The K13 propeller domain mutation and pfmdr1 amplification have been proposed as useful molecular markers for detection and monitoring of artemisinin resistant Plasmodium falciparum Welch, 1897. Genomic DNA isolates of P. falciparum was extracted from 235 dried blood spot or whole blood samples collected from patients with uncomplicated falciparum malaria residing in areas along the Thai-Myanmar border during 2006-2010. Nested polymerase chain reaction (PCR) and sequencing were performed to detect mutations in K13 propeller domain of P. falciparum at codon 427-709. Pfmdr1 gene copy number was determined by SYBR Green I real-time PCR. High prevalence of pfmdr1 multiple copies was observed (42.5% of isolates). The presence of K13 mutations was low (40/235, 17.2%). Seventeen mutations had previously been reported and six mutations were newly detected. The C580Y was found in two isolates (0.9%). The F446I, N458Y and P574L mutations were commonly detected. Seven isolates had both K13 mutation and pfmdr1 multiple copies. It needs to be confirmed whether parasites harbouring both K13 mutation and pfmdr1 multiple copies and/or the observed new mutations of K13 propeller domain are associated with clinical artemisinin resistance., Papichaya Phompradit, Wanna Chaijaroenkul, Phunuch Muhamad, Kesara Na-Bangchang., and Obsahuje bibliografii
This article presents an alternative approach useful for medical practitioners who wish to detect malaria and accurately identify the level of severity. Malaria classi?ers are usually based on feed forward neural networks. In this study, the proposed classiffier is developed based on the Jordan-Elman neural networks. Its performance is evaluated using a receiver-operating characteristic curve, sensitivity, specificity, positive predictive value, negative predictive value, confusion matrix, mean square error, determinant coefficient, and reliability. The effectiveness of the classiffier is compared to a support vector machine and multiple regression models. The results of the comparative analysis demonstrate a superior performance level of the Jordan-Elman neural network model. Further comparison of the classier with previous literature indicates performance improvement over existing results. The Jordan-Elman neural networks classiffier can assist medical practitioners in the fast detection of malaria and determining its severity, especially in tropical and subtropical regions where cases of malaria are prevalent