Emerging vistas on electrochemical detection of diabetic retinopathy biomarkers

Advancing Diabetes Management: The Future of Enzyme-Less Nanoparticle-Based Glucose Sensors-A Review

BACKGROUND

Glucose is vital for biological processes, requiring blood sugar levels to be maintained between 3.88 and 6.1 mmol/L, especially during fasting. Elevated levels signal diabetes, a global concern affecting 537 million people, necessitating effective glucose-monitoring devices.

METHOD

Enzyme-based sensors, though selective, are sensitive to environmental factors. Nonenzymatic sensors, especially those with nanoparticles, offer stability, high surface area, and cost-effectiveness. Existing literature supports their immediate glucose oxidation, showcasing exceptional sensitivity.

RESULTS

This review details nonenzymatic sensors, highlighting materials, detection limits, and the promise of nanoparticle-based designs, which exhibit enhanced sensitivity and selectivity in glucose detection.

CONCLUSION

Nanoparticle-based sensors, as reviewed, show potential for glucose monitoring, overcoming enzyme-based limitations. The conclusion suggests future directions for advancing these sensors, emphasizing ongoing innovation in this critical research area.

Composite membrane of graphene oxide and gold nanoparticles functionalized S fiber taper aptasensor for highly sensitive bisphenol A detection

The S fiber taper (SFT) aptasensor with a composite sensitive membrane of graphene oxide and gold nanoparticles was proposed for the rapid and highly sensitive detection of bisphenol A (BPA). The SFT was obtained using a fusion splicer; subsequently, the composite film was deposited on its surface, and the specific aptamer was covalently bonded to the surface of gold nanoparticles. The detection mechanism relies on monitoring changes in the external refractive index induced by the specific binding of BPA to the aptamer. The developed SFT aptasensor exhibited a remarkable sensitivity of 15.5 nm/nM and a limit of detection as low as 0.01 nM for BPA. These findings highlight the aptasensor's potential for diverse monitoring applications.

Application of SERS-based nanobiosensors to metabolite biomarkers of CKD

A clinical diagnosis of chronic kidney disease (CKD) is commonly achieved by estimating the serum levels of urea and creatinine (CR). Given the limitations of the conventional diagnostic assays, it is imperative to seek alternative, economical strategies for the detection of CKD-specific biomarkers with high specificity and selectivity. In this respect, surface-enhanced Raman spectroscopy (SERS) can be regarded as an ideal choice. SERS signals can be greatly amplified by noble metal nanoparticles (e.g., gold nanoparticles (GNPs)) of numerous sizes, shapes, and configurations to help achieve ultra-sensitive single molecule-level detection at 10^-15 M (up to 10 orders of magnitude more sensitive than fluorescence-based detection). The irregular geometry of GNPs with spike-like tips, dimers, and aggregates with small nanogaps (i.e., due to plasmon coupling such as Raman hot spots) play a pivotal role in enhancing the specificity and sensitivity of SERS. This review critically outlines the performance of SERS-based biosensors in the ultrasensitive detection of CKD biomarkers in various body fluids in terms of basic quality assurance parameters (e.g., limit of detection, figure of merit, enhancement factor, and stability of the biosensor). Moreover, the challenges and perspectives are described with respect to the expansion of such sensing techniques in practical clinical settings.

Trends in Quantification of HbA1c Using Electrochemical and Point-of-Care Analyzers

Glycated hemoglobin (HbA1c), one of the many variants of hemoglobin (Hb), serves as a standard biomarker of diabetes, as it assesses the long-term glycemic status of the individual for the previous 90-120 days. HbA1c levels in blood are stable and do not fluctuate when compared to the random blood glucose levels. The normal level of HbA1c is 4-6.0%, while concentrations > 6.5% denote diabetes. Conventionally, HbA1c is measured using techniques such as chromatography, spectroscopy, immunoassays, capillary electrophoresis, fluorometry, etc., that are time-consuming, expensive, and involve complex procedures and skilled personnel. These limitations have spurred development of sensors incorporating nanostructured materials that can aid in specific and accurate quantification of HbA1c. Various chemical and biological sensing elements with and without nanoparticle interfaces have been explored for HbA1c detection. Attempts are underway to improve the detection speed, increase accuracy, and reduce sample volumes and detection costs through different combinations of nanomaterials, interfaces, capture elements, and measurement techniques. This review elaborates on the recent advances in the realm of electrochemical detection for HbA1c detection. It also discusses the emerging trends and challenges in the fabrication of effective, accurate, and cost-effective point-of-care (PoC) devices for HbA1c and the potential way forward.

