The key glycolytic enzyme phosphofructokinase is involved in resistance to antiplasmodial glycosides

ABSTRACT Plasmodium parasites rely heavily on glycolysis for ATP production and for precursors for essential anabolic pathways, such as the methylerythritol phosphate (MEP) pathway. Here, we show that mutations in the Plasmodium falciparum glycolytic enzyme, phosphofructokinase (PfPFK9), are associated with in vitro resistance to a primary sulfonamide glycoside (PS-3). Flux through the upper glycolysis pathway was significantly reduced in PS-3-resistant parasites, which was associated with reduced ATP levels but increased flux into the pentose phosphate pathway. PS-3 may directly or indirectly target enzymes in these pathways, as PS-3-treated parasites had elevated levels of glycolytic and tricarboxylic acid (TCA) cycle intermediates. PS-3 resistance also led to reduced MEP pathway intermediates, and PS-3-resistant parasites were hypersensitive to the MEP pathway inhibitor, fosmidomycin. Overall, this study suggests that PS-3 disrupts core pathways in central carbon metabolism, which is compensated for by mutations in PfPFK9, highlighting a novel metabolic drug resistance mechanism in P. falciparum. IMPORTANCE Malaria, caused by Plasmodium parasites, continues to be a devastating global health issue, causing 405,000 deaths and 228 million cases in 2018. Understanding key metabolic processes in malaria parasites is critical to the development of new drugs to combat this major infectious disease. The Plasmodium glycolytic pathway is essential to the malaria parasite, providing energy for growth and replication and supplying important biomolecules for other essential Plasmodium anabolic pathways. Despite this overreliance on glycolysis, no current drugs target glycolysis, and there is a paucity of information on critical glycolysis targets. Our work addresses this unmet need, providing new mechanistic insights into this key pathway

Neural network modelling of RC deep beam shear strength

YesA 9 x 18 x 1 feed-forward neural network (NN) model trained using a resilient back-propagation algorithm and early stopping technique is constructed to predict the shear strength of deep reinforced concrete beams. The input layer covering geometrical and material properties of deep beams has nine neurons, and the corresponding output is the shear strength. Training, validation and testing of the developed neural network have been achieved using a comprehensive database compiled from 362 simple and 71 continuous deep beam specimens. The shear strength predictions of deep beams obtained from the developed NN are in better agreement with test results than those determined from strut-and-tie models. The mean and standard deviation of the ratio between predicted capacities using the NN and measured shear capacities are 1.028 and 0.154, respectively, for simple deep beams, and 1.0 and 0.122, respectively, for continuous deep beams. In addition, the trends ascertained from parametric study using the developed NN have a consistent agreement with those observed in other experimental and analytical investigations

Load capacity of reinforced concrete continuous deep beams

Most codes of practice, such as EC2 and ACI 318-05, recommend the use of strut-and-tie models for the design of reinforced concrete deep beams. However, studies on the validity of the strut-and-tie models for continuous deep beams are rare. This paper evaluates the strut-and-tie model specified by ACI 318-05 and mechanism analysis of the plasticity theory in predicting the load capacity of 75 reinforced concrete continuous deep beams tested in the literature. The influence of such main parameters as compressive strength of concrete, shear span-to-overall depth ratio, main longitudinal bottom reinforcement, and shear reinforcement on the load capacity is also investigated using both methods and experimental results. Experimental results were closer to the predictions obtained from the mechanism analysis than the strut-and-tie model. The strut-and-tie model highly overestimated the load capacity of continuous deep beams without shear reinforcement