In this study, we report the successful development of a novel high-sensitivity intensity-based Surface Plasmon Resonance imaging (SPRi) biosensor and its application for detecting molecular interactions. By optimizing the excitation wavelength and employing a wavelength division multiplexing (WDM) algorithm, the system can determine the optimal excitation wavelength based on the initial refractive index of the sample without adjusting the incidence angle. The experimental results demonstrate that the refractive index resolution of the system reaches 1.77×10-6 RIU. Moreover, it can obtain the optimal excitation wavelength for samples with an initial refractive index in the range of 1.333 to 1.370 RIU and accurately monitor variations within the range of 0.0037 RIU without adjusting the incidence angle. Additionally, our new SPRi technique realized real-time detection of high-throughput biomolecular binding processes, enabling analysis of kinetic parameters. This research is expected to advance the development of more accurate SPRi technologies for molecular interaction analysis.

The advancement of nanoscience and material design in recent times has facilitated the creation of point-of-care devices for cancer diagnosis and biomolecule sensing. Exosomes (EXOs) facilitate the transfer of bioactive molecules between cancer cells and diverse cells in the local and distant microenvironments, thereby contributing to cancer progression and metastasis. Specifically, EXOs derived from cancer are likely to function as biomarkers for early cancer detection due to the genetic or signaling alterations they transport as payload within the cancer cells of origin. It has been verified that EXOs circulate steadily in bodily secretions and contain a variety of information that indicates the progression of the tumor. However, acquiring molecular information and interactions regarding EXOs has presented significant technical challenges due to their nanoscale nature and high heterogeneity. Colorimetry, surface plasmon resonance (SPR), fluorescence, and Raman scattering are examples of optical techniques utilized to quantify cancer exosomal biomarkers, including lipids, proteins, RNA, and DNA. Many optically active nanoparticles (NPs), predominantly carbon-based, inorganic, organic, and composite-based nanomaterials, have been employed in biosensing technology. The exceptional physical properties exhibited by nanomaterials, including carbon NPs, noble metal NPs, and magnetic NPs, have facilitated significant progress in the development of optical nanobiosensors intended for the detection of EXOs originating from tumors. Following a summary of the biogenesis, biological functions, and biomarker value of known EXOs, this article provides an update on the detection methodologies currently under investigation. In conclusion, we propose some potential enhancements to optical biosensors utilized in detecting EXO, utilizing various NP materials such as silicon NPs, graphene oxide (GO), metal NPs, and quantum dots (QDs).

Optical biosensors have employed at least three distinct system architectures over the last 40 years, moving from \"sample in-answer out\" systems to completely embedding the optical biosensor into the sample to embedding the recognition module in the sample and optically interrogating the recognition module from outside of the sample. This trends article provides an overview of the evolution of these three system architectures and discusses how each architecture has been applied to solve the measurement challenges of a wide variety of applications. A fourth biosensor system architecture, that of an \"autonomous\" biosensor which \"takes the user out of the loop\" while both detecting target analytes and responding to that measurement, is currently under development for applications initially including environmental cleanup and \"smart therapeutics.\" As is the case in many other areas of technology, it will be profoundly interesting to observe the further development and application of elegant, simpler (optical) biosensor systems to address tomorrow\'s measurement needs.

Small molecule natural compounds are gaining popularity in biomedicine due to their easy access to wide structural diversity and their proven health benefits in several case studies. Affinity measurements of small molecules below 100 Da molecular weight in a label-free and automatized manner using small amounts of samples have now become a possibility and reviewed in the present work. We also highlight novel label-free setups with excellent time resolution, which is important for kinetic measurements of biomolecules and living cells. We summarize how molecular-scale affinity data can be obtained from the in-depth analysis of cellular kinetic signals. Unlike traditional measurements, label-free biosensors have made such measurements possible, even without the isolation of specific cellular receptors of interest. Throughout this review, we consider epigallocatechin gallate (EGCG) as an exemplary compound. EGCG, a catechin found in green tea, is a well-established anti-inflammatory and anti-cancer agent. It has undergone extensive examination in numerous studies, which typically rely on fluorescent-based methods to explore its effects on both healthy and tumor cells. The summarized research topics range from molecular interactions with proteins and biological films to the kinetics of cellular adhesion and movement on novel biomimetic interfaces in the presence of EGCG. While the direct impact of small molecules on living cells and biomolecules is relatively well investigated in the literature using traditional biological measurements, this review also highlights the indirect influence of these molecules on the cells by modifying their nano-environment. Moreover, we underscore the significance of novel high-throughput label-free techniques in small molecular measurements, facilitating the investigation of both molecular-scale interactions and cellular processes in one single experiment. This advancement opens the door to exploring more complex multicomponent models that were previously beyond the reach of traditional assays.

The expanding interest in digital biomarker analysis focused on non-invasive human bodily fluids, such as sweat, highlights the pressing need for easily manufactured and highly efficient soft lab-on-skin solutions. Here, we report, for the first time, the integration of microfluidic paper-based devices (μPAD) and non-enhanced Raman-scattering-enabled optical biochemical sensing (Raman biosensing). Their integration merges the enormous benefits of μPAD, with high potential for commercialization and use in resource-limited settings, with biorecognition-element-free (but highly selective) optical Raman biosensing. The introduced thin (0.36 mm), ultra-lightweight (0.19 g), and compact footprint (3 cm2) opto-paperfluidic sweat patch is flexible, stretchable, and conforms, irritation-free, to hairless or minimally haired body regions to enable swift sweat collection. As a great advantage, this new bio-chemical sensory system excels through its absence of onboard biorecognition elements (bioreceptor-free) and omission of plasmonic nanomaterials. The proposed easy fabrication process is adaptable to mass production by following a fully sustainable and cost-effective process utilizing only basic tools by avoiding typically employed printing or laser patterning. Furthermore, efficient collection and transportation of precise sweat volumes, driven exclusively by the wicking properties of porous materials, shows high efficiency in liquid transportation and reduces biosensing latency by a factor of 5 compared to state-of-the-art epidermal microfluidics. The proposed unit enables electronic chip-free and imaging-less visual sweat loss quantification as well as optical biochemical analysis when coupled with Raman spectroscopy. We investigated the multimodal quantification of sweat urea and lactate levels ex vivo (with syntactic sweat including +30 sweat analytes on porcine skin) and achieved a linear dynamic range from 0 to 100 mmol/L during fully dynamic continuous flow characterization.

