Human Lymphocytic choriomeningitis virus Antibody ELISA Kit

Arenaviridae is a viral family whose members are associated with rodent-transmitted infections to humans responsible of severe diseases. The current lack of a vaccine and limited therapeutic options make the development of efficacious drugs of high priority. The cap-snatching mechanism of transcription of Arenavirus performed by the endonuclease domain of the L-protein is unique and essential, so we developed a drug design program targeting the endonuclease activity of the prototypic Lymphocytic ChorioMeningitis Virus. Since the endonuclease activity is metal ion dependent, we designed a library of compounds bearing chelating motifs (diketo acids, polyphenols, and N-hydroxyisoquinoline-1,3-diones) able to block the catalytic center through the chelation of the critical metal ions, resulting in a functional impairment. We pre-screened 59 compounds by Differential Scanning Fluorimetry. Then, we characterized the binding affinity by Microscale Thermophoresis and evaluated selected compounds in in vitro and in cellula assays. We found several potent binders and inhibitors of the endonuclease activity. This study validates the proof of concept that the endonuclease domain of Arenavirus can be used as a target for anti-arena-viral drug discovery and that both diketo acids and N-hydroxyisoquinoline-1,3-diones can be considered further as potential metal-chelating pharmacophores.

Brain source imaging based on EEG aims to reconstruct the neural activities producing the scalp potentials. This includes solving the forward and inverse problems. The aim of the inverse problem is to estimate the activity of the brain sources based on the measured data and leadfield matrix computed in the forward step. Spatial filtering, also known as beamforming, is an inverse method that reconstructs the time course of the source at a particular location by weighting and linearly combining the sensor data. In this paper, we considered a temporal assumption related to the time course of the source, namely sparsity, in the Linearly Constrained Minimum Variance (LCMV) beamformer. This assumption sounds reasonable since not all brain sources are active all the time such as epileptic spikes and also some experimental protocols such as electrical stimulations of a peripheral nerve can be sparse in time. Developing the sparse beamformer is done by incorporating L1-norm regularization of the beamformer output in the relevant cost function while obtaining the filter weights. We called this new beamformer SParse LCMV (SP-LCMV). We compared the performance of the SP-LCMV with that of LCMV for both superficial and deep sources with different amplitudes using synthetic EEG signals. Also, we compared them in localization and reconstruction of sources underlying electric median nerve stimulation. Results show that the proposed sparse beamformer can enhance reconstruction of sparse sources especially in the case of sources with high amplitude spikes.