TMS-007 is a member of the SMTP congeners for the treatment of acute ischemic stroke. To support its preclinical study, LC-MS/MS methods for the quantification of TMS-007 in rat plasma and brain were developed. The samples were prepared by protein precipitation. A UPLC HSS T3 column (2.1 mm × 50 mm, 1.8 µm) was used to achieve chromatographic separation. An acetonitrile-water mixture containing ammonium acetate was used as the mobile phase. The methods were validated with regard to selectivity, calibration curve and lower limit of quantitation, accuracy and precision, matrix effect, extraction recovery, carryover, dilution integrity, and stability. The calibration ranges of TMS-007 in rat plasma and brain homogenate were 10.0-10 000 ng mL-1. There was no endogenous or cross interference in the biological matrices. Across these matrices, the intra- and inter-batch coefficients of variation and accuracy deviations for all QC samples met the acceptance criteria. The inter-batch coefficients of variation of the QC samples were ≤11.7% for plasma and ≤15.0% for brain. The inter-batch accuracy ranged from 97.8% to 104.1% for plasma and 97.1% to 102.3% for brain. No significant matrix effect was observed from the matrices (from 102.9% to 111.0% for plasma and from 114.3% to 118.9% for brain). The extraction recoveries of the methods at different concentrations were consistent and reproducible. For plasma, the coefficients of variation were ≤9.9%, and for brain, the coefficients of variation were ≤4.6%. The analyte was stable in different matrices under various storage conditions (room temperature for 8 h, 4 °C in the autosampler for 3 days, 3 freeze-thaw cycles and 7 days at -70 °C). The methods were successfully applied to a preclinical study in a transient middle cerebral artery occlusion rat model after single dose administration. The pharmacokinetic results of the study drug laid a foundation for its further development.
Wearable biochemical sensing is shifting sports physiology from intermittent laboratory sampling toward continuous, in situ readouts during training and competition. This review analyzes skin conformal systems that integrate capillary driven fluid routing, flexible sensor arrays, and wireless readout to capture dynamic changes in eccrine secretions relevant to performance management. We compare microchannel layouts that enable controlled filling, time resolved sampling, and evaporation suppression, and we discuss how material selection governs comfort and analytical fidelity, highlighting tradeoffs among polydimethylsiloxane, polyurethane, and polyethylene terephthalate substrates and their compatibility with scalable fabrication. Manufacturing pathways are assessed from soft lithography to laser cutting, three dimensional printing, and roll to roll processing for high throughput multilayer assembly. For transduction, we summarize enzymatic amperometric schemes based on lactate oxidase with hydrogen peroxide detection, including sensitivity, response time, and oxygen dependence, and we contrast these with emerging non enzymatic catalysts. We then detail potentiometric ion selective electrodes for sodium and potassium, focusing on ion selective membrane chemistry, solid state reference electrodes, Nernstian response, and dominant error sources such as drift and biofouling. System integration challenges, including chemical and electrical cross talk in multiplexed layouts, are linked to microfluidic isolation strategies and multiplexed electronics. Finally, we appraise validation practice, emphasizing the debated sweat to blood relationship, the need for synchronized comparative protocols, and the role of data analytics and machine learning for personalization, drift compensation, and prediction of thermoregulatory strain. Remaining barriers include long term stability, adhesion under motion, manufacturability, and regulatory evidence requirements in real world settings. Across these topics, the review emphasizes technology-specific limitations, engineering translation metrics, and realistic breakthrough directions rather than treating wearable sweat patches as a mature plug-and-play platform.
