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Dopamine (DPA) plays a critical role in regulating various physiological systems, and its abnormal levels are associated with disorders such as neurodegenerative diseases, psychiatric disorders, drug addiction, and certain cancers. Therefore, the development of reliable, rapid, and sensitive methods for DPA determination is crucial for early diagnosis and effective treatment, particularly in clinical settings and point-of-care testing (POCT). In this study, a "turn-on" fluorescent probe was developed using a terbium-based metal-organic framework (Tb-MOF) constructed from dual ligands. This design addresses the drawbacks of traditional turn-off MOF sensors by enabling fluorescence enhancement upon dopamine (DPA) recognition, leading to improved sensitivity and selectivity. The dual-ligand Tb-MOF design strategically combines terephthalic acid for efficient Tb³⁺ sensitization with 2-methylimidazole facilitating selective dopamine recognition. After only 3 min of incubation in phosphate buffer (pH 7.00) at room temperature, the assay enables immediate detection of DPA in plasma and urine samples following simple protein precipitation, supporting its suitability for POCT. This represents a clear advantage over many previously reported fluorescent probes that require longer incubation times or elevated temperatures. The proposed probe demonstrated high selectivity and sensitivity for DPA determination in human plasma and urine, with a wide linear range of 1.00 × 10^-9 - 1.00 × 10^-7 M and a low limit of detection of 2.05 × 10^-10 M. These advantages highlight the potential of this dual-ligand Tb-MOF probe as a valuable platform for point-of-care testing aimed at early detection and monitoring of DPA-related disorders.

In the present work, we present a 3D-printed mini electrochemical cell designed using polylactic acid (PLA) filament. This platform incorporates a fully integrated electrochemical cell, where the working, reference, and counter electrodes are entirely fabricated from pencil graphite electrodes (PGE). A Reference electrode for the three-electrode system was obtained by applying conductive silver ink onto a pencil graphite electrode. In this study, an electrochemical immunosensor was developed for the detection of carcinoembryonic antigen (CEA). The sensor surface was functionalized using appropriate chemical agents, followed by the immobilization of Anti-CEA for specific recognition. CEA determination was performed by monitoring the interactions on the sensor surface with the Electrochemical Impedance Spectroscopy (EIS). Combined with a 3D-printed electrochemical cell, the CEA immunosensor exhibited a linear response to CEA from 4.0 ng mL^-1 to 250 ng mL^-1 with a detection limit (LOD) of 1.2 ng mL^-1. Also, the proposed immunosensor was successfully applied for CEA detection in commercial human urine samples, achieving CEA detection recoveries ranging from 93 % to 98 %. The developed electrochemical biosensor shows promise for accurately detecting CEA in real samples, providing a precise method that could be valuable for clinical tumor detection.

Electrochemical detection of microRNA biomarkers in tumor interstitial fluid is promising for accurately assessing tumor burden, but remains challenging due to interference and reproducibility limitations. Here, a magnetic separation-integrated electrochemical biosensor utilizing the synergy of photo-electronic cascading was developed for anti-interference and highly reproductive detection of miRNA-21. Polydopamine-coated Fe₃O₄ magnetic nanoparticles were adopted as electroactive probe carriers and photoactivatable electron donors. The assembled probes decoupled with nanoparticles through catalytic hairpin assembly reactions in response to miRNA-21. Magnetic separation discarded interferents and decoupled probes, reducing the signal noise. A temporary magnetic field during sample addition confined the nanoparticles on the working electrode as aggregates, improving their collision probability and sustainability in subsequent electrochemical measurement. Near-infrared irradiation enhanced electron transfer, accelerating synergy of photo-electronic cascading between polydopamine electron donors and non-decoupled probes for signal reporting. This biosensor exhibited a detection limit of 148 fM with a relative standard deviation of 1.60 % in standard samples, as well as higher fidelity and smaller variance (7.64 %) than traditional RT-qPCR assay (24.31 %) in tumor samples. This work provided an approach to remove the interferents in the complex physiological environment and achieve high-reproducibility electrochemical detection, hopefully benefiting the application of tumor evaluation.

