The frequency and prevalence of invasive fungal infections have increased, particularly among hospitalized patients with severe underlying illnesses and/or immunocompromised individuals [1,2]. The survival of these patients relies on the prompt identification of the infection and on the timely initiation of antifungal therapy, and yet standard laboratory testing may yield ambiguous results [3]. The diagnostic approaches for candidiasis include culture testing, serological assays, and histopathologic analysis of tissues; however, these methods may be time-consuming and can yield insensitivity or inaccuracies. The prevailing "gold standard" for identifying Candida spp. fungemia is blood culture. Nonetheless, this is considered insensitive, as it has been shown to be positive in fewer than half of individuals with chronic disseminated candidiasis [4]. Culture methods are time-intensive, with certain Candida species requiring up to a week for development, which is an intolerable delay before initiating fungemia treatment. The difficulties in diagnosing Candida infections highlight the necessity for efficient and rapid methods to detect and identify clinically relevant fungi in a microbiology laboratory. This work involves the development of an electrochemical DNA-based sensor for the rapid, simple, and precise detection of Candida spp. This sensor, self-assembled in an electronic paper device (ePAD), is based on the electrochemical detection of the hybridization reaction between two complementary single-stranded DNA sequences. Initial research indicated that this DNA-based sensor may identify Candida spp. in synthetic DNA samples. Notwithstanding these results, efforts are underway to enhance the sensor for measuring Candida albicans; this methodology will be corroborated by a further study. Future advancements will focus on application within a medical setting, encompassing sensitivity, accuracy, response time, challenges, and potential.

Introduction

Nanoconjugates are widely used as recognizing agents in different immunoassays, targeted therapies and biochemical methods of analysis. The performance of such conjugates strongly depends on their kinetic properties in interaction with the analyte. Various techniques such as surface plasmon resonance (SPR), bio-layer interferometry (BLI) and spectral correlation interferometry (SCI) allow for the kinetic characterization of protein–protein interactions, but there is still a lack of methods for screening nanoconjugate kinetics. Here, we show a novel approach for the characterization of magnetic nanoconjugates binding kinetics, which is based on the magnetometric detection of conjugates in the analytical zone of lateral flow assay (LFA) test strips. The developed method was used to screen different magnetic nanoconjugates against biotin, which was further used for its sensitive detection.

Methods

Magnetic nanocojugates were synthesized by adding 200 nm magnetic particles to a carbodiimide solution. After incubation, the particles were washed three times with water, and the solution of anti-biotin antibodies was added to the coupling mixture. After incubation, BSA solution was also added to block the excess of activated carboxyl groups. Then, the suspension of magnetic conjugates was washed with water again, and then used for further analysis.

Synthesized nanoconjugates were tested on LFA test strips with BSA-biotin conjugates immobilized in the analytical zone. The interaction between the target and labels was registered with the magnetic particle quantification (MPQ) technique during the test.

Results

Different amounts of nanoconjugates were taken every test. The experimental relation between the nanoconjugate concentration and saturation MPQ signal was fitted with a Langmuir adsorption model to determine the K_D constant. After that, real-time interaction data were used to measure the k_on and k_off rate constants.

Based on determined kinetic properties, the most efficient conjugate was chosen in the magnetic LFA test system for biotin detection.

Conclusions

The developed approach was used to provide the kinetics screening of magnetic nanoconjugates. This method could be particularly advantageous in point-of-care diagnostics and the development of rapid screening assays for biomedical applications.

