronosyloxy)-5-hydroxy-2-phenyl-/Chrysin-7-O-β-D-glucoronide/Chrysin 7-O-beta-D-glucopyranuronoside/Chrysin-7-O-Beta-D-glucoronide/Chrysin glucuronide/Chrysin 7-beta-D-glucuronide/Chrysin-7-O-β-D-glucuronide
787.8±60.0 °C at 760 mmHg
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PCR-restriction endonuclease analysis (PRA) was used for direct identification of Mycobacterium haemophilum in clinical specimens from immunocompromised patients. PRA correctly identified M. haemophilum in four smear-positive specimens. Direct identification by PRA takes 2 to 3 working days compared to the 3 to 5 weeks required for culture isolation and identification by conventional methods.
In recent years, Mycobacterium haemophilum has emerged as an important human pathogen (6, 10, 12), causing mainly opportunistic infections in severely immunocompromised patients with AIDS and those receiving immunosuppressive therapy after transplantation (1, 4, 7, 8, 12, 16). M. haemophilum has also been isolated from localized lesions in immunocompetent pediatric patients with cervical lymphadenopathy (3, 9). Superficial lesions such as cutaneous lesions and multiple skin nodules are not uncommon clinical presentations of M. haemophilum infection (6, 10, 12). Though necessary, culture isolation and identification of mycobacteria by conventional methods, especially for M. haemophilum, are time-consuming and laborious, usually taking 3 to 5 weeks (5). PCR-restriction endonuclease analysis (PRA) of an amplified 439-bp segment of the hsp65 gene encoding the 65-kDa heat shock protein has been successfully used for the rapid identification of mycobacterial isolates to the species level and has gained wide acceptance (11, 13, 14). This study is aimed at applying PRA as a means of rapid identification of M. haemophilum directly from acid-fast bacillus (AFB) smear-positive skin lesion specimens from immunocompromised patients.
Direct Identification of Mycobacterium haemophilum in Skin Lesions of Immunocompromised Patients by PCR-Restriction Endonuclease Analysis
S. X. Wang,1,* L. H. Sng,1 H. N. Leong,2 and B. H. Tan2
The WHO has established the disability-adjusted life year (DALY) as a metric for measuring the burden of human disease and injury globally. However, most DALY estimates have been calculated as national totals. We mapped spatial variation in the burden of human African trypanosomiasis (HAT) in Uganda for the years 2000-2009. This represents the first geographically delimited estimation of HAT disease burden at the sub-country scale.
Disability-adjusted life-year (DALY) totals for HAT were estimated based on modelled age and mortality distributions, mapped using Geographic Information Systems (GIS) software, and summarised by parish and district. While the national total burden of HAT is low relative to other conditions, high-impact districts in Uganda had DALY rates comparable to the national burden rates for major infectious diseases. The calculated average national DALY rate for 2000-2009 was 486.3 DALYs/100 000 persons/year, whereas three districts afflicted by rhodesiense HAT in southeastern Uganda had burden rates above 5000 DALYs/100 000 persons/year, comparable to national GBD 2004 average burden rates for malaria and HIV/AIDS.
These results provide updated and improved estimates of HAT burden across Uganda, taking into account sensitivity to under-reporting. Our results highlight the critical importance of spatial scale in disease burden analyses. National aggregations of disease burden have resulted in an implied bias against highly focal diseases for which geographically targeted interventions may be feasible and cost-effective. This has significant implications for the use of DALY estimates to prioritize disease interventions and inform cost-benefit analyses.
Incorporating Scale Dependence in Disease Burden Estimates: The Case of Human African Trypanosomiasis in Uganda
Finola Hackett, 1 , * Lea Berrang Ford, 1 Eric Fevre, 2 and Pere Simarro 3
The increasing availability of whole-genome sequence data is expected to increase the accuracy of genomic prediction. However, results from simulation studies and analysis of real data do not always show an increase in accuracy from sequence data compared to high-density (HD) single nucleotide polymorphism (SNP) chip genotypes. In addition, the sheer number of variants makes analysis of all variants and accurate estimation of all effects computationally challenging. Our objective was to find a strategy to approximate the analysis of whole-sequence data with a Bayesian variable selection model. Using a simulated dataset, we applied a Bayes R hybrid model to analyse whole-sequence data, test the effect of dropping a proportion of variants during the analysis, and test how the analysis can be split into separate analyses per chromosome to reduce the elapsed computing time. We also investigated the effect of imputation errors on prediction accuracy. Subsequently, we applied the approach to a dataset that contained imputed sequences and records for production and fertility traits for 38,492 Holstein, Jersey, Australian Red and crossbred bulls and cows.
With the simulated dataset, we found that prediction accuracy was highly increased for a breed that was not represented in the training population for sequence data compared to HD SNP data. Either dropping part of the variants during the analysis or splitting the analysis into separate analyses per chromosome decreased accuracy compared to analysing whole-sequence data. First, dropping variants from each chromosome and reanalysing the retained variants together resulted in an accuracy similar to that obtained when analysing whole-sequence data. Adding imputation errors decreased prediction accuracy, especially for errors in the validation population. With real data, using sequence variants resulted in accuracies that were similar to those obtained with the HD SNPs.
We present an efficient approach to approximate analysis of whole-sequence data with a Bayesian variable selection model. The lack of increase in prediction accuracy when applied to real data could be due to imputation errors, which demonstrates the importance of developing more accurate methods of imputation or directly genotyping sequence variants that have a major effect in the prediction equation.
Electronic supplementary material
The online version of this article (doi:10.1186/s12711-017-0347-9) contains supplementary material, which is available to authorized users.
Multi-breed genomic prediction using Bayes R with sequence data and dropping variants with a small effect
Irene van den Berg,corresponding author1 Phil J. Bowman,2,3 Iona M. MacLeod,2 Ben J. Hayes,2,4 Tingting Wang,2 Sunduimijid Bolormaa,2 and Mike E. Goddard1,2