Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0024530 (malaria)
 44,886 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

The objective of the study is to investigate the prevalence of malaria and HIV coinfection and assess the effect of HIV coinfection on malaria disease severity in malaria patients from the endemic area of Thailand along the Thai-Myanmar border. Blood samples were collected from a total of 867 patients with malaria (all species and severity) who attended Mae Tao clinic for migrant workers, Tak Province during 2005-2007 (439 samples), 2008-2010 (273 samples), and 2011-2013 (155 samples). The average prevalence rate of malaria and HIV coinfected cases in this malaria endemic area of the country during the three periods was 1.85%. HIV coinfection was observed only in samples with mono-infection of Plasmodium falciparum or Plasmodium vivax, with similar proportions (0.81 vs. 1.04%). Patients' admission parasite density, an indicator of disease severity, was significantly higher in cases with HIV coinfection observed during 2008-2010. Anemia was found at a significantly higher frequency in patients coinfected with malaria and HIV observed during 2005-2007 compared with those infected with malaria alone. No association was observed between malaria and HIV coinfection and gender, and infected malaria species during the three observation periods. Patients with malaria and HIV coinfection had a significantly lower hemoglobin level than those with malaria infection alone. In conclusion, the prevalence of malaria and HIV coinfection in population of the malaria endemic area along the Thai-Myanmar border is low. HIV coinfection tended to increase parasite density, an indicator of malaria disease severity.
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PMID:Prevalence of malaria and HIV coinfection and influence of HIV infection on malaria disease severity in population residing in malaria endemic area along the Thai-Myanmar border. 2572 46

Disease represents a specific case of malfunctioning within a complex system. Whereas it is often feasible to observe and possibly treat the symptoms of a disease, it is much more challenging to identify and characterize its molecular root causes. Even in infectious diseases that are caused by a known parasite, it is often impossible to pinpoint exactly which molecular profiles of components or processes are directly or indirectly altered. However, a deep understanding of such profiles is a prerequisite for rational, efficacious treatments. Modern omics methodologies are permitting large-scale scans of some molecular profiles, but these scans often yield results that are not intuitive and difficult to interpret. For instance, the comparison of healthy and diseased transcriptome profiles may point to certain sets of involved genes, but a host of post-transcriptional processes and regulatory mechanisms renders predictions regarding metabolic or physiological consequences of the observed changes in gene expression unreliable. Here we present proof of concept that dynamic models of metabolic pathway systems may offer a tool for interpreting transcriptomic profiles measured during disease. We illustrate this strategy with the interpretation of expression data of genes coding for enzymes associated with purine metabolism. These data were obtained during infections of rhesus macaques (Macaca mulatta) with the malaria parasite Plasmodium cynomolgi or P. coatneyi. The model-based interpretation reveals clear patterns of flux redistribution within the purine pathway that are consistent between the two malaria pathogens and are even reflected in data from humans infected with P. falciparum. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang.
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PMID:Metabolic modeling helps interpret transcriptomic changes during malaria. 2906 11