dc.contributor.author | Nieto-Chaupis, Huber | |
dc.date.accessioned | 2024-04-05T14:32:36Z | |
dc.date.available | 2024-04-05T14:32:36Z | |
dc.date.issued | 2023 | |
dc.identifier.uri | https://hdl.handle.net/20.500.13067/3087 | |
dc.description.abstract | It is well-known the abundance of Glutamate in brain, that plays the role as neurotransmitter and it is essential for the learning and memory. Because this neurotransmitters have a negative electric charge, then them can be affected by external electromagnetic fields. When neural synapse acquires anomalous behavior, the one can anticipate that electrodynamics might restore the correct synapse action. Therefore one can expect that advanced chips such as Neuralink turned out to be marketable, then various technological opportunities would emerge. In this manner, as an alternative to the Internet of Bio-nano Things (IBNT), a novel Internet of NanoMedicine Things (INMT) based at implanted chips might be an interesting medical methodology that aims to detain the progress of neurodegenerative disorders. Thus, one might to expect fast improvement in comparison to pharmacological schemes that might to add toxicity to patients. In this paper, from the fact that neurotransmiters carry electric charge, then it is presented a physics-based model that would sustain the INMT. | es_PE |
dc.format | application/pdf | es_PE |
dc.language.iso | eng | es_PE |
dc.publisher | IEEE | es_PE |
dc.rights | info:eu-repo/semantics/restrictedAccess | es_PE |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | es_PE |
dc.subject | Electrodynamics | es_PE |
dc.subject | Neurotransmitters | es_PE |
dc.subject | Toxicology | es_PE |
dc.subject | Mathematical models | es_PE |
dc.subject | Pharmacology | es_PE |
dc.subject | Internet | es_PE |
dc.subject | Behavioral sciences | es_PE |
dc.title | Physics Fundamentals of an Internet of Nanomedicine Things | es_PE |
dc.type | info:eu-repo/semantics/article | es_PE |
dc.identifier.journal | 2023 IEEE International Conference on Big Data (BigData) | es_PE |
dc.subject.ocde | https://purl.org/pe-repo/ocde/ford#2.02.04 | es_PE |
dc.relation.url | https://doi.org/10.1109/BigData59044.2023.10386725 | es_PE |