Activities of Quinoxaline, Nitroquinoxaline, and [1,2,4]Triazolo[4,3-a]quinoxaline Analogs of MMV007204 against Schistosoma mansoni.

The reliance on one drug, praziquantel, to treat the parasitic disease schistosomiasis in millions of people a year shows the need to further develop a pipeline of new drugs to treat this disease. Recently, an antimalarial quinoxaline derivative (MMV007204) from the Medicines for Malaria Venture (MMV) Malaria Box demonstrated promise against Schistosoma mansoni In this study, 47 synthesized compounds containing quinoxaline moieties were first assayed against the larval stage of this parasite, newly transformed schistosomula (NTS); of these, 16 killed over 70% NTS at 10 µM. Further testing against NTS and adult S. mansoni yielded three compounds with 50% inhibitory concentrations (IC50s) of ≤0.31 µM against adult S. mansoni and selectivity indices of ≥8.9. Administration of these compounds as a single oral dose of 400 mg/kg of body weight to S. mansoni-infected mice yielded only moderate worm burden reduction (WBR) (9.3% to 46.3%). The discrepancy between these compounds' good in vitro activities and their poor in vivo activities indicates that optimization of their pharmacokinetic properties may yield compounds with greater bioavailabilities and better antischistosomiasis activities in vivo.

Control of a humanoid robot by a noninvasive brain-computer interface in humans.

We describe a brain-computer interface for controlling a humanoid robot directly using brain signals obtained non-invasively from the scalp through electroencephalography (EEG). EEG has previously been used for tasks such as controlling a cursor and spelling a word, but it has been regarded as an unlikely candidate for more complex forms of control owing to its low signal-to-noise ratio. Here we show that by leveraging advances in robotics, an interface based on EEG can be used to command a partially autonomous humanoid robot to perform complex tasks such as walking to specific locations and picking up desired objects. Visual feedback from the robot's cameras allows the user to select arbitrary objects in the environment for pick-up and transport to chosen locations. Results from a study involving nine users indicate that a command for the robot can be selected from four possible choices in 5 s with 95% accuracy. Our results demonstrate that an EEG-based brain-computer interface can be used for sophisticated robotic interaction with the environment, involving not only navigation as in previous applications but also manipulation and transport of objects.