Plasmodium falciparum Adhesion on Human Brain Microvascular Endothelial Cells Involves Transmigration-Like Cup Formation and Induces Opening of Intercellular Junctions

Cerebral malaria, a major cause of death during malaria infection, is characterised by the sequestration of infected red blood cells (IRBC) in brain microvessels. Most of the molecules implicated in the adhesion of IRBC on endothelial cells (EC) are already described; however, the structure of the IRBC/EC junction and the impact of this adhesion on the EC are poorly understood. We analysed this interaction using human brain microvascular EC monolayers co-cultured with IRBC. Our study demonstrates the transfer of material from the IRBC to the brain EC plasma membrane in a trogocytosis-like process, followed by a TNF-enhanced IRBC engulfing process. Upon IRBC/EC binding, parasite antigens are transferred to early endosomes in the EC, in a cytoskeleton-dependent process. This is associated with the opening of the intercellular junctions. The transfer of IRBC antigens can thus transform EC into a target for the immune response and contribute to the profound EC alterations, including peri-vascular oedema, associated with cerebral malaria

Cyanobacterial lipopolysaccharides and human health – a review

Cyanobacterial lipopolysaccharide/s (LPS) are frequently cited in the cyanobacteria literature as toxins responsible for a variety of heath effects in humans, from skin rashes to gastrointestinal, respiratory and allergic reactions. The attribution of toxic properties to cyanobacterial LPS dates from the 1970s, when it was thought that lipid A, the toxic moiety of LPS, was structurally and functionally conserved across all Gram-negative bacteria. However, more recent research has shown that this is not the case, and lipid A structures are now known to be very different, expressing properties ranging from LPS agonists, through weak endotoxicity to LPS antagonists. Although cyanobacterial LPS is widely cited as a putative toxin, most of the small number of formal research reports describe cyanobacterial LPS as weakly toxic compared to LPS from the Enterobacteriaceae. We systematically reviewed the literature on cyanobacterial LPS, and also examined the much lager body of literature relating to heterotrophic bacterial LPS and the atypical lipid A structures of some photosynthetic bacteria. While the literature on the biological activity of heterotrophic bacterial LPS is overwhelmingly large and therefore difficult to review for the purposes of exclusion, we were unable to find a convincing body of evidence to suggest that heterotrophic bacterial LPS, in the absence of other virulence factors, is responsible for acute gastrointestinal, dermatological or allergic reactions via natural exposure routes in humans. There is a danger that initial speculation about cyanobacterial LPS may evolve into orthodoxy without basis in research findings. No cyanobacterial lipid A structures have been described and published to date, so a recommendation is made that cyanobacteriologists should not continue to attribute such a diverse range of clinical symptoms to cyanobacterial LPS without research confirmation

Empirical versus mechanistic modelling: Comparison of an artificial neural network to a mechanistically based model for quantitative structure pharmacokinetic relationships of a homologous series of barbiturates

The aim of the current study was to compare the predictive performance of a mechanistically based model and an empirical artificial neural network (ANN) model to describe the relationship between the tissue-to-unbound plasma concentration ratios (Kpu's) of 14 rat tissues and the lipophilicity (LogP) of a series of nine 5-n-alkyl-5-ethyl barbituric acids. The mechanistic model comprised the water content, binding capacity, number of the binding sites, and binding association constant of each tissue. A backpropagation ANN with 2 hidden layers (33 neurons in the first layer, 9 neurons in the second) was used for the comparison. The network was trained by an algorithm with adaptive momentum and learning rate, programmed using the ANN Toolbox of MATLAB. The predictive performance of both models was evaluated using a leave-one-out procedure and computation of both the mean prediction error (ME, showing the prediction bias) and the mean squared prediction error (MSE, showing the prediction accuracy). The ME of the mechanistic model was 18% (range, 20 to 57%), indicating a tendency for overprediction; the MSE is 32% (range, 6 to 104%). The ANN had almost no bias: the ME was 2% (range, 36 to 64%) and had greater precision than the mechanistic model, MSE 18% (range, 4 to 70%). Generally, neither model appeared to be a significantly better predictor of the Kpu's in the rat