The increasing demand for portable, low-cost, and sustainable analytical methodologies has motivated the development of miniaturized electrochemical systems for pharmaceutical and clinical monitoring. In this work, an electrochemical method based on screen-printed carbon electrodes and nickel oxide-modified SPCE was optimized for the voltammetric determination of acetaminophen in pharmaceutical formulations and human urine using the DropSens configuration. Square-wave voltammetry, selected for its sensitivity and suitability for on-site analysis, enabled reliable quantification under alkaline conditions (pH 9.5). Both electrode types provided detection limits in the low-ppm range and broad linearity. Precision values varied depending on the matrix and electrode type, with RSDs generally below 20%. Accuracy ranged from 87 to 108% in drug formulations and exceeded 90% in urine samples. The DropSens platform produced analytical results comparable to those obtained with a conventional electrochemical cell while reducing sample and reagent consumption. Method validation for urine analysis was performed using in-tube solid-phase microextraction coupled to nano liquid chromatography with diode-array detection, which confirmed the reliability of the voltammetric method. Excretion profiles obtained by the proposed electrodes closely matched chromatographic data, with differences below 1% at the main excretion maxima (2 and 6 h after administration). Overall, the proposed miniaturized electrochemical approach provides a rapid, accurate, and sustainable alternative for the determination of acetaminophen in complex biological matrices, offering significant advantages in terms of portability, operational simplicity, and alignment with green analytical chemistry principles. The HEXAGON tool was used to support this last statement.
The presence of hexavalent chromium (Cr6+) and silver ions (Ag+) in pharmaceutical systems poses significant safety concerns due to their toxicity and bioaccumulation potential. Therefore, the development of rapid, reliable, and matrix-tolerant sensing platforms is highly desirable. Herein, ultra-small carbon quantum dots (CDs, ∼0.982 nm) were synthesized from Mahoniae Caulis extract and m-phenylenediamine (m-PD) as carbon and nitrogen sources. The natural heteroatoms and phytochemicals in Mahoniae Caulis endowed the CDs with abundant surface functionalities, strong green fluorescence, and excellent water dispersibility. Based on dynamic and static quenching effects, the CDs acted as a ratiometric fluorescent probe for simultaneous detection of Ag+ and Cr6+ with high sensitivity and selectivity. Good linear relationships were obtained for Ag+ (0-2.5 µM) and Cr6+ (0-40 µM) with detection limits of 0.03 µM and 2.2 µM, respectively. The probe also exhibited strong anti-interference capability in complex herbal matrices, and the results were consistent with ICP-MS analysis, confirming its analytical reliability. The ratiometric strategy enabled effective self-calibration and reduced environmental and instrumental interference compared with conventional single-emission probes. In addition, the CDs showed favorable biocompatibility and fluorescence properties. This work provides a sustainable approach for constructing multifunctional CDs and demonstrates their potential for heavy-metal monitoring and quality control in natural products and traditional Chinese medicines.
A hexafluoroisopropanol (HFIP)-induced salt-free catanionic surfactant supramolecular solvent (SUPRAS)-based direct immersion single-drop microextraction method was developed and coupled with high-performance liquid chromatography-ultraviolet detection to determine sulfonamides (SAs) and fluoroquinolones (FQs) in lake water samples. Dodecanoic acid and dodecyltrimethylammonium hydroxide were used as the salt-free catanionic surfactant pair for SUPRAS preparation, while HFIP served as the coacervate-inducing agent. Cryo-scanning electron microscopy revealed spherical micellar aggregates in the SUPRAS, which facilitated hydrophobic, hydrogen-bonding, and electrostatic interactions for analyte extraction. A novel holder, fabricated from a gel pen refill protective case, was designed to stably support the high-density SUPRAS droplet (up to 40 µL for 20 min), improving the precision and accuracy of the method. Sulfadiazine, sulfamethazine, norfloxacin, ciprofloxacin, danofloxacin, and enrofloxacin were selected as model analytes. Key factors affecting droplet stability and extraction efficiency were optimized. The method exhibited good analytical performance, with linearity (R2) > 0.9938 and limits of detection of 2.1-3.0 ng mL-1 for SAs and 0.8-1.9 ng mL-1 for FQs. Recoveries for spiked lake water samples were in the range of 81.2-106.3%, with intra- and inter-day relative standard deviations all below 7.2%. AGREEprep greenness assessment revealed that the proposed method achieved the highest score (0.45) among the compared methods, owing to its minimal organic solvent consumption, simple operation, low energy consumption, and low waste generation. Compared with reported methods, the proposed approach is simple, efficient, and environmentally benign, demonstrating strong potential for the trace determination of antibiotics in environmental water samples.
