Rapid and Inexpensive Paper-Based Test Simultaneously Quantifies Multiple Cardiac Biomarkers

However, this approach often necessitates costly medical equipment and complex procedures that can only be carried out by highly trained professionals within a medical facility. These factors prolong testing time and hinder the widespread application of diagnostics in remote and resource-poor areas due to limited access to central labs. In response to these challenges, point-of-care (POC) sensors were developed as alternative diagnostic tools, characterized by their simplicity, rapid operation, compact size, and affordability. The most prevalent type of POC tests are paper-based sensors, also known as lateral flow assays (LFAs), where the injected sample fluid flows horizontally and reacts with specific test regions (test lines) to generate, for instance, a color change. Despite their ease of use and cost-effectiveness, existing LFAs have certain drawbacks, such as lower sensitivity and challenges with multiplexed testing for disease biomarkers.

To address these shortcomings, researchers at University of California (UCLA, Los Angeles, CA, USA) have devised a novel paper-based biosensor that utilizes a fluorescent multiplexed vertical flow assay to rapidly and simultaneously measure three cardiac biomarkers from human serum samples. This new paper-based POC sensor's vertical flow design allows for multiple test regions with up to 100 individual test spots on a single disposable cartridge. The powerful sensor operates with just a small serum droplet and can be easily used by a minimally trained individual in under 15 minutes per patient. Along with its multiplexing capabilities, the paper-based sensor also boasts high sensitivity, achieving a detection limit better than ~0.5 ng/mL for each biomarker — less than one billionth of half a gram per milliliter of serum.

Additionally, the UCLA researchers have created a mobile phone-based, low-cost handheld fluorescence reader and a deep learning-assisted signal analysis pipeline to automatically and accurately quantify the three target biomarkers in a user-friendly manner. The team tested their paper-based multiplexed sensor for the quantification of three biomarkers of acute coronary syndrome (ACS), including myoglobin, creatine kinase-MB (CK-MB), and heart-type fatty acid binding protein (FABP). ACS is a cardiovascular condition that demands prompt diagnosis in emergency situations, and these target markers are released into the bloodstream shortly after symptom onset. The newly-developed paper-based sensor was evaluated on human serum samples, and the measured concentrations for all three cardiac biomarkers aligned well with the benchmark measurements obtained by a standard laboratory test. Given its accuracy, speed, user-friendliness, and affordability, this deep learning-enabled paper-based multiplexed sensor offers an attractive POC testing option for various applications in remote and resource-limited settings.

“Compared to a commonly used linear calibration method, our deep learning-based analysis benefits from the function approximation power of neural networks to learn non-trivial relationships between the multiplexed fluorescence signals from the paper-based sensor and the underlying analyte concentrations in serum,” said Artem Goncharov, a graduate student at UCLA Electrical & Computer Engineering Department. “As a result, we have accurate quantitative measurements for all three biomarkers of interest despite the background noise present in clinical serum samples.”

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