Acute kidney injury (AKI) is a swiftly advancing condition that may result in kidney failure and pose a significant threat to life. Therefore, diagnosis of AKI is crucial for treating AKI and preventing the worsening of the condition. We developed a near-infrared fluorescent probe, CyO@CD-Ser, designed for the diagnosis of AKI. This probe targets kidney injury molecule-1 (KIM-1) and is activated by ONOO^-. It consists of l-serine-modified β-cyclodextrin and compound CyO. In this system, ONOO^-, a novel biomarker for AKI, triggers the oxidation of CyO to produce compound CyOH, resulting in a pronounced enhancement of near infrared fluorescence, offering a clear, visual signal for the detection of AKI. Through its modification with l-serine, β-cyclodextrin is able to efficiently penetrate cells by targeting KIM-1 receptors, thereby enhancing its specificity and effectiveness. In vitro and in vivo experiments demonstrate the probe's high sensitivity and biocompatibility, with a detection limit of 42.7 nM for ONOO^-. Fluorescence is detectable in cisplatin-induced AKI after 12 h, becoming significant at 24 h. This suggests that CyO@CD-Ser can identify AKI earlier than traditional methods, offering a valuable tool for studying and diagnosing drug-induced AKI. Consequently, the probes we constructed can detect AKI early by precisely targeting and identifying biomarkers, preventing the deterioration of the condition, and providing an opportunity for early treatment for patients.

Hemorrhagic fever with renal syndrome (HFRS), caused by Hantaan virus, poses a serious public health threat. Current diagnostic methods remain limited by low sensitivity, complex procedures, and high sample requirements. To address this, we developed a highly sensitive single-molecule biosensor using multi-fluorophore nucleic acid probes and STORM imaging for the detection of Hantaan virus RNA. The probe was synthesized via PCR incorporating EdUTP, enabling site-specific coupling of multiple Cy5 fluorophores through copper-catalyzed click chemistry. This multi-fluorophore probe, combined with magnetic beads and a capture sequence, specifically targeted viral RNA and enabled quantification by super-resolution imaging. Compared to conventional single-fluorophore probes, our system exhibited a significantly lower detection limit of 57.54 aM. Notably, this is the first application of STORM to a single-molecule viral RNA detection platform. The method offers a broadly applicable, ultrasensitive strategy for early clinical diagnostics of Hantaan virus and potentially other pathogens.

Microbial transglutaminase (mTG) is widely used in the food industry to enhance the appearance and texture of meat and fish products, as well as the smoothness and richness of dairy products. However, the undisclosed excessive addition of mTG contributes to various health issues, including celiac disease with intestinal leakage, anemia, osteoporosis, dermatitis, and other parenteral symptoms. In this study, we developed a novel method combining gold nanoparticles (AuNPs), machine learning, and deep learning to study mTG activity in both aqueous solutions and diverse processed foods. Our results demonstrate that this colorimetric method, based on bifunctionalized AuNPs, exhibits sufficient sensitivity to detect pure mTG down to 0.01U and spans a detection range from 0.01U to 1U. Based on the colorimetric changes of gold nanoparticles, we constructed a dataset of 648 mTG concentration-absorbance data points from 6 different food types. We employed machine learning algorithms, including Decision Tree (DT), Random Forest (RF), and Multilayer Perceptron (MLP), to predict mTG concentration based on the colorimetric signal in various foods. Notably, the MLP model achieved a high prediction accuracy of 0.96. Blind tests on six types of supermarket-purchased meat, seafood, and dairy products showed predictions consistent with expected mTG levels. This study establishes an efficient strategy for the identification and prediction of mTG activity in a wide range of food products.

The rising incidence of liver cancer highlights the urgent need for novel therapies targeting crucial molecular mediators such as tubulin, a key protein involved in cancer cell proliferation. This study aims to address this need through a robust pipeline combining QSAR, molecular docking, dynamics, and ADME to identify new promising anti-liver-cancer agents, with a focus on virtual screening of purchasable Aldrich® Market Select phenanthrene analogs. A QSAR model with 92.7 % predictive accuracy highlighted HeavyAtomCount and Chi1n as pivotal structural descriptors correlating with anti-proliferative activity in HepG2 cells. Subsequently, QSAR-based virtual screening enabled the identification of top candidates based on their anti-proliferative potential. Virtual screening via molecular docking prioritized compound 31, which exhibited exceptional binding affinity (-8.684 kcal/mol) at tubulin's colchicine site. ADME profiling confirmed favorable pharmacokinetics and low BBB permeability for lead candidates. Molecular dynamics (MD) simulations (200 ns) further validated compound 31's stability, indicative of a tightly bound conformation. By integrating QSAR, docking, ADME, and MD, this work establishes a computationally rigorous pipeline for anticancer drug discovery, offering phenanthrene-based scaffolds as candidates for in vitro testing. These results not only elucidate structure-activity principles for tubulin inhibition but also provide a pipeline for accelerating drug discovery, especially novel anticancer agents.

