Tenidap

Tenidap, a selective human inwardly rectifying potassium (Kir) 2.3 channel opener, has been reported to have antiepileptic effect in the pilocarpine temporal lobe epilepsy rat model in our previous study. However, the effect of tenidap on neurons and its relationship with the epileptiform bursting charges in neuron is still required to be explored.

In this study, cyclothiazide (CTZ) induced cultured hippocampal neuron epileptic model was used to study the antiepileptic effect of tenidap and the relationship between Kir2.3 channel and the neuronal epileptiform burst.

Patch clamp recording showed that both acute (2h) and chronic (48h) CTZ pre-treatment all significantly induced robust epileptiform burst activities in cultured hippocampal neurons, and tenidap acutely application inhibited this highly synchronized abnormal activities. The effect of tenidap is likely due to increased activity of Kir2.3 channels, since tenidap significantly enhanced kir current recorded from those neurons. In addition, neurons overexpressing Kir2.3 channels, by transfection with Kir2.3 plasmid, showed a significant large increase of the Kir current, prevented CTZ treatment to induce epileptiform burst discharge.

Our current study demonstrated that over activation of Kir2.3 channel in hippocampal neurons could positively interference with epileptiform burst activities, and tenidap, as a selective Kir2.3 channel opener, could be a potential candidate for seizure therapy.

Arterial Spin Labelling (ASL) imaging derives a perfusion image by tracing the accumulation of magnetically labeled blood water in the brain. As the image generated has an intrinsically low signal to noise ratio (SNR), multiple measurements are routinely acquired and averaged, at a penalty of increased scan duration and opportunity for motion artefact. However, this strategy alone might be ineffective in clinical settings where the time available for acquisition is limited and patient motion are increased. This study investigates the use of an Independent Component Analysis (ICA) approach for denoising ASL data, and its potential for automation. 72 ASL datasets (pseudo-continuous ASL; 5 different post-labeling delays: 400, 800, 1200, 1600, 2000 m s; total volumes = 60) were collected from thirty consecutive acute stroke patients. The effects of ICA-based denoising (manual and automated) where compared to two different denoising approaches, aCompCor, a Principal Component-based method, and Enhancement of Automated Blood Flow Estimates (ENABLE), an algorithm based on the removal of corrupted volumes. Multiple metrics were used to assess the changes in the quality of the data following denoising, including changes in cerebral blood flow (CBF) and arterial transit time (ATT), SNR, and repeatability. Additionally, the relationship between SNR and number of repetitions acquired was estimated before and after denoising the data. The use of an ICA-based denoising approach resulted in significantly higher mean CBF and ATT values (p < 0.001), lower CBF and ATT variance (p < 0.001), increased SNR (p < 0.001), and improved repeatability (p < 0.05) when compared to the raw data. The performance of manual and automated ICA-based denoising was comparable. These results went beyond the effects of aCompCor or ENABLE. Following ICA-based denoising, the SNR was higher using only 50% of the ASL-dataset collected than when using the whole raw data. The results show that ICA can be used to separate signal from noise in ASL data, improving the quality of the data collected. In fact, this study suggests that the acquisition time could be reduced by 50% without penalty to data quality, something that merits further study. Independent component classification and regression can be carried out either manually, following simple criteria, or automatically.