Ensuring food quality and safety is a cornerstone of modern agriculture, encompassing all stages of production, processing, storage, and distribution. Agricultural products are vulnerable to contamination by various harmful substances, including mycotoxins such as ochratoxin A, zearalenone, and aflatoxin B1. These toxic compounds are produced by certain fungi and pose serious health risks to humans and animals. Developing efficient methods for optimizing detection systems is crucial for addressing contamination challenges. This study focuses on creating an optimized methodology for the development of magnetic immunochromatographic assays for the detection of these mycotoxins in cereal products. This study employed spherical superparamagnetic nanoparticles as a basis for the immunochromatographic assay. A competitive immunoassay format was used as the foundation for quantifying the studied mycotoxins in cereal samples. The methodology included the covalent immobilization of antibodies specific to each mycotoxin on the surface of magnetic nanoparticles. Antigen-coated conjugate pads were prepared for each mycotoxin to serve as test lines. The systematic optimization of parameters such as the density of antibodies on magnetic particles, the amount of bioconjugates used in each test, and the antigen printing density on the test lines was performed. Quantitative measurements of the magnetic nanoconjugates’ distribution were carried out using the magnetic particle quantification method. The optimized parameters were validated against high-performance liquid chromatography (HPLC) results for cereal products contaminated with the studied mycotoxins. The developed methodology enabled the systematic optimization of key assay parameters to achieve high sensitivity and specificity for detecting the studied mycotoxins. Adjustments to the density of antibodies immobilized on magnetic nanoparticles were made within a range of 0.25 to 7.5 µg per conjugation. Optimal conditions were identified by achieving the maximum signal-to-noise ratio at a toxin concentration of 1 ng/mL. The additional fine-tuning of bioconjugate amounts and antigen printing densities ensured reproducibility and sensitivity. The validation process demonstrated that assays developed using this methodology achieved detection limits in the low ng/mL range and showed excellent correlation with HPLC data. The proposed methodology provides a robust framework for optimizing magnetic immunochromatographic assays for the detection of mycotoxins in cereal products. This approach enables the efficient development of reliable test systems, contributing to enhanced food safety and quality assurance across the agricultural supply chain.

Paper-based electrochemical biosensors have emerged as a revolutionary technology in healthcare diagnostics due to their affordability, portability, ease of use, and environmental sustainability. These biosensors utilize paper as the primary material, capitalizing on its unique properties, such as high porosity, flexibility, and capillary action, which make it an ideal candidate for low-cost, functional, and reliable diagnostic devices. The simplicity and cost-effectiveness of paper-based biosensors make them especially suitable for point-of-care (POC) applications, particularly in resource-limited settings where traditional diagnostic tools may be inaccessible. Their lightweight nature and ease of operation allow non-specialized users to perform diagnostic tests without the need for complex laboratory equipment, making them suitable for emergency, field, and remote applications. Technological advancements in paper-based biosensors have significantly enhanced their capabilities. Integration with microfluidic systems has improved fluid handling and reagent storage, resulting in enhanced sensor performance, including greater sensitivity and specificity for target biomarkers. The use of nanomaterials in electrode fabrication, such as reduced graphene oxide and gold nanoparticles, has further elevated their sensitivity, allowing for precise detection of low-concentration biomarkers. Moreover, the development of multiplexed sensor arrays has enabled the simultaneous detection of multiple biomarkers from a single sample, facilitating comprehensive and rapid diagnostics in clinical settings. These biosensors have found applications in diagnosing a wide range of diseases, including infectious diseases, cancer, and metabolic disorders. They are also effective in genetic analysis and metabolic monitoring, such as tracking glucose, lactate, and uric acid levels, which are crucial for managing chronic conditions like diabetes and kidney diseases. In this review, the latest advancements in paper-based electrochemical biosensors are explored, with a focus on their applications, technological innovations, challenges, and future directions.

Water pollution poses serious threats to public health and the environment, requiring efficient and scalable monitoring solutions. This paper presents a Cyber–Physical System (CPS) that integrates paper-based biosensors with an Edge IoT architecture and Long-Range Wide Area Network (LoRaWAN) for real-time assessment of water quality. The biosensors detect pollutants such as arsenic, lead, and nitrates with a detection limit of 0.5 ppb. The collected data are transmitted via LoRaWAN to edge devices, where preprocessing and analysis are performed using the Temporal Convolutional Network (TCN) algorithm. The system proposed is compared with existing LSTM systems based on two performance metrics: detection accuracy and latency. Paper-based biosensors are fabricated using silver nanoparticle-functionalized substrates for high sensitivity and low-cost pollutant detection. Data transmission is based on LoRaWAN protocol to have long-range communication with packet loss per cent at a minimum level. TCN algorithm deployment at the edge allows for real-time processing for time-series data analysis due to its high accuracy and low latency properties, compared to LSTM models, which were mainly chosen due to their usage in most applications dealing with time-series-based analysis. Experimentation was carried out by deploying the developed CPS in controlled environments, simulating pollutant levels at different levels and executing them for accuracy in detecting pollutants and the latency of data processing. The system's energy consumption was reduced through efficient edge processing, enhancing the long-term sustainability of its deployments. The TCN framework achieved a detection accuracy of 98.7%, which surpasses LSTM by 92.4%. In addition, TCN reduces latency in processing by 38% to enable fast data analysis and decision-making. LoRaWAN allows for perfect packet transmission of up to 15 km while the loss rate stays as low as 2.1%. These results establish the proposed CPS as reliable, efficient, and scalable for real-time water contamination monitoring. Thus, this research introduces the integration of paper-based biosensors with advanced computational frameworks like TCN and explores its great potential as a transformative development to pave the way toward more sophisticated multi-sensor fusion systems in future studies.

