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Cell lysis is the starting step of many biomedical assays. Electric field-based cell lysis is widely used in many applications, including point-of-care (POC) applications, because it provides an easy one-step solution. Many electric field-based lysis methods utilize micro-electrodes to apply short electric pulses across cells. Unfortunately, these cell lysis devices produce relatively low cell lysis efficiency as electric fields do not reach a significant portion of cells in the sample. Additionally, the utility of syringe pumps for flow cells in and out of the microfluidics channel causes cell loss and low throughput cell lysis. To address these critical issues, we suspended the cells in a sessile drop and concentrated on the electrodes. We used low-frequency AC electric fields (1 Vpp, 0–100 kHz) to drive the cells effectively towards electrodes and lysed using a short pulse of 10 V. A post-lysis analysis was performed using a hemocytometer, UV-vis spectroscopy, and fluorescence imaging. The results show that the pre-electric polarization of cells, prior to applying short electrical pulses, enhances the cell lysis efficiency. Additionally, the application of AC electric fields to concentrate cells on the electrodes reduces the assay time to about 4 min. In this study, we demonstrated that low-frequency AC electric fields can be used to pre-polarize and concentrate cells near micro-electrodes and improve cell lysis efficiency. Due to the simplicity and rapid cell lysis, this method may be suitable for POC assay development. 

Amino acid identification is crucial across various scientific disciplines, including biochemistry, pharmaceutical research, and medical diagnostics. However, traditional methods such as mass spectrometry require extensive sample preparation and are time-consuming, complex and costly. Therefore, this study presents a pioneering Machine Learning (ML) approach for automatic amino acid identification by utilizing the unique absorption profiles from an Elliptical Dichroism (ED) spectrometer. Advanced data preprocessing techniques and ML algorithms to learn patterns from the absorption profiles that distinguish different amino acids were investigated to prove the feasibility of this approach. The results show that ML can potentially revolutionize the amino acid analysis and detection paradigm. 

Low-cost, highly-sensitivity, and minimally invasive tests for the detection and monitoring of life-threatening diseases and disorders can reduce the worldwide disease burden. Despite a number of interdisciplinary research efforts, there are still challenges remaining to be addressed, so clinically significant amounts of relevant biomarkers in body fluids can be detected with low assay cost, high sensitivity, and speed at point-of-care settings. Although the conventional proteomic technologies have shown promise, their ability to detect all levels of disease progression from early to advanced stages is limited to a limited number of diseases. One potential avenue for early diagnosis is microRNA (miRNA). Due to their upstream positions in regulatory cascades, blood-based miRNAs are sensitive biomarkers that are detectable earlier than those targeted by other methods. Therefore, miRNA is a promising diagnostic biomarker for many diseases, including those lacking optimal diagnostic tools. Electric fields have been utilized to develop various biomedical assays including cell separation, molecules detection and analysis. Recently, there has been a great interest in the utility of electric fields with optical detection methods, including fluorescence and surface plasmons toward biomarker detection. This mini review first summarizes the recent development of miRNA as a biomarker. Second, the utility of electric fields and their integration with fluorescence detection methods will be discussed. Next, recent studies that utilized electric fields and optical detection methods will be discussed. Finally, in conclusion, technology gaps and improvements needed to enable low-cost and sensitive biomarker detection in point-of-care settings will be discussed.