Malaria is an infection disease caused by plamodium parasite. Sporadic cases have not been observed since 2011, but imported cases still present owing to travel. In this study, we aimed to evaluate labotauary and clinical findings patients with malaria who were hospitalized and treated in our hospital.
The development of a blood-stage malaria vaccine has largely focused on the subunit approach. However, the limited success of this strategy, mainly due to antigenic polymorphism and the failure to maintain potent parasite-specific immune responses, indicates that other approaches must be considered. Whole parasite (WP) vaccines offer many advantages over sub-units; they represent every antigen on the organism, thus limiting the effects of antigenic polymorphism, and similarly they compensate for individual Immune-Response (Ir) gene-regulated non-responsiveness to any particular antigen. From a development perspective, they negate the need to identify and compare the relative efficacies of individual candidate antigens. WP vaccines induce protective immunity that is largely cell-mediated.
Glucose‐6‐phosphate dehydrogenase (G6PD) is an essential enzyme that protects red blood cells from oxidative damage. Although G6PD‐deficient alleles appear to confer a protective effect of malaria, the link with clinical protection against Plasmodium infection is conflicting.
Recently, a promising technique has come forward in field of radiation-agriculture in which the natural polysaccharides are modified into useful oligomers after depolymerization. Ionizing radiation technology is a simple, pioneering, eco-friendly, and single step degradation process which is used in exploiting the efficiency of the natural polysaccharides as plant growth promoters. Arsenic (As) is a noxious and toxic to growth and development of medicinal plants. Artemisinin is obtained from the leaves of Artemisia annua L., which is effective in the treatment of malaria.
Gliding motility and cell invasion are essential for the successful transmission of Plasmodium parasites. These processes rely on an acto-myosin motor located underneath the parasite plasma membrane. The Myosin A-tail interacting protein (MTIP) connects the class XIV myosin A (MyoA) to the gliding-associated proteins and is essential for assembly of the motor at the inner membrane complex.
Malaria is an infectious disease caused by parasitic protozoa in the Plasmodium genus. A complete understanding of the biology of these parasites is challenging in view of their need to switch between the vertebrate and insect hosts. The parasites are also capable of becoming highly motile and of remaining dormant for decades, depending on the stage of their life cycle.
Pyrethroid-impregnated bed nets have driven considerable reductions in malaria-associated morbidity and mortality in Africa since the beginning of the century1. The intense selection pressure exerted by bed nets has precipitated widespread and escalating resistance to pyrethroids in African Anopheles populations, threatening to reverse the gains that been made by malaria control2. Here we show that expression of a sensory appendage protein (SAP2), which is enriched in the legs, confers pyrethroid resistance to Anopheles gambiae.
Plasmodium falciparum transmission depends on mature gametocytes that can be ingested by mosquitoes taking a bloodmeal on human skin. Although gametocyte skin sequestration has long been hypothesized as important contributor to efficient malaria transmission, this has never been formally tested.
William L Hamilton and colleagues1 and Rob W van der Pluijm and colleagues2 described the genomic evolution of Plasmodium falciparum malaria and the spread of resistance in this species to dihydroartemisinin–piperaquine in Southeast Asia. Resistance in the region has been associated with crt polymorphisms, 1, 2, 3 copy number variations in plasmepsins, 1, 2, 3, 4, 5 and mdr1 genes.4, 5
Most previous studies adopted single traditional time series models to predict incidences of malaria. A single model cannot effectively capture all the properties of the data structure. However, a stacking architecture can solve this problem by combining distinct algorithms and models. This study compares the performance of traditional time series models and deep learning algorithms in malaria case prediction and explores the application value of stacking methods in the field of infectious disease prediction.