Severe Dengue fever can cause Dengue Hemorrhagic Fever (DHF), a life-threatening condition characterized by plasma leakage and hemoconcentration. A hematocrit (Hct) rise of ≥20% is a key indicator for medical intervention, but current monitoring is invasive and intermittent. This study aims to determine the optimal design parameters for a non-invasive optical sensor to continuously monitor hemoconcentration. We developed a high-fidelity Monte Carlo model of light transport in a multi-layered skin model, with the epidermis set to a 5% melanin volume fraction (Fitzpatrick type II/III). To ensure signal reliability, simulations were conducted with a high photon count (1×108 photons), yielding a stochastic (Monte Carlo) signal-to-noise ratio of approximately 36 dB. We simulated diffuse reflectance at four characteristic wavelengths (577 nm, 660 nm, 800 nm—the isosbestic point—, and 940 nm) over source-detector separations of 0.5–8.0 mm. Sensor sensitivity was quantified as the reflectance change for a +25% relative Hct rise (e.g., 42% to 52.5%), mimicking severe hemoconcentration, and its dependence on baseline dermal blood volume fraction (BVF) was investigated. Sensor sensitivity showed a non-linear dependence on BVF, showing a direct correlation with perfusion level, reaching an optimal 6.41% for a robust 5% BVF at 8.0 mm. A dedicated sweep showed that even under low-perfusion shock conditions (1% BVF), the sensor maintains a highly significant sensitivity of 5.71% (also at 8.0 mm), indicating that sensitivity remains high across a physiologically relevant perfusion range. In the analysis, at a robust 5% BVF, the 800 nm wavelength demonstrated superior reliability, with peak sensitivity at 6.41% at 8.0 mm. Visible wavelengths (577 nm and 660 nm) exhibited high theoretical sensitivity, while 940 nm was compromised by water absorption. Based on these findings, a non-invasive optical sensor for hemoconcentration is most effective operating at 800 nm, within the evaluated spectral set, with a source-detector separation of ≥6.0 mm, targeting the deep dermis while minimizing superficial interference. This design provides an optimal balance of tissue penetration, robust sensitivity to Hct changes, and reduced sensitivity to oxygenation-related variability while maintaining signal stability. This work enables the design of a device for continuous monitoring, supporting continuous monitoring of hemoconcentration trends relevant to plasma leakage progression.

Correct focal positioning is essential for microscopy imaging of live moving subjects such as Caenorhabditis elegans. However, many methods can be too slow to perform real-time control to keep the subject in focus. In this work, we propose a convolutional neural network-based method to perform one-shot prediction of the optimal focusing distance, without the need to scan iteratively the optical axis to find the optimal position. A new data augmentation technique is proposed, and its effectiveness is validated through statistical analysis. This technique is shown to improve results without the need for additional data collection. Several architectures are trained in z-stacks of images, using the proposed data augmentation technique, and compared on a validation set. Through this comparison, we find that the ConvNext V2, a novel architecture in this context, outperforms other models proposed in previous works. Furthermore, the impact of the Field of View used for the model’s prediction is studied, with the aim of further understanding the influence of spatial resolution and spatial compression on the performance of the model.

Avian influenza (AI), particularly highly pathogenic avian influenza (HPAI), represents a serious and growing threat to global poultry production, international trade, and human health security. Control of AI is complicated by the high evolutionary rate of influenza A viruses, which drives antigenic diversity and ongoing emergence of novel strains. Effective surveillance and disease management therefore depend on timely and accurate diagnostics. While conventional methods—including virus isolation, reverse transcription-quantitative polymerase chain reaction (RT-qPCR), and enzyme-linked immunosorbent assays (ELISAs)—remain effective and widely used, they are limited by long turnaround times, the need for specialized equipment, and reliance on highly trained personnel. In addition, strict state and federal regulatory requirements restrict testing to a limited number of authorized laboratories. Although these regulations are essential for maintaining diagnostic accuracy and quality assurance, they place substantial strain on laboratory capacity during outbreaks and delay actionable results. The need for rapid, on-site decision making has driven interest in alternative diagnostic approaches, including biosensor technologies. A major limitation of current diagnostic strategies is the lack of robust DIVA (Differentiating Infected from Vaccinated Animals) capability. In countries such as the United States, where poultry vaccination against AI is not routinely practiced, the absence of DIVA-compatible diagnostics has hindered adoption of vaccination as a disease management tool, as seropositive birds and products face significant trade restrictions. Biosensor platforms capable of enabling DIVA strategies offer a potential pathway to support vaccination while preserving surveillance integrity. This review examines the current landscape of AI and HPAI diagnostics, emphasizing the limitations of traditional approaches and the opportunities presented by biosensor platforms. We evaluate electrochemical, optical, piezoelectric, and nucleic-acid-based biosensors, with particular attention to biorecognition strategies, performance metrics, field deployability, and applications supporting subtype discrimination, DIVA implementation, and One Health surveillance.