The development of sensitive, accurate methods for detecting amphetamine-type stimulants (ATSs) has become increasingly urgent, driven by the pressing need to protect human health and environmental safety. However, achieving efficient, selective detection in complex sample matrices remains a major analytical challenge. Magnetic solid-phase extraction (MSPE) offers notable advantages over conventional methods (e.g., LLE, SPE, and DLLME), including shorter extraction times, simpler procedures, and more rapid separation. In this study, a double-shell Fe3O4@SiO2@5F composite was developed via the deposition of a fluorine- and nitrogen-doped polymer onto Fe3O4@SiO2. The characterization confirmed the successful preparation of the coating and showed that the composite material has an adsorption capacity superior to that of traditional materials. An MRM-based GC-MS/MS method was established, for the enrichment and quantification of six ATSs, including AMP, MATM, PEA, MDA, MDMA and MDEA. This method exhibits good recoveries, with linear correlation coefficients (R2) ranging from 0.9974 to 0.9999. The limits of detection (LODs) and quantification (LOQs) were 0.66-1.11 ng mL-1 and 1.98-3.30 ng mL-1, respectively. The adsorption mechanism, explored through theoretical calculations, was consistent with experimental data: kinetics followed the pseudo-second-order (PSO) model, while isotherms followed the Langmuir model. Furthermore, density functional theory (DFT) calculations demonstrated that the adsorption process was synergistically driven by π-π, C-H⋯π, and hydrogen-bonding interactions. The composite demonstrated excellent stability across eight consecutive extraction-desorption cycles. Moreover, its successful use in real environmental water and biological samples confirmed its practical value. This study underscores the significant potential of the developed composite for the reliable monitoring and screening of trace drugs.
L-lactate is a key metabolite and biomarker with wide range of concentrations across human body fluids. Accurate and continuous monitoring of human lactate and lactate dehydrogenase (LDH) concentration is essential for sports, clinical, and diagnostic applications. Conventional enzyme-based sensors employing lactate oxidase or lactate dehydrogenase dominate current practice but face inherent limitations in stability, operating conditions, and production cost. On the other hand, affinity-based sensors can be promising alternatives. This review highlights recent advancements in affinity sensors for L-lactate and LDH detection such as the first reported L-lactate aptamers, their selection and characterization, and their integration into wearable electrochemical devices. Affinity sensors for lactate dehydrogenase (LDH) detection, including aptamer- and antibody-based assays, are also summarized. Advances in protein and small-molecule probes as well as MIP-based sensors are discussed with emphasis on sensitivity, selectivity, and potential for multiplexed, non-invasive monitoring. Overall, these sensors provide crucial insights into the development of accurate, cost-effective, and stable biosensing approaches for L-lactate and LDH detection in diverse settings.
Nanozymes hold great promise in point-of-care (POC) diagnostics. However, their lower catalytic activity has significantly limited their clinical applications. In this study, we report a surface engineering strategy based on charge-transfer ligand modulation to develop a trimetallic nanozyme (D-PtPdOs) with superior peroxidase-like activity, and integrate it with machine learning (ML) algorithms to enable ultrasensitive and intelligent detection of Pseudomonas aeruginosa (P. aeruginosa) in immunoassay platforms. By leveraging ligand-induced charge transfer, we precisely tuned the surface electron density of the nanozyme. The resulting D-PtPdOs nanozyme exhibits extraordinary catalytic activity, significantly outperforming natural horseradish peroxidase (HRP). Density functional theory (DFT) calculations reveal that d-histidine modification enhanced the adsorption capacity for hydrogen peroxide (H2O2) and lowered the activation energy barrier, thereby drastically increasing the maximum reaction rate (Vmax). This research establishes a versatile surface ligand engineering paradigm, offering a novel design framework to overcome the catalytic bottlenecks inherent in nanozymes. Due to its superior catalytic activity, the D-PtPdOs was successfully integrated into enzyme-linked immunosorbent assay (ELISA) and lateral flow immunoassays (LFIA) platforms for P. aeruginosa detection, achieving sensitivity enhancements of 14.58-fold and 250-fold compared with conventional HRP-ELISA and AuNPs-LFIA, respectively. Furthermore, by incorporating ML algorithms, the platform enables high-precision classification and quantitative prediction of P. aeruginosa infection levels in complex human blood samples, effectively mitigating signal uncertainties caused by matrix interference. This work establishes a foundation for the deep integration of immunoassays with intelligent software and portable devices, significantly advancing the development of smart point-of-care diagnostics.
