Recent advances in nanomaterials-based electrochemical immunosensors and aptasensors for HER2 assessment in breast cancer

A new insight into the early detection of HER2 protein in breast cancer patients with a focus on electrochemical biosensors approaches: A review

Breast cancer is one of the leading causes of death in women and is a prevalent kind of cancerous growth, representing a substantial risk to women's health. Early detection of breast cancer is essential for effective treatment and improved survival rates. Biomarkers, active substances that signal the existence and advancement of a tumor, play a significant role in the early detection of breast cancer. Hence, accurate identification of biomarkers for tumors is crucial for diagnosing and treating breast cancer. However, the primary diagnostic methods used for the detection of breast cancer require specific equipment, skilled professionals, and specialized analysis, leading to elevated detection expenses. Regarding this obstacle, recent studies emphasize electrochemical biosensors as more advanced and sensitive detection tools compared to traditional methods. Electrochemical biosensors are employed to identify biomarkers that act as unique indicators for the onset, recurrence, and monitoring of therapeutic interventions for breast cancer. This study aims to provide a summary of the electrochemical biosensors that have been employed for the detection of breast cancer at an early stage over the past decade. Initially, the text provides concise information about breast cancer and tumor biomarkers. Subsequently, an in-depth analysis is conducted to systematically review the progress of electrochemical biosensors developed for the stable, specific, and sensitive identification of biomarkers associated with breast cancer. Particular emphasis was given to crucial clinical biomarkers, specifically the human epidermal growth factor receptor-2 (HER2). The analysis then explores the limitations and challenges inherent in the design of effective biosensors for diagnosing and treating breast cancer. Ultimately, we provided an overview of future research directions and concluded by outlining the advantages of electrochemical biosensor approaches.

Employing Pd nanoparticles decorated on halloysite nanotube/carbon composite for electrochemical aptasensing of HER2 in breast cancer patients

An effective biosensing platform is described based on halloysite nanotube/carbon composite decorated with Pd nanoparticles (HNT/C@Pd NPs). A novel electrochemical aptasensor was designed using the proposed nano-platform to determine human epidermal growth factor receptor 2 (HER2), a breast cancer biomarker. Inherently, aptasensing interfaces provide high sensitivity and selectivity for tumor markers owing to the high specific surface area of HNT/C and good conductivity stems from deposition of Pd NPs into HNT/C composite. With a correlation coefficient of 0.996, the electrochemical aptasensor demonstrated a wide linear range from 0.03 ng/mL to 9 ng/mL. The limit of detection (LOD) of the established assay was 8 pg/mL based on S/N = 3 method. Further, the designed biosensor demonstrated acceptable selectivity, good reproducibility, and high stability. The applicability of the impedimetric sensor in human serum samples was also examined and compared to enzyme-linked immunosorbent assay (ELISA) assay (p-value >0.05). Based on the results, it was found that the proposed methodology can be used in quantification of breast cancer markers for early diagnosis and treatment.

A label-free electrochemical aptasensor based on Bi-Sb alloy materials for potential POCT of HER-2

As a prognostic biomarker for breast cancer, human epidermal growth factor receptor 2 (HER-2) is of crucial diagnostic value. Here, a label-free electrochemical aptasensor was established for the ultrasensitive detection of HER-2 using a modified electrode of Bi-Sb alloy materials (Bi-Sb AMs). The performance of the aptasensor was enhanced greatly due to the introduction of Bi-Sb alloy materials (Bi-Sb AMs) with high conductivity. Furthermore, by integrating the aptasensor with the Sensit Smart U-disk electrochemical analyzer, the point-of-care testing (POCT) for HER-2 was realized. Under the optimal experimental parameters, the POCT analyzer showed a wide linear response from 0.01 pg mL-1 to 100 ng mL-1, with a low detection limit (LOD) of 5.96 fg mL-1 for the detection of HER-2. The presented POCT analyzer exhibited good specificity, stability, and reproducibility. Benefiting from the simple operation and rapid testing, the developed analyzer will have potential application in the prognostic diagnosis and treatment of breast cancer.

Advances in wearable electrochemical antibody-based sensors for cortisol sensing

Cortisol is a crucial hormone involving many physiological processes. Hence, cortisol detection is essential. This review highlights the key progress made on wearable electrochemical sensors using antibodies. It covers the design, principle, and electroanalytical methodology for detecting cortisol noninvasively. This article also analyzes and collects the analytical performances of electrochemical cortisol sensors. The development of these sensors continues to face challenges such as biofouling, sample management, sensitivity, flexibility, stability, and recognition layer performance. It is also necessary to develop a sensitive electrode and material. This article also presents potential strategies for designing antibody electrodes and provides examples of sensing systems. Additionally, it discusses the challenges in translating research into practical applications.