This study addresses the need for detecting human papillomavirus type 16 DNA (HPV-16), a high-risk factor for cervical cancer, by developing a highly sensitive fluorescence sensing method based on tungsten disulfide (WS2) nanosheets and exonuclease III (EXO III)-assisted cyclic amplification. The method is constructed by combining the highly efficient fluorescence quenching capability of tungsten disulfide (WS2) nanosheets with a fluorescein (FAM)-labeled complementary DNA (cDNA) probe. When the target HPV-16 is present, it specifically hybridizes with the cDNA to form a double-stranded structure. This double-stranded structure can be cleaved by EXO III. The cleaved cDNA is not adsorbed by WS2 nanosheets, generating a significant fluorescence signal. The released HPV-16 can then participate in the reaction again, achieving multiple rounds of fluorescence signal amplification. Under optimal conditions, the detection limit of the method is 0.35 pM. The method was successfully applied to the detection of HPV-16 in spiked serum samples, demonstrating the advantages of operational simplicity, high sensitivity, and good specificity. It provides a promising rapid detection method for clinical application research related to human papillomavirus.

Rare-earth-doped upconversion nanoparticles (UCNPs) exhibit upconversion luminescence upon excitation with infrared light and have been extensively utilized in the field of biosensing. In this study, a UCNPs-based biosensor with porous silicon (PSi) as the substrate was developed for the first time, enabling the detection of target DNA molecule concentration. First, a PSi substrate was prepared via electrochemical etching and subsequently functionalized to enable target DNA molecules to immobilize onto the inner walls of the PSi substrate’s pores. Then, UCNPs-labeled probe DNA molecules hybridized with the target DNA molecules, enabling indirect attachment of UCNPs to the inner walls of the PSi substrate. Subsequently, the sample surface is irradiated with a 980 nm laser. Upconversion fluorescence images of the sample, both before and after the biological reaction, are captured using an image acquisition device. Image processing software is employed to calculate the average change in grayscale values, enabling the determination of the molecular concentration of target DNA. The limit of detection (LOD) of this method for target DNA molecular concentration is 86 pM, demonstrating that it enables low-cost, highly sensitive, rapid, and convenient biological detection of target DNA molecules.

Forensic DNA profiling commonly relies on polymerase chain reaction (PCR) amplification followed by capillary electrophoresis (CE) or massively parallel sequencing (MPS), which requires expensive, laboratory-based equipment that depends on a stable power supply and is unsuitable for field applications. Here, we present a proof-of-concept assay that uses recombinase polymerase amplification (RPA) combined with exo probe detection for rapid, isothermal genotyping of insertion–deletion (InDel) markers. To the best of our knowledge, this study represents the first demonstration of forensic DNA typing using RPA coupled with exo probes. The reaction proceeds at 39 °C and combines amplification and detection in a single 20 min step. Thirteen DNA samples were genotyped in triplicate across eight InDel loci using allele-specific fluorescent probes. Genotypes were derived from differential endpoint fluorescence between matched and mismatched probes. Compared with benchmark genotyping, 97.07% of genotypes (n = 307) were correct at 1 ng DNA input. Accurate profiles were reliably obtained for DNA inputs as low as 250 pg, and partial profiles were still detectable at 31 pg. The results demonstrate that RPA-based InDel genotyping is fast, sensitive, and reproducible. With further optimization, such as refined probe design and selection of robust loci, the assay has clear potential to achieve complete accuracy and to be integrated into portable lab-on-a-chip platforms for rapid, field-deployable forensic identification.

Considering the present environmental concerns, nanomaterial-based methods should be applied to achieve the bioeconomic sustainability initiatives and climate change mitigation. Plants and plant extracts are one of the most underused biomass and bioactive ingredients resources. Moreover, nowadays crop loss is one of the main problems that the world faces, together with the depletion of natural resources, increasing population and limited arable land, leading to increased food scarcity and demand. To correctly attribute/use plant-based bioresources or to rapidly decide which farming operations should be performed before crop loss, we should be able to properly characterize plants or plant-based resources by the desired useful characteristics, such as (bio)chemical characteristics, rather than simply observing physical traits of plants (because, when these traits become visible, it may be too late for crop loss mitigation). Plant crops could be optimized, for example, using electrochemical methods that assess the nutrient uptake and nutrient use efficiency (NUE) or the oxidative stress burst encountered before crop loss, in order to improve crop yields and crop quality. Other different important analytes (such as hormones, pathogens, metabolites, etc.) or plant characteristics (such as genus, species, phylogenetic analysis, etc.) can be evaluated with these electrochemical sensors and methods. In the present review, we focus on the application of nanomaterials/nanotechnologies for the development of fast, accurate, accessible, cost-effective, sensitive and selective analytical electrochemical methods for the detection of different relevant biomolecules in plants or plant-related samples (plant extracts, plant cells, plant tissues, and/or plant-derived natural drinks/foods, as well as entire plants/plant parts), both in vivo vs. ex vivo and in situ vs. ex situ. This review systematically presents and critically discusses the outcomes of current electrochemical methods (both applied in the lab or as wearable/implantable sensors) and the future perspectives of these nanotechnology-based sensors, with an accent on wearable sensors for smart and precision agriculture, as real-world sensing technologies with significant practical impact. The novelty of this article is the abundance of electrochemical analytical parameters gathered and discussed, for such a large number of analyte categories.