N-Nitrosamines (NAms) are potent carcinogenic and mutagenic contaminants that may originate from both exogenous exposure and endogenous nitrosation. Human urine is an important non-invasive matrix for biomonitoring NAms exposure; however, the trace concentrations of these compounds and the complexity of the urinary matrix require highly sensitive and selective analytical methods. In this study, a highly automated method based on solid-phase extraction (SPE) coupled with gas chromatography-triple quadrupole tandem mass spectrometry (GC-MS/MS) was developed for the simultaneous determination of eight NAms in urine. By optimizing the SPE sorbent, sample loading strategy, and elution conditions, efficient enrichment and cleanup of the target compounds were achieved using only 4 mL of urine. The automated SPE procedure reduces manual handling, improves sample-preparation reproducibility, and enhances operational efficiency. Method validation showed the recoveries of 80.68% to 111.33%, with intra-day and inter-day precisions (RSDs) below 5.59% and 7.72%, respectively. The limits of detection (LOD) ranged from 0.00335 to 0.01 µg L-1. The method was applied to 60 urine samples from a community-based population, in which NDMA and NPYR showed the highest detection frequencies. These results indicate that the method is sensitive, repeatable, and analytically stable, and is suitable for biomonitoring trace-level human exposure to NAms.
Polycyclic aromatic hydrocarbons are persistent organic contaminants that are frequently reported at elevated concentrations in industrially and agriculturally impacted soils. Conventional PAH determination relies on solid phase extraction followed by chromatographic analysis, which offers high sensitivity but is limited by cost, time, and infrastructure requirements. In this study, a paper-based analytical device is developed as a complementary post extraction screening tool for PAHs at concentrations relevant to high loading soil environments. The device employs a horseradish peroxidase-hydrogen peroxide chromogenic substrate system, with colorimetric signals captured using a USB camera. Naphthalene, pyrene, and benz[α]anthracene were investigated as representative PAHs. The device exhibited concentration dependent responses over the 20 to 200 mM range, with limits of detection of 1.11 to 3.82 mM and signal development within 3 min. Optical characterization using UV-Vis and fluorescence spectroscopy supported the observed responses, while mechanistic and specificity studies indicated that signal generation was influenced by PAH-assay interactions under the applied reaction conditions. Application to soil matrix spiked samples following extraction demonstrated measurable responses that fell within the dynamic range of the calibration curves, although matrix-induced signal variation arising from co-extracted organic matter, residual extractants, and changes in optical scattering and wettability on the paper substrate was observed. These results demonstrate the feasibility of integrating enzyme-based paper devices into solid phase extraction centered analytical workflows as rapid and low cost screening tools for identifying elevated PAH levels in industrial and agricultural soils prior to confirmatory laboratory analysis.
Wearable biosensing technologies are advancing sports performance monitoring by enabling the continuous and real-time measurement of physiological and biochemical parameters. Among non-invasive biofluids, sweat has become the most widely studied medium due to its easy accessibility during physical activity and its presence of multiple relevant biomarkers. This review critically examines the recent developments in sweat-based wearable biosensing technologies and identifies the key challenges that hinder their transition from laboratory prototypes to practical sports-monitoring systems. The discussion includes a brief introduction to sweat generation, the important biomarkers present in sweat, and their significance in sports health monitoring. Various electrochemical sensing platforms designed for sweat analysis are reviewed, with an emphasis on their structural designs and operational mechanisms. Major application areas, including lactate monitoring for fatigue detection, electrolyte sensing for hydration assessment, and cortisol measurement for stress evaluation, are discussed. This review also highlights the important challenges, including sensor calibration, motion-related artifacts, variability in sweat composition among individuals, and long-term operational stability. Emerging approaches, including multimodal sensing, machine-learning-assisted data interpretation, nanomaterial-enabled sensors, and closed-loop feedback systems, are also discussed as potential solutions to improve the reliability and real-world applicability of sweat-based wearable biosensors for sports performance monitoring.
