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Volume 28
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Med. Sci. Forum, 2024, ECCM 2024

The 2nd International Electronic Conference on Clinical Medicine
Online | 13–15 November 2024

Volume Editors:
Emmanuel Andrès, University Hospital Strasbourg, France
Kent Doi, University of Tokyo, Japan

Number of Papers: 2
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Cover Story (view full-size image): This conference presented the latest studies in various fields of clinical medicine. The goal was to show the current state, challenges, opportunities, and future trends pertaining to the following [...] Read more.
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40 pages, 398 KiB  
Conference Report
Abstracts of the 2nd International Electronic Conference on Clinical Medicine (ECCM 2024)
by Emmanuel Andrès and Kent Doi
Med. Sci. Forum 2024, 28(1), 1; https://doi.org/10.3390/msf2024028001 - 22 Nov 2024
Viewed by 1081
Abstract
The 2nd International Electronic Conference on Clinical Medicine: Chronic Diseases—Current State and Future Trends (ECCM 2024) was organized by MDPI and MDPI Journal of Clinical Medicine and took place online from 13 to 15 November 2024. This conference report is an abstract collection [...] Read more.
The 2nd International Electronic Conference on Clinical Medicine: Chronic Diseases—Current State and Future Trends (ECCM 2024) was organized by MDPI and MDPI Journal of Clinical Medicine and took place online from 13 to 15 November 2024. This conference report is an abstract collection from different sessions of ECCM 2024. Full article
(This article belongs to the Proceedings of The 2nd International Electronic Conference on Clinical Medicine)
8 pages, 1287 KiB  
Proceeding Paper
Modeling Electrical Potential in Multi-Dendritic Neurons Using Bessel Functions
by Kaouther Selmi, Souhaila Khalfallah and Kais Bouallegue
Med. Sci. Forum 2024, 28(1), 2; https://doi.org/10.3390/msf2024028002 - 20 Mar 2025
Viewed by 151
Abstract
Understanding the distribution of electrical potential within neurons is critical for advancing our comprehension of neuronal signaling and communication. Neurons, the fundamental units of the nervous system, rely on complex electrochemical processes to transmit information. The intricate structure of neurons, especially those with [...] Read more.
Understanding the distribution of electrical potential within neurons is critical for advancing our comprehension of neuronal signaling and communication. Neurons, the fundamental units of the nervous system, rely on complex electrochemical processes to transmit information. The intricate structure of neurons, especially those with multiple dendrites, plays a crucial role in how these electrical signals are generated, propagated, and integrated. Despite significant progress in neuroscience, accurately modeling the electrical potential within neurons with elaborate dendritic architectures remains a challenge. This article introduces a novel approach to modeling the electrical potential in multi-dendritic neurons using Bessel functions, which offers a more precise and detailed representation of these processes. The proposed method involves solving the electric potential diffusion equation in cylindrical coordinates, a mathematical framework that naturally aligns with the geometry of dendrites. The radial and axial components of the solution are expressed using Bessel functions and sinusoidal functions, respectively. Bessel functions are particularly well-suited for this purpose due to their ability to describe waveforms in cylindrical systems, making them ideal for capturing the spatial variations in electrical potential within the cylindrical shape of dendrites. By leveraging this mathematical approach, we obtain a complete representation of the potential distribution across the neuron, from the soma (cell body) through the dendrites to the synaptic terminals. This model accurately captures the spatial variations of electrical potential in different regions of the neuron, including areas with complex dendritic arborizations, which are branching structures that significantly influence the neuron’s electrical characteristics. Simulation results underscore the effectiveness of this approach in reproducing realistic neuronal behavior. The model successfully mimics the way electrical signals propagate and interact within dendritic structures, providing crucial insights into the underlying mechanisms of signal integration and transmission in neurons. Full article
(This article belongs to the Proceedings of The 2nd International Electronic Conference on Clinical Medicine)
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