Lidocaine effects on acetylcholine-elicited currents from mouse superior cervical ganglion neurons

Lidocaine is a commonly used local anaesthetic that, besides blocking voltage-dependent Na+ channels, has multiple inhibitory effects on muscle-type nicotinic acetylcholine (ACh) receptors (nAChRs). In the present study, we have investigated the effects of lidocaine on ACh-elicited currents (IAChs) from cultured mouse superior cervical ganglion (SCG) neurons, which mainly express heteromeric α3β4 nAChRs. Neurons were voltage-clamped by using the perforated-patch method and IAChs were elicited by fast application of ACh (100-300 μM), either alone or in presence of lidocaine at different concentrations. IAChs were reversibly blocked by lidocaine in a concentration-dependent way (IC50 = 41 μM; nH close to 1) and the inhibition was, at least partially, voltage-dependent, indicating an open-channel blockade. Besides, lidocaine blocked resting (closed) nAChRs, as evidenced by the increased inhibition caused by a 12 s lidocaine application just before its co-application with the agonist, and also enhanced IAChs desensitisation, at concentrations close to the IC50. These results indicate that lidocaine has diverse inhibitory actions on neuronal heteromeric nAChRs resembling those previously reported for Torpedo (muscle-type) nAChRs ( Alberola-Die et al., 2011). The similarity of lidocaine actions on different subtypes of heteromeric nAChRs differs with the specific effects of other compounds, restricted to particular subtypes of nAChRs.

Automatic Abdominal Aortic Aneurysm segmentation in MR images

Abdominal Aortic Aneurism is a disease related to a weakening in the aortic wall that can cause a break in the aorta and the death. The detection of an unusual dilatation of a section of the aorta is an indicative of this disease. However, it is difficult to diagnose because it is necessary image diagnosis using computed tomography or magnetic resonance. An automatic diagnosis system would allow to analyze abdominal magnetic resonance images and to warn doctors if any anomaly is detected. We focus our research in magnetic resonance images because of the absence of ionizing radiation. Although there are proposals to identify this disease in magnetic resonance images, they need an intervention from clinicians to be precise and some of them are computationally hard. In this paper we develop a novel approach to analyze magnetic resonance abdominal images and detect the lumen and the aortic wall. The method combines different algorithms in two stages to improve the detection and the segmentation so it can be applied to similar problems with other type of images or structures. In a first stage, we use a spatial fuzzy C-means algorithm with morphological image analysis to detect and segment the lumen; and subsequently, in a second stage, we apply a graph cut algorithm to segment the aortic wall. The obtained results in the analyzed images are pretty successful obtaining an average of 79% of overlapping between the automatic segmentation provided by our method and the aortic wall identified by a medical specialist. The main impact of the proposed method is that it works in a completely automatic way with a low computational cost, which is of great significance for any expert and intelligent system.