In this study, a dried-droplet LIBS methodology for determining cadmium in cow milk has been developed. The performance of the methodology was shown by standard and real protein samples. A standard protein, bovine serum albumin (BSA), and whey protein extracted from skim cow milk were incubated in standard Cd solutions, and the complex solution was filtered through cut-off filters by centrifugation. The unreacted cadmium in the filtrate and Cd-bound protein in the filtered fraction were loaded separately onto a Si-wafer substrate and analyzed via dried-droplet LIBS methodology. Measurements were performed at reduced pressures by taking advantage of the signal enhancement effect. The optimum pressure for most Cd emission lines was found to be 100 mbar. It has been shown that the dried-droplet LIBS methodology at reduced pressures can be used for the identification and determination of free and protein-bound Cd in the whey matrix. The concentration-based detection limit of Cd bound to whey proteins was determined to be 20.2 ng mL^-1, which corresponds to as low as 10 pg in absolute amount with a sample volume of 500 nL. The LOQ value is estimated as 67.3 ng mL^-1 and 33.3 pg, in terms of concentration unit and absolute amount, respectively. The use of small sample volumes is important in the analysis of limited amounts of samples, such as body fluids. Preconcentration studies with multiple loadings of the sample on the same spot resulted in improvements in concentration-based detection. 8 ng mL^-1 Cd in the whey matrix that could not be determined by a single droplet loading due to being below the detection limit; could be determined after 10 consecutive loadings. The methodology may also be applied to the determination of other toxic metals bound to proteins for food quality control.

Hydrogel-based materials for e-skin applications have aroused tremendous attention due to their ability to simulate human skin's sensory capabilities and possess mechanical properties comparable to those of skin. When used as sensors attached to the skin, hydrogels are inevitably subject to damage, highlighting the need for self-healing properties. Furthermore, the lack of recyclability in traditional hydrogel sensors is detrimental to sustainability. To address this issue, we developed a hydrogel based on multiple noncovalent bonds and ferric ion/tannic acid redox system, combined with polyvinyl alcohol as a reinforcing skeleton and low polymerization of polyacrylic acid. This design endows the hydrogel with excellent self-healing properties, easy recyclability and enhanced mechanical properties. Additionally, as a strain sensor, it exhibits competitive performance including high sensitivity, rapid response time and excellent sensing stability. With these remarkable characteristics, the hydrogel demonstrates significant potential as a sensor for sustainable e-skin applications.

Accurate and synchronized assessment of biochemical parameters, such as biomarker concentration and body fluid viscosity, is crucial for advancing early disease detection and health management. Conventional biomolecular multiparameter detection methods often rely on multiple sensors or analytical techniques, which introduce cross-talk between sensing modalities, data inconsistencies, and complex calibration requirements, ultimately compromising detection precision and adaptability. We propose a streamlined detection approach that leverages a single uncoated Quartz Crystal Microbalance (QCM) sensor to monitor the dynamic magnetized motion of biomolecules under multimodal magnetic field modulation. Unlike conventional QCM methods that rely on static mass loading effects, this approach enables the sensor to capture motion signals that encode information about biomolecule concentration and base liquid viscosity. A backpropagation (BP) neural network is employed to model the nonlinear coupling between these motion-derived signal characteristics and the target biochemical parameters. The proposed method is validated using prostate-specific antigen (PSA) as a biomolecular model analyte. Experimental results from blind tests, where both concentration and viscosity were simultaneously unknown, demonstrate a prediction accuracy of 90 % for concentrations ranging from 0.01 to 1000 ng/mL and 87 % for viscosities between 1 and 6 cP. By integrating multimodal magnetic modulation with QCM-based motion sensing and machine learning, the BP-MMM-QCM technique provides a versatile and high-precision solution for biomolecule analysis. Accurate detection of biomolecule concentrations is essential for early disease diagnosis as well as monitoring disease progression and therapeutic responses. This approach overcomes the limitations of conventional QCM methods and enables real-time, multi-parameter detection in a single assay, making it a promising tool for disease diagnostics and health monitoring applications.