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A structural equation modeling approach for the adoption of cloud computing to enhance the Malaysian healthcare sector

Ratnam KA, Dominic PD, Ramayah T

J Med Syst, 2014 Aug;38(8):82.
PMID: 24957398 DOI: 10.1007/s10916-014-0082-5

The investments and costs of infrastructure, communication, medical-related equipments, and software within the global healthcare ecosystem portray a rather significant increase. The emergence of this proliferation is then expected to grow. As a result, information and cross-system communication became challenging due to the detached independent systems and subsystems which are not connected. The overall model fit expending over a sample size of 320 were tested with structural equation modelling (SEM) using AMOS 20.0 as the modelling tool. SPSS 20.0 is used to analyse the descriptive statistics and dimension reliability. Results of the study show that system utilisation and system impact dimension influences the overall level of services of the healthcare providers. In addition to that, the findings also suggest that systems integration and security plays a pivotal role for IT resources in healthcare organisations. Through this study, a basis for investigation on the need to improvise the Malaysian healthcare ecosystem and the introduction of a cloud computing platform to host the national healthcare information exchange has been successfully established.
Performance of univariate forecasting on seasonal diseases: the case of tuberculosis

Permanasari AE, Rambli DR, Dominic PD

Adv Exp Med Biol, 2011;696:171-9.
PMID: 21431557 DOI: 10.1007/978-1-4419-7046-6_17

The annual disease incident worldwide is desirable to be predicted for taking appropriate policy to prevent disease outbreak. This chapter considers the performance of different forecasting method to predict the future number of disease incidence, especially for seasonal disease. Six forecasting methods, namely linear regression, moving average, decomposition, Holt-Winter's, ARIMA, and artificial neural network (ANN), were used for disease forecasting on tuberculosis monthly data. The model derived met the requirement of time series with seasonality pattern and downward trend. The forecasting performance was compared using similar error measure in the base of the last 5 years forecast result. The findings indicate that ARIMA model was the most appropriate model since it obtained the less relatively error than the other model.
iGluK-Deep: computational identification of lysine glutarylation sites using deep neural networks with general pseudo amino acid compositions

Naseer S, Ali RF, Khan YD, Dominic PDD

J Biomol Struct Dyn, 2022;40(22):11691-11704.
PMID: 34396935 DOI: 10.1080/07391102.2021.1962738

Lysine glutarylation is a post-translation modification which plays an important regulatory role in a variety of physiological and enzymatic processes including mitochondrial functions and metabolic processes both in eukaryotic and prokaryotic cells. This post-translational modification influences chromatin structure and thereby results in global regulation of transcription, defects in cell-cycle progression, DNA damage repair, and telomere silencing. To better understand the mechanism of lysine glutarylation, its identification in a protein is necessary, however, experimental methods are time-consuming and labor-intensive. Herein, we propose a new computational prediction approach to supplement experimental methods for identification of lysine glutarylation site prediction by deep neural networks and Chou's Pseudo Amino Acid Composition (PseAAC). We employed well-known deep neural networks for feature representation learning and classification of peptide sequences. Our approach opts raw pseudo amino acid compositions and obsoletes the need to separately perform costly and cumbersome feature extraction and selection. Among the developed deep learning-based predictors, the standard neural network-based predictor demonstrated highest scores in terms of accuracy and all other performance evaluation measures and outperforms majority of previously reported predictors without requiring expensive feature extraction process. iGluK-Deep:Computational Identification of lysine glutarylationsites using deep neural networks with general Pseudo Amino Acid Compositions Sheraz Naseer, Rao Faizan Ali, Yaser Daanial Khan, P.D.D DominicCommunicated by Ramaswamy H. Sarma.
Fulltext Impact of Drug Induced Long QT Syndrome: A Systematic Review