Exploring various novel diagnostic and therapeutic approaches in treating diabetic retinopathy

Diabetic retinopathy is regarded as a common manifestation of diabetes mellitus, being a prominent cause of visual impairment and blindness. This microvascular complication is marked by the appearance of microaneurysms, elevated vascular permeability, capillary blockage, and proliferation of neovasculature. The etiology behind retinopathy is ambiguous and the efficacy of current treatment strategies is minimal. Early diagnosis of this complication using a biomarker with high sensitivity and specificity is very essential for providing better therapeutic strategies. The current available therapeutic options are limited with various adverse effects. Laser treatment is not beneficial in all the situations, economic constraints being the major challenge. Surgical interventions are employed when pharmacotherapy and laser treatment fail. New pharmacological treatments are becoming a necessity for treating the condition. This review highlights the use of various diagnostic tools, emerging biomarkers for early detection of diabetic retinopathy, pathological mechanisms associated with the disease, current therapeutic approaches used and future strategies for more enhanced treatment options and more potent pharmacological actions.

Diagnostic circulating biomarkers to detect vision-threatening diabetic retinopathy: Potential screening tool of the future?

With the increasing prevalence of diabetes in developing and developed countries, the socio-economic burden of diabetic retinopathy (DR), the leading complication of diabetes, is growing. Diabetic retinopathy (DR) is currently one of the leading causes of blindness in working-age adults worldwide. Robust methodologies exist to detect and monitor DR; however, these rely on specialist imaging techniques and qualified practitioners. This makes detecting and monitoring DR expensive and time-consuming, which is particularly problematic in developing countries where many patients will be remote and have little contact with specialist medical centres. Diabetic retinopathy (DR) is largely asymptomatic until late in the pathology. Therefore, early identification and stratification of vision-threatening DR (VTDR) is highly desirable and will ameliorate the global impact of this disease. A simple, reliable and more cost-effective test would greatly assist in decreasing the burden of DR around the world. Here, we evaluate and review data on circulating protein biomarkers, which have been verified in the context of DR. We also discuss the challenges and developments necessary to translate these promising data into clinically useful assays, to detect VTDR, and their potential integration into simple point-of-care testing devices.

Colorimetric and Electrochemical Screening for Early Detection of Diabetes Mellitus and Diabetic Retinopathy-Application of Sensor Arrays and Machine Learning

In this review, a selection of works on the sensing of biomarkers related to diabetes mellitus (DM) and diabetic retinopathy (DR) are presented, with the scope of helping and encouraging researchers to design sensor-array machine-learning (ML)-supported devices for robust, fast, and cost-effective early detection of these devastating diseases. First, we highlight the social relevance of developing systematic screening programs for such diseases and how sensor-arrays and ML approaches could ease their early diagnosis. Then, we present diverse works related to the colorimetric and electrochemical sensing of biomarkers related to DM and DR with non-invasive sampling (e.g., urine, saliva, breath, tears, and sweat samples), with a special mention to some already-existing sensor arrays and ML approaches. We finally highlight the great potential of the latter approaches for the fast and reliable early diagnosis of DM and DR.

Label Free Glucose Electrochemical Biosensor Based on Poly(3,4-ethylenedioxy thiophene):Polystyrene Sulfonate/Titanium Carbide/Graphene Quantum Dots

The electrochemical biosensor devices based on enzymes for monitoring biochemical substances are still considered attractive. We investigated the immobilization of glucose oxidase (GOx) on a new composite nanomaterial poly(3,4-ethylenedioxythiophene): polystyrene sulfonate (PEDOT:PSS)/titanium carbide,(Ti₃C₂)/graphene quantum dots(GQD) modified screen-printed carbon electrode (SPCE) for glucose sensing. The characterization and electrochemical behavior of PEDOT:PSS/Ti₃C₂/GQD towards the electrocatalytic oxidation of GOx was analyzed by FTIR, XPS, SEM, cyclic voltammetry (CV), and differential pulse voltammetry (DPV). This composite nanomaterial was found to tend to increase the electrochemical behavior and led to a higher peak current of 100.17 µA compared to 82.01 µA and 95.04 µA for PEDOT:PSS and PEDOT:PSS/Ti₃C₂ alone. Moreover, the detection results demonstrated that the fabricated biosensor had a linear voltammetry response in the glucose concentration range 0-500 µM with a relatively sensitivity of 21.64 µAmM^-1cm^-2 and a detection limit of 65 µM (S/N = 3), with good stability and selectivity. This finding could be useful as applicable guidance for the modification screen printed carbon (SPCE) electrodes focused on composite PEDOT:PSS/Ti₃C₂/GQD for efficient detection using an enzyme-based biosensor.

