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An Approach to Point-Of-Care Ultrasound Evaluation of the Abdominal Aorta
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Machine-Learning-Assisted Catalytic Sensor Array for Rapid Diagnosis of Aortic Dissection.

Siyun Guo1,2, Yukang Wen3, Xiang Zhou3

  • 1Department of Cardiology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.

Analytical Chemistry
|July 8, 2026
PubMed
Summary

A novel sensor array utilizing manganese porphyrin and machine learning accurately diagnoses acute aortic dissection (AD) from plasma. This rapid diagnostic tool offers improved accuracy for cardiovascular emergency triage.

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Area of Science:

  • Biomaterials Science
  • Cardiovascular Medicine
  • Machine Learning Applications

Background:

  • Acute aortic dissection (AD) is a life-threatening cardiovascular emergency with challenging clinical diagnosis.
  • Accurate and rapid diagnostic tools for AD are crucial for timely intervention and improved patient outcomes.
  • Current diagnostic methods can be time-consuming or lack specificity, necessitating novel approaches.

Purpose of the Study:

  • To develop and validate a machine-learning-assisted sensor array for the rapid and precise triage of acute aortic dissection.
  • To investigate the role of the protein corona effect in the sensor array's disease-specific colorimetric response.
  • To evaluate the diagnostic performance of the sensor array, both alone and in combination with clinical biomarkers.

Main Methods:

  • Fabrication of a sensor array using manganese porphyrin (Mn-TCPP) and metal-organic frameworks (PFC-73-Mn, PCN-224-Mn).
  • Exposure of the sensor array to patient plasma to generate disease-specific colorimetric patterns via differential catalytic inhibition.
  • Application of machine learning algorithms (XGBoost and Random Forest) for data analysis and classification.
  • Analysis of 181 patient samples, including AD patients and those with other causes of acute chest pain.

Main Results:

  • The sensor array demonstrated disease-specific colorimetric responses influenced by the protein corona effect.
  • Combined with an XGBoost algorithm, the sensor array achieved 86.5% accuracy and 0.933 AUC in distinguishing AD from other chest pain causes.
  • Fusion with clinical biomarkers using a Random Forest classifier further enhanced diagnostic performance to 91.9% accuracy and 0.985 AUC.
  • The system requires only 20 μL of plasma and provides results within 15 minutes.

Conclusions:

  • The developed machine-learning-assisted sensor array provides a rapid, robust, and accurate tool for acute aortic dissection diagnosis.
  • The study offers mechanistic insights into protein corona-mediated sensing for disease triage.
  • This approach holds significant potential for improving emergency cardiovascular care through faster and more precise diagnostics.