Arunachalam K, Lakshmanan S, Maan A, Kumar N, Dominic P

J Clin Med Res, 2018 May;10(5):384-390.
PMID: 29581800 DOI: 10.14740/jocmr3338w

Background: Drug induced long QT syndrome is quite common in daily clinical practice but its impact is unknown.
Methods: PubMed and EMBASE databases (until May 2, 2017) were searched to identify studies reporting drug induced long QT syndrome and followed the PRISMA guidelines. The main outcomes measured in these studies were QTc prolongation, ventricular arrhythmias, torsade de pointes (TdP) and death.
Results: Out of 176 non-duplicate reports, 36 studies satisfied inclusion criteria and provided data on patients exposed to drugs that can potentially cause long QT. Totally, 14,756 patients were exposed and 930 patients (6.3%) were found to have QTc prolongation. The number of males was 6,400 and females were 5,723 patients. The mean age of the patients was 43.8 ± 9.36 years. Ventricular arrhythmias were found in 379 patients (2.6%), 26 patients were found to have premature atrial contractions (PACs) and premature ventricular contractions (PVCs). TdP was found in 49 patients (0.33 %), sudden cardiac death (SCD) was found in five patients and 586 patients were found to have all-cause mortality.
Conclusions: Around 6% of patients have risk of QT prolongation when exposed but only 0.3% developed TdP and 2.6% developed ventricular arrhythmias. Risk of developing arrhythmias is higher with concomitant use of multiple QT prolonging drugs.
Fulltext Genomic analysis of a pre-elimination Malaysian Plasmodium vivax population reveals selective pressures and changing transmission dynamics

Auburn S, Benavente ED, Miotto O, Pearson RD, Amato R, Grigg MJ, et al.

Nat Commun, 2018 Jul 03;9(1):2585.
PMID: 29968722 DOI: 10.1038/s41467-018-04965-4

The incidence of Plasmodium vivax infection has declined markedly in Malaysia over the past decade despite evidence of high-grade chloroquine resistance. Here we investigate the genetic changes in a P. vivax population approaching elimination in 51 isolates from Sabah, Malaysia and compare these with data from 104 isolates from Thailand and 104 isolates from Indonesia. Sabah displays extensive population structure, mirroring that previously seen with the emergence of artemisinin-resistant P. falciparum founder populations in Cambodia. Fifty-four percent of the Sabah isolates have identical genomes, consistent with a rapid clonal expansion. Across Sabah, there is a high prevalence of loci known to be associated with antimalarial drug resistance. Measures of differentiation between the three countries reveal several gene regions under putative selection in Sabah. Our findings highlight important factors pertinent to parasite resurgence and molecular cues that can be used to monitor low-endemic populations at the end stages of P. vivax elimination.
Fulltext Genomic Analysis of Plasmodium vivax in Southern Ethiopia Reveals Selective Pressures in Multiple Parasite Mechanisms

Auburn S, Getachew S, Pearson RD, Amato R, Miotto O, Trimarsanto H, et al.

J Infect Dis, 2019 Oct 22;220(11):1738-1749.
PMID: 30668735 DOI: 10.1093/infdis/jiz016

The Horn of Africa harbors the largest reservoir of Plasmodium vivax in the continent. Most of sub-Saharan Africa has remained relatively vivax-free due to a high prevalence of the human Duffy-negative trait, but the emergence of strains able to invade Duffy-negative reticulocytes poses a major public health threat. We undertook the first population genomic investigation of P. vivax from the region, comparing the genomes of 24 Ethiopian isolates against data from Southeast Asia to identify important local adaptions. The prevalence of the Duffy binding protein amplification in Ethiopia was 79%, potentially reflecting adaptation to Duffy negativity. There was also evidence of selection in a region upstream of the chloroquine resistance transporter, a putative chloroquine-resistance determinant. Strong signals of selection were observed in genes involved in immune evasion and regulation of gene expression, highlighting the need for a multifaceted intervention approach to combat P. vivax in the region.
Fulltext Genomic analysis of local variation and recent evolution in Plasmodium vivax

Pearson RD, Amato R, Auburn S, Miotto O, Almagro-Garcia J, Amaratunga C, et al.

Nat Genet, 2016 Aug;48(8):959-964.
PMID: 27348299 DOI: 10.1038/ng.3599

The widespread distribution and relapsing nature of Plasmodium vivax infection present major challenges for the elimination of malaria. To characterize the genetic diversity of this parasite in individual infections and across the population, we performed deep genome sequencing of >200 clinical samples collected across the Asia-Pacific region and analyzed data on >300,000 SNPs and nine regions of the genome with large copy number variations. Individual infections showed complex patterns of genetic structure, with variation not only in the number of dominant clones but also in their level of relatedness and inbreeding. At the population level, we observed strong signals of recent evolutionary selection both in known drug resistance genes and at new loci, and these varied markedly between geographical locations. These findings demonstrate a dynamic landscape of local evolutionary adaptation in the parasite population and provide a foundation for genomic surveillance to guide effective strategies for control and elimination of P. vivax.
Fulltext Malaria is a cause of iron deficiency in African children

Muriuki JM, Mentzer AJ, Mitchell R, Webb EL, Etyang AO, Kyobutungi C, et al.

