View source for 18263503. SYSTEMS AND METHODS FOR DIAGNOSING NEURODEGENERATIVE DISEASES simplified abstract (Arizona Board of Regents on Behalf of Arizona State University)

=SYSTEMS AND METHODS FOR DIAGNOSING NEURODEGENERATIVE DISEASES=


==Organization Name==

[[:Category:Arizona Board of Regents on Behalf of Arizona State University|Arizona Board of Regents on Behalf of Arizona State University]]


[[Category:Arizona Board of Regents on Behalf of Arizona State University]]

==Inventor(s)==

[[:Category:Carol J. Huseby of Tempe AZ (US)|Carol J. Huseby of Tempe AZ (US)]][[Category:Carol J. Huseby of Tempe AZ (US)]]

[[:Category:Paul Coleman of Fountain Hills AZ (US)|Paul Coleman of Fountain Hills AZ (US)]][[Category:Paul Coleman of Fountain Hills AZ (US)]]

==SYSTEMS AND METHODS FOR DIAGNOSING NEURODEGENERATIVE DISEASES - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18263503 titled 'SYSTEMS AND METHODS FOR DIAGNOSING NEURODEGENERATIVE DISEASES

==Simplified Explanation==

The abstract describes a patent application for a machine learning model that can select transcripts in blood to distinguish neurodegenerative diseases based on mRNA gene expression changes.

* The processor is configured to implement a machine learning model.
* The model is trained to select transcripts in blood for distinguishing neurodegenerative diseases.
* The algorithm leverages blood-based changes in mRNA gene expression.
* The goal is to differentiate patients with any neurodegenerative disease, regardless of specific proteins or post-translational modifications.

== Potential Applications ==
This technology could be applied in medical diagnostics to aid in the early detection and differentiation of neurodegenerative diseases.

== Problems Solved ==
This technology addresses the challenge of accurately diagnosing neurodegenerative diseases using blood-based biomarkers, which can be less invasive and more accessible than traditional methods.

== Benefits ==
The benefits of this technology include early detection of neurodegenerative diseases, personalized treatment plans, and potentially improved patient outcomes.

== Potential Commercial Applications ==
One potential commercial application of this technology could be the development of a diagnostic tool for healthcare providers to use in identifying neurodegenerative diseases.

== Possible Prior Art ==
Prior research may exist on using machine learning models for analyzing gene expression patterns in blood samples to diagnose various diseases.

=== What are the limitations of this technology in real-world applications? ===

This article does not address the scalability of the technology for widespread use in clinical settings or potential challenges in integrating it into existing diagnostic workflows.

=== How does this technology compare to traditional methods of diagnosing neurodegenerative diseases? ===

This article does not provide a direct comparison between the accuracy, cost-effectiveness, or efficiency of this technology compared to traditional diagnostic methods like imaging or cerebrospinal fluid analysis.


==Original Abstract Submitted==

A processor is configured to implement a machine learning model that is trained to select transcripts in blood for distinguishing neurodegenerative diseases. The algorithm is developed via machine learning and leverages concepts associated with blood-based changes in mRNA gene expression for differentiating patients of any neurodegenerative disease regardless of the proteins or their post-translational modifications occurring in disease.



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