Serum protein profile study of myocardial infarction using a LED induced fluorescence based HPLC system

A label-free ratiometric homogeneous electrochemical aptasensor based on dual catalytic hairpin self-assembly for rapid and sensitive detection of ochratoxin A in food

The food contamination with Ochratoxin A (OTA) has highlighted the need to create precise, sensitive, and convenient techniques. Herein, we proposed a label-free and immobilization-free ratiometric homogeneous electrochemical aptasensor based on dual catalytic hairpin self-assembly (CHA) for OTA detection. Methylene blue (MB) and ferrocene (Fc) in solution were utilized as label-free signaling molecules, generating a response signal (IMB) and a reference signal (IFc), respectively. The ratio of IMB/IFc was utilized as a measure to quantify OTA. Dual CHA was exploited to increase the ratiometric signal and enhance the amplification efficiency. This aptasensor achieved trace-level detection for OTA over a linear range of lower concentrations (1.0 × 10-3 ng/mL-1.0 × 103 ng/mL) with LOD of 92 fg/mL. The aptasensor was successfully applied to detect OTA in cereal and wine, with comparable results of HPLC-MS/MS. This strategy provided a viable platform for rapid, sensitive, and accurate detection of OTA in food.

HPLC-LED-Induced Fluorescence Analysis of Tear Fluids: An Objective Method for Primary Open Angle Glaucoma Diagnosis

PURPOSE

The study showcased the application of the lab-assembled HPLC-LED-IF system to analyze proteins in tear fluid samples collected from individuals diagnosed with primary open-angle glaucoma (POAG).

METHODS

Clinical application of the said technique was evaluated by recording chromatograms of tear fluid samples from control and POAG subjects and by analyzing the protein profile using multivariate analysis. The data analysis methods involved are principal component analysis (PCA), Match/No-Match, and artificial neural network (ANN) based binary classification for disease diagnosis.

RESULTS

Mahalanobis distance and spectral residual values calculated using a standard calibration set of clinically confirmed POAG samples for the Match/No-Match test gave 86.9% sensitivity and 81.8% specificity. ANN with leaving one out procedure has given 87.1% sensitivity and 81.8% specificity.

CONCLUSION

The results of the study revealed that the utilization of a 278 nm LED excitation in the HPLC system offers good sensitivity for detecting proteins at low concentrations allowing to obtain reliable protein profiles for the diagnosis of POAG.

Direct immersion single-drop microextraction combined with fluorescence detection using an optical probe. Application for highly sensitive determination of rhodamine 6G

The use of an optical probe for fluorescence detection combined with direct immersion single-drop microextraction has been demonstrated as an innovative approach. The optical probe served both as a drop holder for extractant and as a measuring device which made it possible to eliminate the use of cuvettes. A laser and a light emitting diode (LED) were tested as possible light sources. Both of them showed comparable results. However, given the much smaller half-band width of the laser radiation, its use has proven to be preferable since background correction can be eliminated. Direct immersion single-drop microextraction of an ionic association complex of rhodamine 6G with picric acid with subsequent fluorescent detection (λex was 532 nm and 525 nm for laser and LED, respectively; λem was 560 nm for both laser and LED) was used a model system to evaluate the new approach. The extractant phase was a 55 μL amyl acetate microdrop fixed in the optical part of the probe. LOD, LOQ and linear calibration range were found as 0.14, 0.48 and 0.5-10 nmol L-1, and 0.15, 0.50 and 0.5-5 nmol L-1 for laser and LED light sources, respectively. The accuracy of the method was assessed by analyzing real water samples.

Protein profile pattern analysis: A multifarious, in vitro diagnosis technique for universal screening

Universal health care is attracting increased attention nowadays, because of the large increase in population all over the world, and a similar increase in life expectancy, leading to an increase in the incidence of non-communicable (various cancers, coronary diseases, neurological and old-age-related diseases) and communicable diseases/pandemics like SARS-COVID 19. This has led to an immediate need for a healthcare technology that should be cost-effective and accessible to all. A technology being considered as a possible one at present is liquid biopsy, which looks for markers in readily available samples like body fluids which can be accessed non- or minimally- invasive manner. Two approaches are being tried now towards this objective. The first involves the identification of suitable, specific markers for each condition, using established methods like various Mass Spectroscopy techniques (Surface-Enhanced Laser Desorption/Ionization Mass Spectroscopy (SELDI-MS), Matrix-Assisted Laser Desorption/Ionization (MALDI-MS), etc., immunoassays (Enzyme-Linked Immunoassay (ELISA), Proximity Extension Assays, etc.) and separation methods like 2-Dimensional Polyacrylamide Gel Electrophoresis (2-D PAGE), Sodium Dodecyl-Sulfate Polyacrylamide Gel Electrophoresis (SDS-PAGE), Capillary Electrophoresis (CE), etc. In the second approach, no attempt is made the identification of specific markers; rather an efficient separation method like High-Performance Liquid Chromatography/ Ultra-High-Performance Liquid Chromatography (HPLC/UPLC) is used to separate the protein markers, and a profile of the protein pattern is recorded, which is analysed by Artificial Intelligence (AI)/Machine Learning (MI) methods to derive characteristic patterns and use them for identifying the disease condition. The present report gives a summary of the current status of these two approaches and compares the two in the use of their suitability for universal healthcare.

Protein profile analysis of tear fluid with hyphenated HPLC-UV LED-induced fluorescence detection for the diagnosis of dry eye syndrome

Tear fluid contains organic and inorganic constituents, variations in their relative concentrations could provide valuable information and can be useful for the detection of several ophthalmological diseases. This report describes the application of the lab-assembled light-emitting diode (LED)-based high-performance liquid chromatography system for protein profiling of tear fluids to diagnose dry eye disease. Principal Component Analysis (PCA), match/no-match, and Artificial Neural Network (ANN) based binary classification of protein profile data were performed for disease diagnosis. Results from the match/no-match test of the protein profile data showed 94.4% sensitivity and 87.8% specificity. ANN with the leaving one out procedure has given 91.6% sensitivity and 93.9% specificity.