18002979. METHOD FOR DIAGNOSING RESPIRATORY PATHOGENS AND PREDICTING COVID-19 RELATED OUTCOMES simplified abstract (THE REGENTS OF THE UNIVERSITY OF COLORADO, A BODY CORPORATE)

METHOD FOR DIAGNOSING RESPIRATORY PATHOGENS AND PREDICTING COVID-19 RELATED OUTCOMES

Organization Name

THE REGENTS OF THE UNIVERSITY OF COLORADO, A BODY CORPORATE

Inventor(s)

KATHLEEN Barnes of ENGLEWOOD CO (US)

IVANA Yang of ENGLEWOOD CO (US)

CHRISTOPHER Gignoux of CASTLE ROCK CO (US)

RASIKA Mathias of AURORA CO (US)

PAUL Norman of AURORA CO (US)

ALEM Taye of SAN DIEGO CA (US)

RISHI Porecha of SAN DIEGO CA (US)

BRET Barnes of SAN DIEGO CA (US)

BRETT Peterson of BOULDER CO (US)

METHOD FOR DIAGNOSING RESPIRATORY PATHOGENS AND PREDICTING COVID-19 RELATED OUTCOMES - A simplified explanation of the abstract

This abstract first appeared for US patent application 18002979 titled 'METHOD FOR DIAGNOSING RESPIRATORY PATHOGENS AND PREDICTING COVID-19 RELATED OUTCOMES

Simplified Explanation

The invention is a DNA methylation-based platform and machine learning algorithms for diagnosing respiratory pathogens, including SARS-CoV-2, and predicting COVID-19 related outcomes.

• The platform utilizes DNA methylation patterns to identify the presence of viral infections, such as SARS-CoV-2, and determine if a subject has COVID-19.
• Machine learning algorithms are employed to predict if a subject with COVID-19 is likely to develop acute respiratory distress syndrome or multisystem inflammatory syndrome in children.

Potential Applications

This technology could be applied in healthcare settings for rapid and accurate diagnosis of respiratory pathogens, leading to timely treatment and containment of infectious diseases.

Problems Solved

This innovation addresses the challenges of accurately diagnosing viral infections, such as SARS-CoV-2, and predicting disease outcomes in patients with COVID-19, which can help healthcare providers make informed decisions regarding patient care.

Benefits

The use of DNA methylation-based platform and machine learning algorithms offers a non-invasive and efficient method for diagnosing respiratory pathogens and predicting COVID-19 related outcomes, ultimately improving patient outcomes and reducing the spread of infectious diseases.

Potential Commercial Applications

This technology has potential commercial applications in diagnostic laboratories, hospitals, and public health agencies for screening and monitoring respiratory infections, especially during outbreaks such as the COVID-19 pandemic.

Possible Prior Art

Prior art may include existing methods for diagnosing viral infections and predicting disease outcomes, such as PCR-based tests and clinical scoring systems. However, the combination of DNA methylation analysis and machine learning algorithms for this specific application may be novel.

How does this technology compare to existing diagnostic methods for respiratory pathogens?

This technology could be compared to traditional PCR-based tests and clinical scoring systems to evaluate its accuracy, efficiency, and cost-effectiveness in diagnosing respiratory pathogens and predicting COVID-19 related outcomes.

What are the potential limitations or challenges in implementing this technology in healthcare settings?

Challenges may include the need for specialized equipment and expertise for DNA methylation analysis, as well as the integration of machine learning algorithms into existing healthcare systems. Additionally, regulatory approval and data privacy concerns may need to be addressed before widespread adoption of this technology.

Original Abstract Submitted

Provided by the inventive concept is a DNA methylation-based platform, and machine learning algorithms, for diagnosing respiratory pathogens including SARS-CoV-2 and predicting COVID-19 related outcomes, and methods of using the same, such as in identifying the presence of a viral infection, such as a SARS-CoV-2 infection, determining whether a subject has COVID-19, and/or whether a subject with COVID-19 is likely to develop acute respiratory distress syndrome or multisystem inflammatory syndrome in children.