The effect of glutamate and N-methyl-d-aspartate (NMDA) on electrical signalling - action potentials (AP) and excitation current transients - was studied in intact macrophyte Nitellopsis obtusa (Characeaen) internodal cell. Intracellular glass electrode recordings of single cell in current clamp and two-electrode voltage clamp modes indicate that glutamate (Glu, 0.1-1.0 mM) and NMDA (0.01-1.0 mM) increase electrically induced AP amplitude by hyperpolarising excitation threshold potential (Eth) and prolong AP fast repolarisation phase. Amplitude of Cl- current transient, as well as its activation and inactivation durations were also increased. Both Glu and NMDA act in a dose-dependent manner. The effect of NMDA exceeds that of Glu. Ionotropic glutamate receptor inhibitors AP-5 (NMDA-type receptors) and DNQX (AMPA/Kainate-type) have no effect on Nitellopsis cell electrical signalling per se, yet robustly inhibit excitatory effect of NMDA. This study reinforces NMDA as an active component in glutamatergic signalling at least in some plants and stresses the elaborate fine-tuning of electrical signalling.
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http://dx.doi.org/10.1071/FP20074 | DOI Listing |
Med Biol Eng Comput
January 2025
School of Control Science and Engineering, Tiangong University, Tianjin, 300387, China.
With the advancement of artificial intelligence technology, more and more effective methods are being used to identify and classify Electroencephalography (EEG) signals to address challenges in healthcare and brain-computer interface fields. The applications and major achievements of Graph Convolution Network (GCN) techniques in EEG signal analysis are reviewed in this paper. Through an exhaustive search of the published literature, a module-by-module discussion is carried out for the first time to address the current research status of GCN.
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December 2024
School of Food Science and Technology, State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, 214122, Jiangsu, China.
Bio-nanopore technology holds great promise in biomacromolecule detection, with its high throughput and low cost positioning it as an ideal detection tool. This technology employs a unique detection mechanism that utilizes nanoscale pores to rapidly and sensitively convert biological molecules interactions into electrical signals, enabling real-time, single-molecule detection with exceptional sensitivity. This review focuses on the latest advancements in this technology across various domains, including DNA and RNA sequencing, protein detection, and small molecule identification.
View Article and Find Full Text PDFAbdom Radiol (NY)
January 2025
AGH University of Krakow, Krakow, Poland.
Purpose: Shear wave elastography (SWE) provides a means for adding information about the mechanical properties of tissues to a diagnostic ultrasound examination. It is important to understand the physics and methods by which the measurements are made to aid interpretation of the results as they relate to disease processes.
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J Phys Chem Lett
January 2025
Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States.
Dark-field and confocal approaches to circular dichroism (CD) spectroscopy of uniaxial thin films examine the relationship between symmetry and incoherence in the nonreciprocal CD response, or the component that is antisymmetric about the light propagation direction. Modifying a conventional CD spectrometer for low-angle scattering detection isolates incoherent contributions to nonreciprocal CD of drop-cast thin films, boasting 5-to-10-fold enhancements in CD dissymmetry parameters. Conversely, confocal detection suppresses the nonreciprocal CD response.
View Article and Find Full Text PDFAnal Chem
January 2025
Key Laboratory of Nondestructive Test (Ministry of Education), Nanchang Hangkong University, Nanchang 330063, China.
Off-axis integrated cavity output spectroscopy (OA-ICOS) allows the laser to be reflected multiple times inside the cavity, increasing the effective absorption path length and thus improving sensitivity. However, OA-ICOS systems are affected by various types of noise, and traditional filtering methods offer low processing efficiency and perform limited feature extraction. Deep learning models enable us to extract important features from large-scale, complex spectral data and analyze them efficiently and accurately.
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