Detection of low-abundance biomarker lipocalin 1 for diabetic retinopathy using optoelectrokinetic bead-based immunosensing.

Early diagnosis of diabetic retinopathy (DR) is vital but challenging. DR is a common complication and a major cause of vision loss in patients with diabetes mellitus. Without appropriate medical intervention, visual impairment may become a great burden to our healthcare system. In clinical practice, the current diagnostic methods, such as fluorescence angiography and optical coherence tomography, remain constrained by non-quantitative examinations and individual ophthalmologists' experiences. Late diagnosis often prevents early treatment. To address the constraints on current diagnostics, this study developed an optoelectrokinetic bead-based immunosensing technique for detecting lipocalin 1 (LCN1), a DR biomarker. The concentration level of LCN1 in the tears of DR patients increases with DR severity. The immunoassay was dependent on the formation of sandwiched immunocomplexes on the particles. A secondary antibody labeled with dyes/quantum dots (QDs) was used to visualize the presence of the target antigens. Rapid electrokinetic patterning (REP), an optoelectrokinetic technique, was used to dynamically enhance the fluorescent signal by concentrating the modified particles. The limit of detection (LOD) of the technique could reach 110pg/mL. Only 1.5µL of a sample fluid was required for the measurement. Our results showed that highly sensitive and improved LOD is subjected to particle stacking, small particle size, and compact cluster. By labeling different particle sizes with dyes/QDs for LCN1 and TNF-α, we successfully used REP to detect the two DR biomarkers on the same platform. The development of an optoelectrokinetic bead-based immunosensing technique can provide new insights into diagnosing other low-abundance diseases in the future.

Multiple ECG beats recognition in the frequency domain using grey relational analysis.

This paper proposes a method for multiple ECG beats recognition using novel grey relational analysis (GRA). Converts each QRS complex to a Fourier spectrum from ECG signals, the spectrum varies with the rhythm origin and conduction path. The variations of power spectra are observed in the range of 0 Hz-20 Hz in the frequency domain. According to the frequency-domain parameters, GRA performs to recognize the cardiac arrhythmias including the supraventricular ectopic beat, bundle branch ectopic beat, ventricular ectopic beat, and fusion beat. The method was tested on MIT-BIH arrhythmia database. The results demonstrate the efficiency of the proposed non-invasive method, and also show high accuracy for detecting electrocardiogram (ECG) signals.