Research Projects

Myocardial infarction (MI) is one of the leading causes of death in the world and is considered a severe public health problem.¹ This condition, marked by inadequate oxygen supply culminating in cardiac tissue damage, underscores the urgency for precise diagnostic measures. Once heart tissue is damaged, cardiac troponins (cTnI) are released into the bloodstream 90 min to 3 hrs before (and for many hours after) the onset of MI.² Elevated cTnI is a concern for heart damage (5-50 ng/mL in serum)³, the presence of cTnI in interstitial fluid⁴ and other bodily fluids such as serum,⁵-⁶ urine,⁷ saliva,⁸pericardial fluid⁹-¹⁰ and sweat¹¹ is well-documented (Table 1). Timely measurement of cTnI levels is the cornerstone in monitoring cardiac ailments, notably MI.¹² The concentration profile of cTnI in biological fluids mirrors the extent of myocardial injury, equipping clinicians with indispensable information to tailor treatment strategies.¹³-¹⁴ Aptamer-based electrochemical sensors have drawn considerable interest due to their promise to advance sensing capabilities. However, their market adoption is hindered by the relatively short lifespan of aptamers in biological fluids, primarily attributed to their vulnerability to nucleases. Moreover, selecting nuclease-resistant aptamers is challenging with the traditional aptamer selection process. In this work, we propose to develop a one-step method for selecting nuclease-resistant aptamers or cTnI. After validation, the aptamer will be further engineered as pseudoknot to add conformational switching properties and attachment moieties, then apply this to develop a transdermal wearable patch for continuous monitoring of cTnI. For this, we will integrate it with a custom-designed microfluidic patch for continuous sweat collection and replenishment. Additionally, we will employ specialized electrodes featuring a conductive antifouling layer formed by self-assembled monolayers to enhance signal stability and reduce biofouling, ensuring long-term performance (Figure 1). Artificial intelligence (AI) would be instrumental in managing MI via signal processing of measured data. AI can analyze large volumes of real-time cTnI data from wearable patch sensors to detect patterns and predict potential cardiac events before critical thresholds are reached. Moreover, AI improves the sensitivity and specificity of wearable biosensors by filtering noise and identifying meaningful trends, facilitating early intervention and reducing false alarms. The aptamer-based sensor will read out using a high-sensitivity microfluidic integrated electrochemical wearable sensor and the Integration of AI with proposed systems ensures seamless communication of critical data, enabling quick response and informed decision-making. This innovative approach not only holds promise for a unique method for nuclease-resistant aptamer selection but also augmenting early detection and management of MI with AI, in an emergency room or at home setting.