Lateral flow assays (LFAs) are paper-based analytical devices (PADs) widely used in medical diagnostics, food safety, and environmental monitoring. These test strips provide rapid, on-site analysis without the need for specialized equipment, making them both affordable and highly portable. However, traditional LFAs struggle to detect low concentrations of analytes, which are often present during the early stages of diseases. Preconcentration techniques can significantly enhance sensitivity, but current methods that require an external power source compromise the simplicity and portability that make LFAs so valuable. To address this limitation, we propose a novel solution using diffusiophoresis (DP), a solute-driven electrokinetic phenomenon, to concentrate analytes on PADs without the need for external power. By harnessing the spontaneous electric field generated at the interface between electrolytes with different ionic strengths, we aim to achieve online electrokinetic preconcentration while preserving the portability of LFAs. In standard colorimetric LFAs, this approach could dramatically improve detection limits. Mobile coloured nanoparticles conjugated to detection reagents will bind to target analytes, which will then interact with immobilized capture reagents at the test line. Based on DP studies conducted in microchannels, preconcentration factors exceeding 10^4 could be achieved at the test line, potentially leading to over a 10,000-fold improvement in sensitivity without sacrificing the simplicity and ease of use of LFAs.

Silver nanoparticles (AgNp) play an important role in various domains, including nanomedicine, water disinfection, biosensing, biomedical engineering, photonics and optoelectronics. This is mainly due to their unique optical characteristics which result from the localized surface plasmon resonance (LSPR) and which are comparable to those of gold and copper nanoparticles. Coating Ag with silica (SiO2) improves the thermal stability of nanoparticles, prevents their agglomeration and oxidation and offers adjustable solubility in various solvents, but may eventually affect their optical properties.

In this study, we use the theory of effective media to calculate optical spectra of spherical Ag nanoparticles coated with SiO2 and monodispersed in aqueous solution. The theory of the homogenization gives the absorption and diffusion spectra in terms of the filling factor of nanoparticles and the dielectric function of each component (i.e., AgNp core, SiO2 shell, and surrounding medium). To describe the dielectric function of AgNp, we used the Drude critical point model (DCP), which adjusts the experimental data of metal permittivity in the 200-1400 nm band and describes electron oscillations more appropriately than the classical and commonly used Drude model. The effects of silica layer thickness and AgNp filling factor on optical spectra are reported in this work.

In photothermal therapy, the controlled release of heat by properly synthesized nanomaterials can be assisted by local temperature detection, provided by so-called nanothermometers present near nanoheaters.

The current research study offers various methodologies for measuring local temperature based on the electrical, mechanical or optical properties of materials, like Scanning Thermal Microscopy, Atomic Force Microscopy, Infrared or Fluorescence Thermography and Raman Spectroscopy. In this context, Raman Spectroscopy is a non-contact technique offering high spatial and thermal resolution, which is extremely important for photothermal therapy. Raman signals can be enhanced by exploiting the vicinity of Raman-active materials/molecules to plasmonic nanosystems, which are also widely used to produce nanoheaters.

In this work, composite nanoparticles are specifically developed to provide close contact between plasmonic nanosystems, acting as nanoheaters, and local temperature sensors: silver nanoparticles (AgNP) are surrounded by a shell of anatase titanium dioxide (TiO₂), which has already been tested as a Raman thermometer.

