Modelling in Synthesis and Optimization of Active Vaccinal Components

Cancer is the second leading cause of mortality worldwide, behind heart diseases, accounting for 10 million deaths each year. This study focusses on adenocarcinoma, which is a target of a number of anticancer therapies presently being tested in medical and pharmaceutical studies. The innovative stud...

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Autores principales:	Oana-Constantina Margin, Eva-Henrietta Dulf, Teodora Mocan, Lucian Mocan
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	QSAR ALO ANFIS watershed segmentation nanomaterials vaccine anticancer physiology Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/959b00de32964c4b964ba9f103cb9f53
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spelling	oai:doaj.org-article:959b00de32964c4b964ba9f103cb9f532021-11-25T18:31:34ZModelling in Synthesis and Optimization of Active Vaccinal Components10.3390/nano111130012079-4991https://doaj.org/article/959b00de32964c4b964ba9f103cb9f532021-11-01T00:00:00Zhttps://www.mdpi.com/2079-4991/11/11/3001https://doaj.org/toc/2079-4991Cancer is the second leading cause of mortality worldwide, behind heart diseases, accounting for 10 million deaths each year. This study focusses on adenocarcinoma, which is a target of a number of anticancer therapies presently being tested in medical and pharmaceutical studies. The innovative study for a therapeutic vaccine comprises the investigation of gold nanoparticles and their influence on the immune response for the annihilation of cancer cells. The model is intended to be realized using Quantitative-Structure Activity Relationship (QSAR) methods, explicitly artificial neural networks combined with fuzzy rules, to enhance automated properties of neural nets with human perception characteristics. Image processing techniques such as morphological transformations and watershed segmentation are used to extract and calculate certain molecular characteristics from hyperspectral images. The quantification of single-cell properties is one of the key resolutions, representing the treatment efficiency in therapy of colon and rectum cancerous conditions. This was accomplished by using manually counted cells as a reference point for comparing segmentation results. The early findings acquired are conclusive for further study; thus, the extracted features will be used in the feature optimization process first, followed by neural network building of the required model.Oana-Constantina MarginEva-Henrietta DulfTeodora MocanLucian MocanMDPI AGarticleQSARALOANFISwatershed segmentationnanomaterials vaccineanticancer physiologyChemistryQD1-999ENNanomaterials, Vol 11, Iss 3001, p 3001 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	QSAR ALO ANFIS watershed segmentation nanomaterials vaccine anticancer physiology Chemistry QD1-999
spellingShingle	QSAR ALO ANFIS watershed segmentation nanomaterials vaccine anticancer physiology Chemistry QD1-999 Oana-Constantina Margin Eva-Henrietta Dulf Teodora Mocan Lucian Mocan Modelling in Synthesis and Optimization of Active Vaccinal Components
description	Cancer is the second leading cause of mortality worldwide, behind heart diseases, accounting for 10 million deaths each year. This study focusses on adenocarcinoma, which is a target of a number of anticancer therapies presently being tested in medical and pharmaceutical studies. The innovative study for a therapeutic vaccine comprises the investigation of gold nanoparticles and their influence on the immune response for the annihilation of cancer cells. The model is intended to be realized using Quantitative-Structure Activity Relationship (QSAR) methods, explicitly artificial neural networks combined with fuzzy rules, to enhance automated properties of neural nets with human perception characteristics. Image processing techniques such as morphological transformations and watershed segmentation are used to extract and calculate certain molecular characteristics from hyperspectral images. The quantification of single-cell properties is one of the key resolutions, representing the treatment efficiency in therapy of colon and rectum cancerous conditions. This was accomplished by using manually counted cells as a reference point for comparing segmentation results. The early findings acquired are conclusive for further study; thus, the extracted features will be used in the feature optimization process first, followed by neural network building of the required model.
format	article
author	Oana-Constantina Margin Eva-Henrietta Dulf Teodora Mocan Lucian Mocan
author_facet	Oana-Constantina Margin Eva-Henrietta Dulf Teodora Mocan Lucian Mocan
author_sort	Oana-Constantina Margin
title	Modelling in Synthesis and Optimization of Active Vaccinal Components
title_short	Modelling in Synthesis and Optimization of Active Vaccinal Components
title_full	Modelling in Synthesis and Optimization of Active Vaccinal Components
title_fullStr	Modelling in Synthesis and Optimization of Active Vaccinal Components
title_full_unstemmed	Modelling in Synthesis and Optimization of Active Vaccinal Components
title_sort	modelling in synthesis and optimization of active vaccinal components
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/959b00de32964c4b964ba9f103cb9f53
work_keys_str_mv	AT oanaconstantinamargin modellinginsynthesisandoptimizationofactivevaccinalcomponents AT evahenriettadulf modellinginsynthesisandoptimizationofactivevaccinalcomponents AT teodoramocan modellinginsynthesisandoptimizationofactivevaccinalcomponents AT lucianmocan modellinginsynthesisandoptimizationofactivevaccinalcomponents
_version_	1718411032499585024

Modelling in Synthesis and Optimization of Active Vaccinal Components

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