Morphology-Tuned Electrochemical Immunosensing of a Breast Cancer Biomarker Using Hierarchical Palladium Nanostructured Interfaces

Metallic nanostructures are considered attractive candidates for designing novel biosensors due to their enormously significant surface area, accelerated kinetics, and improved affinity. Controllable morphological tuning of metallic nanostructures on sensing interfaces is crucial for attaining clinically relevant sensitivity and exquisite selectivity in a complex biological environment. Therefore, a facile, convenient, and robust one-step electroreduction method was employed to develop different morphological variants of palladium (Pd) nanostructures supported onto oxidized carbon nanotubes to facilitate label-free electrochemical immunosensing of HER2. The morphological and structural attributes of the synthesized Pd nanostructures were thoroughly investigated using scanning electron microscopy, X-ray diffraction, X-ray photoelectron spectroscopy, and atomic force microscopy techniques. In-depth electrochemical investigations revealed an intimate correlation between the nanostructured sensor and electrochemical response, suggesting the suitability of hierarchical palladium nanostructures supported onto carbon nanotubes [Pd(-0.1 V)/CNT] for sensitive detection of HER2. The high surface area of hierarchical Pd nanostructures enabled an ultrasensitive electrochemical response toward HER2 (detection limit: 1 ng/mL) with a wide detection range of 10 to 100 ng/mL. The ease of surface modification, sensitivity, and reliable electrochemical response in human plasma samples suggested the enormous potential of Pd nanostructuring for chip-level point-of-care screening of HER2-positive breast cancer patients.

Label-Free Plasmon-Enhanced Spectroscopic HER2 Detection for Dynamic Therapeutic Surveillance of Breast Cancer

The expression of human epidermal growth factor receptor-2 (HER2) has important implications for pathogenesis, progression, and therapeutic efficacy of breast cancer. The detection of its variation during the treatment is crucial for therapeutic decision-making but remains a grand challenge, especially at the cellular level. Here, we develop a machine learning-driven surface-enhanced Raman spectroscopy (SERS)-integrated strategy for label-free detection of cellular HER2. Specifically, our method allows the extraction of cell-rich spectral signatures utilized for identification and classification of cancer cells with distinct HER2 expression with a high accuracy of 99.6%. By combining label-free SERS detection and machine learning-driven chemometric analysis, we are able to perform longitudinal monitoring of therapeutic efficacy at the cellular level during the treatment of HER2+ breast cancer, which aids in the subsequent decision-making and management. This work provides a promising technique capable of performing dynamic label-free spectroscopic detection for therapeutic surveillance of diseases.

Unveiling the underpinnings of various non-conventional ELISA variants: a review article

INTRODUCTION

Enzyme-linked immunosorbent assay (ELISA) is a key bio-analytical technique used for the detection of a large array of antigenic substances of scientific, clinical, food safety, and environmental importance. The assay primarily involves capturing and detecting target analytes using specific antigen-antibody interactions. The wide usage of ELISA shoulders on its high specificity and reproducibility. Notwithstanding, the conventional microwell plate-based format of ELISA has some major drawbacks, such as long assay time (4 - 18 h), large sample volumes requirement (100 - 200 μL), lack of multiplicity, and burdensome procedures that limit its utility in rapid and affordable diagnostics.

AREAS COVERED

Here, we reviewed microfluidic-ELISA, paper-ELISA, aptamer-ELISA, and those based on novel incubation such as heat-ELISA, pressure-ELISA, microwave-ELISA, and sound-ELISA. Further, the current trends and future prospects of these ELISA protocols in clinical diagnostics are discussed.

EXPERT OPINION

The reviewed non-conventional ELISA formats are relatively rapid, require low reagent volumes, are multiplexable, and could be performed in a low-cost setup. In our opinion, these non-conventional variants of ELISA are on a par with the conventional format for clinical diagnostics and fundamental biological research and hold added clinical translational potential for quick, inexpensive, and convenient measurements.

Fast Speckle Noise Suppression Algorithm in Breast Ultrasound Image Using Three-Dimensional Deep Learning

The rapid development of ultrasound medical imaging technology has greatly broadened the scope of application of ultrasound, which has been widely used in the screening, diagnosis of breast diseases and so on. However, the presence of excessive speckle noise in breast ultrasound images can greatly reduce the image resolution and affect the observation and judgment of patients’ condition. Therefore, it is particularly important to investigate image speckle noise suppression. In the paper, we propose fast speckle noise suppression algorithm in breast ultrasound image using three-dimensional (3D) deep learning. Firstly, according to the gray value of the breast ultrasound image, the input breast ultrasound image contrast is enhanced using logarithmic and exponential transforms, and guided filter algorithm was used to enhance the details of glandular ultrasound image, and spatial high-pass filtering algorithm was used to suppress the excessive sharpening of breast ultrasound image to complete the pre-processing of breast ultrasound image and improve the image clarity; Secondly, the pre-processed breast ultrasound images were input into the 3D convolutional cloud neural network image speckle noise suppression model; Finally, the edge sensitive terms were introduced into the 3D convolutional cloud neural network to suppress the speckle noise of breast ultrasound images while retaining image edge information. The experiments demonstrate that the mean square error and false recognition rate all reduced to below 1.2% at the 100th iteration of training, and the 3D convolutional cloud neural network is well trained, and the signal-to-noise ratio of ultrasound image speckle noise suppression is greater than 60 dB, the peak signal-to-noise ratio is greater than 65 dB, the edge preservation index value exceeds the experimental threshold of 0.45, the speckle noise suppression time is low, the edge information is well preserved, and the image details are clearly visible. The speckle noise suppression time is low, the edge information is well preserved, and the image details are clearly visible, which can be applied to the field of breast ultrasound diagnosis.