Dual sensitive and rapid detection of glycated human serum albumin using a versatile lead/graphene nanocomposite probe as a fluorescence-electrochemical aptasensor

Monitoring of glycated human serum albumin (GHSA) as a glycemic marker for screening and monitoring of diabetes mellitus is widely practiced for patients with conditions that affect red blood cells. In this study, a complex comprising Pb ions adsorbed on graphene oxide (GO-Pb) was fabricated and utilized as a versatile probe in a fluorescence-electrochemical aptasensor for GHSA quantification. To simplify the aptasensor, the GO-Pb complex probe was prepared via an ion adsorption process. After modification with a fluorophore-labeled aptamer, the GO-Pb complex served as an excellent energy acceptor in fluorescence-based analysis, as well as generating a high current in the electrochemical transducer. Additionally, the proposed platform can detect GHSA via the dual technique from a single sample, allowing for precise and accurate results. Under optimal conditions, the fluorescence-electrochemical aptasensor exhibited a linear relationship with GHSA concentrations from 0.001 to 80 μg mL^-1 and from 0.005 to 10 μg mL^-1 for fluorescence and electrochemical detection, respectively. The corresponding detection limits were 8.80 ng mL^-1 and 0.77 ng mL^-1, respectively. The proposed aptasensor additionally displayed good selectivity and excellent stability. Moreover, its successful application in the analysis of clinical samples further demonstrated its utility. Therefore, the proposed platform has significant potential as a novel, facile, highly responsive, and low-cost monitoring method for the development of diabetes mellitus diagnostic devices intended for a clinical setting.

AUTOMATED SCREENING OF DIABETIC RETINOPATHY WITH OPTIMIZED DEEP CONVOLUTIONAL NEURAL NETWORK: ENHANCED MOTH FLAME MODEL

Nowadays, analysis on retinal image exists as one of the challenging area for study. Numerous retinal diseases could be recognized by analyzing the variations taking place in retina. However, the main disadvantage among those studies is that, they do not have higher recognition accuracy. The proposed framework includes four phases namely, (i) Blood Vessel Segmentation (ii) Feature Extraction (iii) Optimal Feature Selection and (iv) Classification. Initially, the input fundus image is subjected to blood vessel segmentation from which two binary thresholded images (one from High Pass Filter (HPF) and other from top-hat reconstruction) are acquired. These two images are differentiated and the areas that are common to both are said to be the major vessels and the left over regions are fused to form vessel sub-image. These vessel sub-images are classified with Gaussian Mixture Model (GMM) classifier and the resultant is summed up with the major vessels to form the segmented blood vessels. The segmented images are subjected to feature extraction process, where the features like proposed Local Binary Pattern (LBP), Gray-Level Co-Occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRM) are extracted. As the curse of dimensionality seems to be the greatest issue, it is important to select the appropriate features from the extracted one for classification. In this paper, a new improved optimization algorithm Moth Flame with New Distance Formulation (MF-NDF) is introduced for selecting the optimal features. Finally, the selected optimal features are subjected to Deep Convolutional Neural Network (DCNN) model for classification. Further, in order to make the precise diagnosis, the weights of DCNN are optimally tuned by the same optimization algorithm. The performance of the proposed algorithm will be compared against the conventional algorithms in terms of positive and negative measures.

Sensors for diagnosis of prostate cancer: Looking beyond the prostate specific antigen

Prostate cancer represents one of the most common forms of cancer affecting men across the globe. Due to late diagnosis of this disease, the mortality of this condition is very high. Conventional diagnostic methods like the direct rectal examination are uncomfortable and, in most cases, delayed, and further confirmation is required with biopsies and Gleason score. The most common biomarker approved by the FDA (United States Food and Drug Administration) is the prostate specific antigen (PSA) that is detected by conventional biochemical assays which require expensive reagents, is time-consuming and more often is only indicative and cannot be considered confirmative as it is susceptible to erroneous conclusions. The prostate health index employs quantification of PSA in its free and bound forms to enumerate the risk of prostate cancer and has found acceptance with clinicians though the methods used to determine these quantities are slow and require additional sensitivity. Search for novel biomarkers other than PSA has resulted in the identification of several promising candidates. However, their detection is still heavily dependent upon conventional biochemical assays that retain the challenges of being time-consuming, poorly sensitive and expensive. Development of specific sensor technologies integrating nanomaterials offers a viable alternative for rapid and sensitive determination of these non-PSA markers. This review summarizes the major advances in the development of sensors for diagnosis of prostate cancer using non-PSA markers. It also highlights some of the emerging paradigms in cancer diagnosis that may transform the diagnostic field in the context of prostate cancer.