Malaria is a vector-borne parasitic disease with a vast impact on human history, and according to the World Health Organisation, Plasmodium parasites still infect over 200 million people per year. Plasmodium falciparum, the deadliest parasite species, has a remarkable ability to undermine the host immune system and cause life-threatening disease during blood infection. The parasite's host cells, red blood cells (RBCs), generally maintain an asymmetric distribution of phospholipids in the two leaflets of the plasma membrane bilayer.
red blood cells
VAR2CSA is the placental-malaria-specific member of the antigenically variant Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1) family. It is expressed on the surface of Plasmodium falciparum-infected host red blood cells and binds to specific chondroitin-4-sulfate chains of the placental proteoglycan receptor. The functional ∼310 kDa ectodomain of VAR2CSA is a multidomain protein that requires a minimum 12-mer chondroitin-4-sulfate molecule for specific, high affinity receptor binding. However, it is not known how the individual domains are organized and interact to create the receptor-binding surface, limiting efforts to exploit its potential as an effective vaccine or drug target.
Metacytofilin (MCF) was isolated from the fungus Metarhizium sp. TA2759. Although MCF possesses anti-Toxoplasma activity, the effects of this compound against other parasites are unknown. Here, we evaluated the in vitro anti-malarial activity of MCF against the 3D7 strain and the chloroquine-resistant K1 strain of Plasmodium falciparum.
The mechanical properties of erythrocytes have been investigated by different techniques. However, there are few reports on how the viscoelasticity of these cells varies during malaria disease. Here, we quantitatively map the viscoelastic properties of Plasmodium falciparum-parasitized human erythrocytes. We apply new methodologies based on optical tweezers to measure the viscoelastic properties and defocusing microscopy to measure the erythrocyte height profile, the overall cell volume, and its form factor, a crucial parameter to convert the complex elastic constant into complex shear modulus.
Malarial disease caused by Plasmodium parasites challenges the mammalian immune system with a delicate balancing act. Robust inflammatory responses are required to control parasite replication within red blood cells, which if unchecked, can lead to severe anemia and fatality. However, the same inflammatory response that controls parasite replication is also associated with immunopathology and severe disease, as is exemplified by cerebral malaria.
The protozoan parasite Plasmodium falciparum causes the most severe form of human malaria and is estimated to kill 400,000 people a year. The parasite infects and replicates in host red blood cells (RBCs), where it expresses an array of proteases to carry out multiple essential processes. We are investigating the function of falcilysin (FLN), a protease known to be required for parasite development in the RBC.
Cerebral malaria (CM) is a life-threatening diffuse encephalopathy caused by Plasmodium falciparum, in which the destruction of the blood-brain barrier (BBB) is the main cause of death. However, increasing evidence has shown that antimalarial drugs, the current treatment for CM, do little to protect against CM-induced BBB damage. Therefore, a means to alleviate BBB dysfunction would be a promising adjuvant therapy for CM.
Artemisinin combination therapies are the current frontline therapy for falciparum malaria. Artemisinin is activated by heme iron, and the consequent production of reactive oxygen species and carbon-centered radicals results in rapid parasite clearance. Red blood cells (RBCs) from anemic iron-deficient individuals have decreased levels of heme, and such deficiencies are highly prevalent among children and pregnant women in malaria-endemic countries.
Computer-assisted algorithms have become a mainstay of biomedical applications to improve accuracy and reproducibility of repetitive tasks like manual segmentation and annotation. We propose a novel pipeline for red blood cell detection and counting in thin blood smear microscopy images, named RBCNet, using a dual deep learning architecture. RBCNet consists of a U-Net first stage for cell-cluster segmentation, followed by a second stage Faster R-CNN for detecting small cell objects within clusters, identified as connected components from the U-Net stage.
In malaria-naïve children and adults, Plasmodium falciparum -infected red blood cells ( Pf -iRBCs) trigger fever and other symptoms of systemic inflammation. However, in endemic areas where individuals experience repeated Pf infections over many years, the risk of Pf -iRBC-triggered inflammatory symptoms decreases with cumulative Pf exposure. The molecular mechanisms underlying these clinical observations remain unclear.