The synthesis is a multi-step process. Silver cores are prepared through a two-pot reaction. First, the silver precursor (AgNO₃) is reduced and stabilized to produce a seed suspension with an average particle size of 7 (± 2) nm. Then, a growth step is performed to reach nanoparticle sizes of 46 (± 8) nm. Dioxide shell formation is carried out using a sol–gel method starting from an ethanol solution of titanium tetrabutoxide (TTB) as a precursor. A final hydrothermal treatment induces the crystallization of TiO₂ to the anatase form.

The resulting nanocomposites are characterized using various techniques, including Transmission Electron Microscopy (TEM), Scanning Electron Microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), X-ray diffraction (XRD), UV/Vis extinction and Raman Spectroscopy.

This work demonstrates the feasibility of fabricating nanocomposite structures with high potential as photothermal systems, providing a starting point for future improvements in this field.

While 46% of India's agricultural labor provides only 18% of the country's GDP by providing basic ingredients for humankind and raw materials for industrialization, 2.5 billion people worldwide work in the agriculture sector. Following the green revolution, a variety of agricultural techniques were developed, including chemical pesticides and herbicides, which further increased crop yields by successfully controlling the infestation of weeds and other pests. To attain the objective of regional and global food security, technical interventions are required in the fundamentals of food processing, quality assurance, and the identification, diagnosis, and prevention of catastrophic risk. Through molecular recognition materials, antigen–antibody contact, and the ensuing transmission mechanism, recent developments in biosensing technologies and material sciences have been essential in comprehending the dynamics of agricultural processes. One analytical tool that converts biological reactions into electrical signals is a biosensor. Piezoelectric, thermal, DNA-based, tissue-based, enzyme-based, and immune-based biosensors are some examples. Numerous agricultural applications, including the evaluation of toxins in soils and crops, the identification and diagnosis of infectious diseases in crops and animals, online monitoring of important food process parameters, the measurement of animal reproduction, and veterinary medication screening, can make use of biosensors. Ex vivo or in vivo injections of genetically modified proteins into cells are used to create cell- and tissue-based biosensors. The agriculture industry has also been greatly impacted by technological developments in the fields of nanobiosenors, bioelectronics, material science, miniaturization techniques, electrode design, fabrication technology, nanolithography, and microfluidics. It is necessary to focus our research on improving a biosensor's shelf life in order to boost end-user acceptance. As biosensors' fundamental properties improve, they will be widely used in important yet difficult agricultural fields.

Introduction
Surface Plasmon Resonance (SPR) biosensors have emerged as indispensable tools for real-time, label-free detection of biomolecules. However, conventional SPR sensors that employ noble metals are often hampered by substantial optical losses and constrained tunability in the near-infrared (NIR) spectrum. To address these challenges, we introduce an optimized SPR biosensor that integrates transparent conducting oxides (TCOs)—specifically, aluminum-doped zinc oxide (AZO) and indium tin oxide (ITO)—with graphene, thereby enhancing both plasmonic performance and biomolecular interactions.

Methods
The sensor architecture was developed using the Kretschmann configuration, incorporating a BK-7 prism along with sequential layers of AZO, ITO, graphene monolayers, and an additional dielectric layer to maximize sensitivity. Reflectance was modeled via the Transfer Matrix Method (TMM), while the dielectric characteristics of AZO and ITO were determined using the Drude–Lorentz oscillator model. Optimization of critical sensor parameters—including layer thicknesses and incident angles—was achieved through the application of machine learning techniques (Random Forest algorithms) and genetic algorithms (GAs).

Results
The proposed biosensor achieved a maximum sensitivity of 197.64°/RIU and a Figure of Merit (FOM) of 780.4 RIU⁻¹, thereby outperforming conventional SPR sensors based on gold and silver. The incorporation of graphene notably enhanced biomolecule adsorption, while the additional dielectric layer contributed to improved detection accuracy. Moreover, comparative analyses revealed that TCO-based SPR sensors exhibit markedly lower optical losses in the NIR range.

Conclusions
This study presents a novel and highly sensitive SPR biosensor that exploits the superior plasmonic properties of TCOs. By substituting traditional noble metals with AZO and ITO, the sensor achieves enhanced sensitivity, reduced optical losses, and superior detection accuracy. Furthermore, the integration of machine learning optimization techniques significantly refines sensor performance, paving the way for next-generation biosensors with broad applications in medical diagnostics, environmental monitoring, and biophotonics.