Title
Toward a fractalomic idiotype/anti-idiotypic
paradigm
Authors
Francesco Chiappelli1*&
Jaden Penhaskashi2**
Affiliation
1Dental Group of Sherman Oaks, CA 91403 (www.oliviacajulisdds.com);
2West Valley Dental Implant Center, Encino, CA 91316 (minimallyinvasiveperio.com)
Chiappelli.research@gmail.com
Article Type
Editorial
Date
Received September 5, 2022; Revised September 30, 2022; Accepted September 30, 2022, Published September 30, 2022
Abstract
The CoViD-19 pandemic has demonstrated the need for future developments in anti-viral immunology. We propose that artificial intelligence (AI) and machine learning, and in particular fractal analysis could play a crucial role in that context. Fractals - never-ending repeats of self-similar shapes whose composite tend to resemble the whole - are found in most natural biological structures including immunoglobulin and antigenic epitopes. Increased knowledge of the fractalomic properties of the idiotype/anti-idiotypic paradigm should help develop a novel and improved simplified artificial model of the immune system. Case in point, the regulation and dampening of antibodies as well as the synergetic recognition of an antigen by multiple idiotypes are both immune mechanisms that require further analysis. An enhanced understanding of these complexities could lead to better data analysis for novel vaccines to improve their sensitivity and specificity as well as open other new doors in the field of immunology.
Keywords
Corona Virus Disease 2019 (CoViD-19), Severe Acute Respiratory Syndrome Corona Virus-2 (SARS-CoV2), Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL) Vaccinology, Fractal Analysis, Fractal Dimension (D), Idiotype, Immunoglobulin (Ig), T Cell Receptor (TcR), Complementarity-Determining Region (CDR)
Citation
Chiappelli & Penhaskashi, Bioinformation 18(9): 730-733 (2022)
Edited by
P Kangueane
ISSN
0973-2063
Publisher
License
This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.