Nat Med, 2021 Apr;27(4):653-658.
PMID: 33619371 DOI: 10.1038/s41591-021-01238-4

Malaria and iron deficiency (ID) are common and interrelated public health problems in African children. Observational data suggest that interrupting malaria transmission reduces the prevalence of ID1. To test the hypothesis that malaria might cause ID, we used sickle cell trait (HbAS, rs334 ), a genetic variant that confers specific protection against malaria2, as an instrumental variable in Mendelian randomization analyses. HbAS was associated with a 30% reduction in ID among children living in malaria-endemic countries in Africa (n = 7,453), but not among individuals living in malaria-free areas (n = 3,818). Genetically predicted malaria risk was associated with an odds ratio of 2.65 for ID per unit increase in the log incidence rate of malaria. This suggests that an intervention that halves the risk of malaria episodes would reduce the prevalence of ID in African children by 49%.
Fulltext An open dataset of Plasmodium vivax genome variation in 1,895 worldwide samples

MalariaGEN, Adam I, Alam MS, Alemu S, Amaratunga C, Amato R, et al.

Wellcome Open Res, 2022;7:136.
PMID: 35651694 DOI: 10.12688/wellcomeopenres.17795.1

This report describes the MalariaGEN Pv4 dataset, a new release of curated genome variation data on 1,895 samples of Plasmodium vivax collected at 88 worldwide locations between 2001 and 2017. It includes 1,370 new samples contributed by MalariaGEN and VivaxGEN partner studies in addition to previously published samples from these and other sources. We provide genotype calls at over 4.5 million variable positions including over 3 million single nucleotide polymorphisms (SNPs), as well as short indels and tandem duplications. This enlarged dataset highlights major compartments of parasite population structure, with clear differentiation between Africa, Latin America, Oceania, Western Asia and different parts of Southeast Asia. Each sample has been classified for drug resistance to sulfadoxine, pyrimethamine and mefloquine based on known markers at the dhfr, dhps and mdr1 loci. The prevalence of all of these resistance markers was much higher in Southeast Asia and Oceania than elsewhere. This open resource of analysis-ready genome variation data from the MalariaGEN and VivaxGEN networks is driven by our collective goal to advance research into the complex biology of P. vivax and to accelerate genomic surveillance for malaria control and elimination.
A molecular barcode and web-based data analysis tool to identify imported Plasmodium vivax malaria

Trimarsanto H, Amato R, Pearson RD, Sutanto E, Noviyanti R, Trianty L, et al.

Commun Biol, 2022 Dec 23;5(1):1411.
PMID: 36564617 DOI: 10.1038/s42003-022-04352-2

Traditionally, patient travel history has been used to distinguish imported from autochthonous malaria cases, but the dormant liver stages of Plasmodium vivax confound this approach. Molecular tools offer an alternative method to identify, and map imported cases. Using machine learning approaches incorporating hierarchical fixation index and decision tree analyses applied to 799 P. vivax genomes from 21 countries, we identified 33-SNP, 50-SNP and 55-SNP barcodes (GEO33, GEO50 and GEO55), with high capacity to predict the infection's country of origin. The Matthews correlation coefficient (MCC) for an existing, commonly applied 38-SNP barcode (BR38) exceeded 0.80 in 62% countries. The GEO panels outperformed BR38, with median MCCs > 0.80 in 90% countries at GEO33, and 95% at GEO50 and GEO55. An online, open-access, likelihood-based classifier framework was established to support data analysis (vivaxGEN-geo). The SNP selection and classifier methods can be readily amended for other use cases to support malaria control programs.
Fulltext Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition)

Klionsky DJ, Abdelmohsen K, Abe A, Abedin MJ, Abeliovich H, Acevedo Arozena A, et al.

Autophagy, 2016;12(1):1-222.
PMID: 26799652 DOI: 10.1080/15548627.2015.1100356