Point-of-care testing: a disposable label-free electrochemical CA125 and HE4 immunosensors for early detection of ovarian cancer

Ultrasensitive and label-free electrochemical immunosensor for the detection of the ovarian cancer biomarker CA125 based on CuCo-ONSs@AuNPs nanocomposites

Cancer antigen 125 (CA125) is pivotal as a tumor marker in early ovarian cancer prevention and diagnosis. In this work, we introduced an ultrasensitive label-free electrochemical immunosensor tailored for CA125 detection, leveraging nanogold-functionalized copper-cobalt oxide nanosheets (CuCo-ONSs@AuNPs) as nanocomposites. For the inaugural application, copper-cobalt oxide nanosheets delivered the requisite DPV electrochemical response for the immunosensors. Their large specific surface area and commendable electrical conductivity amplify electron transfer and enable significant gold nanoparticle loading. Concurrently, AuNPs offer a plethora of active sites, facilitating easy immobilization of biomolecules via the bond between amino groups and AuNPs. We employed scanning electron microscopy, transmission electron microscopy, and x-ray photoelectron spectroscopy to characterize the nanomaterials' surface morphology and elemental composition. The electrochemical sensor response signals were ascertained using differential pulse voltammetry. Under optimal conditions, the immunosensor exhibited a linear detection range from 1×10-7 U/mL to 1×10-3 U/mL and a detection limit of 3.9×10-8 U/mL (S/N=3). The proposed label-free electrochemical immunosensor furnishes a straightforward, dependable, and sensitive approach for CA125 quantification and stands as a promising method for clinical detection of other tumor markers.

Employing nano-enabled artificial intelligence (AI)-based smart technologies for prediction, screening, and detection of cancer

Cancer has been classified as a diverse illness with a wide range of subgroups. Its early identification and prognosis, which have become a requirement of cancer research, are essential for clinical treatment. Patients have already benefited greatly from the use of artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms in the field of healthcare. AI simulates and combines data, pre-programmed rules, and knowledge to produce predictions. Data are used to improve efficiency across several pursuits and tasks through the art of ML. DL is a larger family of ML methods based on representational learning and simulated neural networks. Support vector machines, convulsion neural networks, and artificial neural networks, among others, have been widely used in cancer research to construct prediction models that enable precise and effective decision-making. Although using these innovative methods can enhance our comprehension of how cancer progresses, further validation is required before these techniques can be used in routine clinical practice. We cover contemporary methods used in the modelling of cancer development in this article. The presented prediction models are built using a variety of guided ML approaches, as well as numerous input attributes and data collections. Early identification and cost-effective detection of cancer's progression are equally necessary for successful treatment of the disease. Smart material-based detection techniques can give end consumers a portable, affordable instrument to easily detect and monitor their health issues without the need for specialized knowledge. Owing to their cost-effectiveness, excellent sensitivity, multimodal detection capacity, and miniaturization aptitude, two-dimensional (2D) materials have a lot of prospects for clinical examination of various compounds as well as cancer biomarkers. The effectiveness of traditional devices is moving faster towards more useful techniques thanks to developments in 2D material-based biosensors/sensors. The most current developments in the design of 2D material-based biosensors/sensors-the next wave of cancer screening instruments-are also outlined in this article.

A disposable impedimetric immunosensor for the analysis of CA125 in human serum samples

Cancer antigen 125 (CA125) is the most common biomarker used to diagnose and monitor ovarian cancer progression for the last four decades, and precise detection of its levels in blood serum is crucial. In this work, label-free impedimetric CA125 immunosensors were fabricated by using screen-printed carbon electrodes modified with poly toluidine blue (PTB) (in deep eutectic solvent)/gold nanoparticles (AuNP) for the sensitive, environmentally friendly, economical, and practical analysis of CA125. The materials of PTBDES and AuNP were characterized by Fourier Transform Infrared (FT-IR), Scanning Electron Microscope (FE-SEM), and X-ray Diffraction (XRD). The analysis of the CA125 was performed by electrochemical impedance spectroscopy and the developed immunosensor. The immunosensor's repeatability, reproducibility, reusability, selectivity, and storage stability were examined. The developed label-free immunosensor allowed the determination of CA125 in fast, good repeatability and a low limit of detection (1.20 pg mL-1) in the linear range of 5-100 pg mL-1. The stable surface of the fabricated immunosensor was successfully regenerated ten times. The application of immunosensors in commercial human blood serum was performed, and good recoveries were achieved. The disposable label-free impedimetric CA125 immunosensor developed for the rapid and practical detection of CA125 is a candidate for use in point-of-care tests in clinical applications of ovarian cancer.