The high toxicity and occurrence of ochratoxin A (OTA) in grains and foods has been a growing concern due to the impacts on health and the economy in many countries. In this sense, simplified devices with high sensitivity and specificity for local monitoring are enthusiastically pursued. In this work, we report for the first time the detection of ochratoxin A in coffee samples using a spoon-shaped waveguide immunosensor. The biosensor was built with the surface of the spoon-shaped waveguide covered by a 60 nm layer of gold to enable the SPR phenomenon. The measurements indicated a linear relationship between the change in the SPR phenomenon values and the OTA concentration in the range from 0.2 ppt to 5 ppt. When analyzed in coffee samples, the biosensor was highly selective and did not suffer matrix interference. The developed biosensor represents a promising analytical device for coffee quality analyses, as it is portable, simple, and suitable for onsite detection of target analytes.

Viral pandemic diseases have disruptive global consequences leading to millions of deaths and a severe impact on the global economy. Inadequate preventative protocols have led to an overwhelming demand for intensive care leading to uncontrollable burdens and even breakdown of healthcare sectors across many countries. The rapid detection of viral disease helps in the understanding of the relevant intricacies, helping to tackle infection with improved guidelines. Portable biosensor devices offer promising solutions by facilitating on-site detection of viral pathogens. This review summarizes the latest innovative strategies reported using electroanalytical methods for the screening of viral antigens. The structural components of viruses and their categories are presented followed by the various recognition elements and transduction techniques involved in biosensors. Core sections focus on biosensors reported for viral genomic detection(DNA and RNA) and antigenic capsid protein. Strategies for addressing the challenges of electroanalytical biosensing of viral components are also presented. The advantages, and disadvantages of biorecognition elements and nanozymes for the detection of viral disease are highlighted. Such technical insights will help researchers working in chemistry, and biochemistry as well as clinicians working in medical diagnostics.

Proteases have been proposed as potential biomarkers for several pathological conditions including cancers, multiple sclerosis and cardiovascular diseases, due to their ability to break down the components of extracellular matrix and basement membrane. The development of protease biosensors opened up the possibility to investigate the proteolytic activity of dysregulated proteases with higher efficiency over the traditional detection assays due to their quick detection capability, high sensitivity and selectivity, simple instrumentation and cost-effective fabrication processes. In contrast to the recently published review papers that primarily focused on one specific class of proteases or one specific detection method, this review article presents different optical and electrochemical detection methods that can be used to design biosensors for all major protease families. The benefits and drawbacks of various transducer techniques integrated into protease biosensing platforms are analyzed and compared. The main focus is on activity-based biosensors that use peptides as biorecognition elements. The effects of nanomaterials on biosensor performance are also discussed. This review should help readers to select the biosensor that best fits their needs, and contribute to the further development of this research field. Protease biosensors may allow better comprehension of protease overexperession and potentially enable novel devices for point-of-care testing.

Biosensors have rapidly emerged as a high-sensitivity and convenient detection method. Among various types of biosensors, optical and electrochemical are the most commonly used. Conventionally, antibodies have been employed to ensure specific interaction between the transmission material and analytes. However, there has been increasing recognition of peptides as a promising recognition element for biosensor development in recent years. The use of peptides as recognition elements provides high level of specificity, sensitivity, and stability for the detection process. The combination of peptide designs and optical or electrochemical detection methods has significantly improved biosensor efficacy. These advancements present opportunities for developing biosensors with diverse functions that can be used to lay a strong scientific foundation for the development of personalized medicine and various other fields. This paper reviews the recent advancements in the development and application of peptide-based optical and electrochemical biosensors, as well as their prospects as a sensor type.

This study explores a smartphone-based spot detection framework for glucose in a rapid, simple, and affordable paper-based analytical device (PAD), which employs machine-learning algorithms to estimate various glucose concentrations. Herein, two different detection mixtures were chosen with chitosan (C) and without chitosan (WC) for the color change analysis. Being a biopolymer, chitosan improves the analytical performance of PADs when used with a chromogenic agent. Moreover, the influence of the illumination conditions and camera optics on the professed color of glucose strips was observed by choosing various illumination conditions and different smartphones. Hence, this study focuses on developing a framework for smartphone-based simple and user-friendly spot-based glucose detection (with a concentration range of 10-40 mM) at any illumination conditions and in any direction of illumination. Additionally, the combination of color spaces and machine-learning algorithms was applied for the signal enhancement. It was observed that the machine learning classifiers, cubic support vector machine (SVM) and narrow neutral network show higher accuracy for the WC samples, which are 92.7 and 92.3%, respectively. The samples with chitosan show higher accuracy for the linear discriminant and quadratic SVM classifiers, which are 94.1 and 93.9%, respectively. Simultaneously, cubic SVM shows ∼93% accuracy for both cases. In order to assess the performance of the devices, a blind test was also conducted. This study demonstrates the potential of the developed system for initial disease screening at the user end. By incorporating machine learning techniques, the platform can provide reliable and accurate results, thus paving the way for estimating the accuracy of the results for improved initial healthcare screening and diagnosis of any disease.