Sulfamethazine (SMZ) is widely used in livestock production, and its residues can enter water and soil environments, posing potential risks to human health and ecosystems. This study focuses on environmental samples and constructs an AuNP-based colorimetric aptasensor using the SMZ1S aptamer for the rapid visual detection of SMZ. Under optimized conditions, the aptasensor showed a wide linear range from 0.05 to 0.4 µg/mL and a limit of detection of 0.039 µg/mL. Molecular dynamics simulations have demonstrated that the aptamer’s binding to SMZ is stable, providing a theoretical basis for the high selectivity of the aptasensor. Spike-and-recovery experiments yielded recoveries of 87.3–105.5%, 88.6–102.8%, and 87.5–103.4% for SMZ in lake water, tap water, and soil samples, respectively, with relative standard deviations of 5.9–8.3%, 8.0–10.6%, and 4.8–9.6%, showing good agreement with high-performance liquid chromatography (HPLC) results (R2 ≥ 0.981). Overall, the proposed aptasensor provides a simple and effective approach for rapid detection of SMZ in environmental samples.

Accurate identification and characterization of subcutaneous tumors are essential for improving breast tumor detection and treatment. This study introduces an innovative Tactile-Sensation Imaging System (TSIS) designed, implemented, and tested to detect and characterize subcutaneous inclusions simulating breast tumors. The system employs a multilayered polydimethylsiloxane (PDMS) optical waveguide that mimics the tactile structure of the human fingertip. By introducing light at a critical angle, the design enables continuous total internal reflection (TIR) within the flexible, transparent waveguide. When external pressure is applied, deformation of the contact area causes light scattering, which is recorded using a high-definition camera and processed as tactile images. Analysis of these images allows estimation of inclusion characteristics such as size, depth, and mechanical properties, including Young’s modulus. Analytical modeling and numerical simulations validated the optical performance of the waveguide, while experimental evaluations using realistic tissue phantoms confirmed the system’s ability to accurately detect and quantify embedded inclusions. The results demonstrated reliable estimations of inclusion dimensions, depths, and stiffness, verifying the system’s sensitivity and precision. The TSIS offers a noninvasive, portable, and cost-efficient solution for quantitative breast tumor assessment, bridging the gap between manual palpation and advanced imaging, with future enhancements aimed at improving resolution and diagnostic accuracy.

In on-site rapid detection, the electrochemical method boasts high sensitivity and rapid response capabilities, while the colorimetric method can provide intuitive visual readings suitable for on-site screening. Therefore, this study developed an innovative dual-mode electrochemical/colorimetric aptasensor for the accurate detection of malathion (MAL) in vegetables. The sensor combines magnetic Fe3O4@ZIF-8-DNA composites and CuZr-MOF-cDNA probes, enabling simultaneous detection of the target through electrochemical reactions and colorimetric changes. The introduction of CuZr-MOF not only enhances the sensor’s conductivity but also significantly amplifies the electrochemical signal through its catalytic properties. The magnetic Fe3O4@ZIF-8-DNA composite facilitates solid–liquid separation under an external magnetic field. When the target MAL is present, the aptamer binds to the target, causing the CuZr-MOF-cDNA probes to release from the composite, altering the number of free probes in the supernatant and generating varying intensities of colorimetric signals. Meanwhile, the MAL captured in the precipitate by the aptamer is quantitatively detected through electrochemical methods. Experimental results demonstrate that as the target concentration increases, the colorimetric signal intensifies while the electrochemical signal weakens, showing a good linear relationship between the two. The aptasensor’s limit of detection (LOD) for colorimetric and electrochemical modes was 1.57 × 10−11 M and 4.76 × 10−11 M, respectively, with recoveries ranging from 87.71% to 107.68% and relative standard deviations between 3.23% and 10.75%. This method exhibits high sensitivity, excellent selectivity, and strong reliability, providing a novel technique for the accurate quantification of MAL in vegetables, particularly suited for on-site rapid detection.