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Updated: Nov 12, 2025
Visualization of SARS-CoV-2 using Immuno RNA-Fluorescence In Situ Hybridization
Published on: December 23, 2020
Host-dependent molecular factors mediating SARS-CoV-2 infection to gain clinical insights for developing effective
Gowhar Shafi1, Shruti Desai2, Krithika Srinivasan2
1iNDX.Ai, Cupertino, CA, USA. gowhar.shafi@indx.ai.
Abstract:
Coronavirus disease 2019 (COVID-19), a recent viral pandemic that first began in December 2019, in Hunan wildlife market, Wuhan, China. The infection is caused by a coronavirus, SARS-CoV-2 and clinically characterized by common symptoms including fever, dry cough, loss of taste/smell, myalgia and pneumonia in severe cases. With overwhelming spikes in infection and death, its pathogenesis yet remains elusive. Since the infection spread rapidly, its healthcare demands are overwhelming with uncontrollable emergencies. Although laboratory testing and analysis are developing at an enormous pace, the high momentum of severe cases demand more rapid strategies for initial screening and patient stratification. Several molecular biomarkers like C-reactive protein, interleukin-6 (IL6), eosinophils and cytokines, and artificial intelligence (AI) based screening approaches have been developed by various studies to assist this vast medical demand. This review is an attempt to collate the outcomes of such studies, thus highlighting the utility of AI in rapid screening of molecular markers along with chest X-rays and other COVID-19 symptoms to enable faster diagnosis and patient stratification. By doing so, we also found that molecular markers such as C-reactive protein, IL-6 eosinophils, etc. showed significant differences between severe and non-severe cases of COVID-19 patients. CT findings in the lungs also showed different patterns like lung consolidation significantly higher in patients with poor recovery and lung lesions and fibrosis being higher in patients with good recovery. Thus, from these evidences we perceive that an initial rapid screening using integrated AI approach could be a way forward in efficient patient stratification.
Insights
Artificial intelligence (AI) can rapidly screen molecular markers and chest X-rays for COVID-19 diagnosis. This approach aids in faster patient stratification, distinguishing severe cases from non-severe ones.
Area of Science:
- Infectious Diseases
- Medical Diagnostics
- Artificial Intelligence in Healthcare
Background:
- Coronavirus disease 2019 (COVID-19) emerged in December 2019, causing a global pandemic with significant mortality.
- The pathogenesis of SARS-CoV-2 remains elusive, and rapid patient stratification is crucial due to overwhelming healthcare demands.
- Existing diagnostic methods require enhancement for faster screening and management of severe COVID-19 cases.
Purpose of the Study:
- To review and collate studies on molecular biomarkers and AI for COVID-19 screening.
- To highlight the utility of artificial intelligence in analyzing molecular markers, chest X-rays, and symptoms for rapid diagnosis.
- To assess the effectiveness of AI in stratifying COVID-19 patients based on disease severity.
Main Methods:
- Literature review of studies utilizing molecular markers (e.g., C-reactive protein, IL-6, eosinophils) and AI for COVID-19.
- Analysis of AI-based screening approaches integrating clinical symptoms, chest X-rays, and molecular data.
- Comparison of findings between severe and non-severe COVID-19 patient groups.
Main Results:
- Molecular markers like C-reactive protein, IL-6, and eosinophils show significant differences between severe and non-severe COVID-19 cases.
- CT scan findings, including lung consolidation and lesions, correlate with patient recovery status.
- AI demonstrated potential in rapidly screening molecular markers and imaging data for diagnosis and stratification.
Conclusions:
- Integrated AI approaches show promise for efficient and rapid patient stratification in COVID-19.
- AI-assisted screening of molecular markers and imaging can expedite diagnosis and improve patient management.
- Further development of AI tools is essential for addressing the challenges posed by the COVID-19 pandemic.
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