ArticleViruses2022
SARS-CoV-2 Morphometry Analysis and Prediction of Real Virus Levels Based on Full Recurrent Neural Network Using TEM Images.
Article in Viruses, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed.
- Article
- Nanomaterial-Enhanced Electrochemical Sensors for Clinical Monitoring of Acyclovir: Integration Into Molecular Diagnostics.Journal of clinical laboratory analysis · 2025Review
- Electron Tomography as a Tool to Study SARS-CoV-2 Morphology.International journal of molecular sciences · 2024Review
- Photonics-powered augmented reality skin electronics for proactive healthcare: multifaceted opportunities.Mikrochimica acta · 2024Review
- Next-generation nanophotonic-enabled biosensors for intelligent diagnosis of SARS-CoV-2 variants.The Science of the total environment · 2023Review
- Targeted delivery of paclitaxel drug using polymer-coated magnetic nanoparticles for fibrosarcoma therapy: in vitro and in vivo studies.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The SARS-CoV-2 virus is responsible for the rapid global spread of the COVID-19 disease. As a result, it is critical to understand and collect primary data on the virus, infection epidemiology, and treatment. Despite the speed with which the virus was detected, studies of its cell biology and architecture at the ultrastructural level are still in their infancy. Therefore, we investigated and analyzed the viral morphometry of SARS-CoV-2 to extract important key points of the virus's characteristics. Then, we proposed a prediction model to identify the real virus levels based on the optimization of a full recurrent neural network (RNN) using transmission electron microscopy (TEM) images. Consequently, identification of virus levels depends on the size of the morphometry of the area (width, height, circularity, roundness, aspect ratio, and solidity). The results of our model were an error score of training network performance 3.216 × 10
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