METHODS: During 2012 to 2017, suspected thalassemia people were detected for common α- and β-thalassemia mutations by gap-Polymerase Chain Reaction (PCR) and reverse dot blot (RDB) analysis in Peking Union Medical College Hospital. One thousand and fifty-nine people with thalassemia mutations were analyzed retrospectively. We picked mutated individuals who originally came from northern areas, and conducted telephone follow-up survey in order to collect their ancestral information. Besides, we used "thalassemia", "mutation", and "Southeast Asian countries" as keywords to search the relevant studies in PubMed and Embase databases.
RESULTS: All carriers included in our study were resided in northern China. Among them, 17.3% were native northerners and 82.7% were immigrants from southern China. Although substantial difference was found in α- and β-thalassemia ratio and detailed spectrum of α- and β-globin mutation spectrum between our data and data obtained from a previous meta-analysis literature focused on southern China, the most common gene mutations were the same. Similar β-thalassemia mutation spectrum was found among Thai, Malaysian Chinese, and Guangdong people, however, no other similarities in gene profile were found between Chinese and other ethnic groups in Southeast Asia.
CONCLUSION: Chinese people in different areas had similar gene mutation, whereas they had significantly different mutation spectrums from other ethnic groups in Southeast Asia.
METHODS: The most used methods for the quantification of Hb A2 are based on automated high performance liquid chromatography (HPLC) or capillary electrophoresis (CE). In particular Hb analyses were performed by HPLC on three dedicated devices. DNA analyses were performed according to local standard protocols.
RESULTS: Here, we described eight new δ-globin gene variants discovered and characterized in some laboratories in Northern Italy in recent years. These new variants were added to the many already known Hb A2 variants that were found with an estimated frequency of about 1-2% during the screening tests in our laboratories.
CONCLUSIONS: The knowledge recognition of the delta variant on Hb analysis and accurate molecular characterization is crucial to provide an accurate definitive thalassemia diagnosis, particularly in young subjects who would like to ask for a prenatal diagnosis or preimplantation genetic diagnosis.
RESULT: By combining SNP information from literatures, GWAS study and NCBI ClinVar, 18 unique SNPs were selected for further analysis. From these 18 SNPs, 10 SNPs came from previous study of Helicobacter pylori infection among Malay patients, 6 SNPs were from NCBI ClinVar and 2 SNPs from GWAS studies. The analysis reveals that both Royal Kelantan Malay genomes shared all the 10 SNPs identified by Maran (Single Nucleotide Polymorphims (SNPs) genotypic profiling of Malay patients with and without Helicobacter pylori infection in Kelantan, 2011) and one SNP from GWAS study. In addition, the analysis also reveals that both Royal Kelantan Malay genomes shared 3 SNP markers; HBG1 (rs1061234), HBB (rs1609812) and BCL11A (rs766432) where all three markers were associated with beta-thalassemia.
CONCLUSIONS: Our findings suggest that the Royal Kelantan Malays carry the SNPs which are associated with protection to Helicobacter pylori infection. In addition they also carry SNPs which are associated with beta-thalassemia. These findings are in line with the findings by other researchers who conducted studies on thalassemia and Helicobacter pylori infection in the non-royal Malay population.
METHODS: The algorithm was developed using data from 345 TDT patients. Spearman's rank correlation was used to evaluate the conceptual overlap between the instruments. Model specifications were chosen using a stepwise regression. Both direct and response mapping methods were attempted. Six mapping estimation methods ordinary least squares (OLS), a log-transformed response using OLS, generalized linear model (GLM), two-part model (TPM), Tobit and multinomial logistic regression (MLOGIT) were tested to determine the root mean squared error (RMSE) and mean absolute error (MAE). Other criterion used were accuracy of the predicted utility score, proportions of absolute differences that was less than 0.03 and intraclass correlation coefficient. An in-sample, leave-one-out cross validation was conducted to test the generalizability of each model.
RESULTS: The best performing model was specified with three out of the four PedsQL GCS scales-the physical, emotional and social functioning score. The best performing estimation method for direct mapping was a GLM with a RMSE of 0.1273 and MAE of 0.1016, while the best estimation method for response mapping was the MLOGIT with a RMSE of 0.1597 and MAE of 0.0826.
CONCLUSION: The mapping algorithm developed using the GLM would facilitate the calculation of utility scores to inform economic evaluations for TDT patients when EQ-5D data is not available. However, caution should be exercised when using this algorithm in patients who have poor quality of life.