Surface-enhanced Raman spectroscopy (SERS) is increasingly emerging as a pivotal analytical technique in the field of clinical diagnostics. The diagnosis and monitoring of infectious diseases demand rapid, highly sensitive, and accurate detection methods. Recent innovations in novel SERS substrate materials and structural designs offer significant enhancement of SERS signals. This review summarizes the applications of SERS technology in the identification of various pathogenic microorganisms (including bacteria, viruses, fungi, atypical pathogens, and parasites), antibiotic susceptibility testing, and the detection of infection and inflammation biomarkers. It highlights the significant value of integrating SERS with other diagnostic platforms-such as PCR/CRISPR, lateral flow assays (LFAs), microfluidics, and microarray chips-for infectious disease detection. The constructed SERS detection platforms demonstrate notable advantages over traditional methods in terms of high sensitivity, rapid response, and multiplex detection capability. Finally, future development trends in SERS detection technology are discussed, focusing on its potential in point-of-care testing (POCT) and integration with artificial intelligence.
Polystyrene microplastics have emerged as a global environmental concern due to their widespread distribution and potential risks to ecosystems and human health. Accurate quantification of microplastics in water matrices remains challenging due to the limitations of conventional detection methods. This study adapted an incremental modification staining-spectrophotometry method for the quantitative detection of microplastic concentration in water, based on the adsorption of hydrophobic dyes onto the surface of microplastics, combined with UV-vis spectrophotometry. Under optimal conditions (pH = 7, room temperature, 1 µm polystyrene microplastics, deionized water, no interferents), there was a good linear relationship between absorbance and polystyrene microplastic concentration in the range of 5-100 mg L-1, with a correlation coefficient (R2) of 0.9933, a limit of detection of 0.76 mg L-1, and a spike recovery rate of 108.65%. Furthermore, the method exhibited strong anti-interference capability, retaining good linearity (R2 > 0.99) and recovery rates (>95%) under various environmental conditions, including temperatures of 25 °C and 45 °C, pH values of 3, 7, 11, and 13, and the presence of common coexisting ions (Ca2+, Na+, K+, SO42-, CO32-, and Cl-). Satisfactory linearity was also achieved in real water matrices such as tap water and lake water. Characterization using optical microscopy, scanning electron microscopy, and Fourier-transform infrared spectroscopy confirmed that the staining process involves physical adsorption without altering the chemical structure or morphology of the polystyrene microplastics. Compared with conventional methods, this approach offers advantages including operational simplicity, high sensitivity, good linearity, and strong environmental adaptability, making it a promising tool for rapid quantitative detection of small-sized polystyrene microplastics in water.
Near-infrared (NIR) spectroscopy combined with machine learning algorithms has been widely adopted for rapid assessment of grain quality attributes. However, conventional calibration models often suffer from overfitting and instability when applied to high-dimensional spectral data with limited sample sizes. In this study, we developed a novel bagging partial least squares (BA-PLS) algorithm for accurate and stable prediction of wheat protein content. A total of 394 wheat samples were collected and their NIR spectra from 950 to 1650 nm were acquired. The BA-PLS algorithm generates multiple bootstrap subsamples, trains PLS models on each subsample, and aggregates their predictions through averaging, effectively reducing prediction variance while preserving the low-bias properties of PLS. The performance of BA-PLS was comprehensively compared with that of standard PLS, support vector regression (SVR), and extreme gradient boosting (XGBoost). The results demonstrated that BA-PLS achieved superior predictive performance with a coefficient of determination (RP2) of 0.9600 and a root mean square error (RMSEP) of 0.3058%. Notably, while SVR and XGBoost exhibited severe overfitting with training to test R2 gaps exceeding 0.4045, BA-PLS exhibited excellent generalization with a minimal R2 gap of 0.0261. Furthermore, BA-PLS provided reliable prediction uncertainty estimates through the standard deviation of ensemble predictions. The proposed BA-PLS algorithm offers a practical and stable solution for rapid wheat protein quantification, with potential applicability to other cereal quality assessment tasks.
Kawasaki disease (KD) is a leading cause of acquired heart disease in children, requiring timely diagnosis. Circulating microRNAs (miRNAs) are promising non-invasive biomarkers for KD, but their accurate quantification is challenged by low abundance, high sequence homology, and complex biological matrices. Herein, we report a self-priming cascade amplification strategy using a structurally stabilized ternary hybridization probe for sensitive and reliable detection of KD-related miRNAs. The rationally designed probe integrates three oligonucleotides into a robust complex, suppressing nonspecific background signals. Upon target recognition, an orchestrated cascade is activated, including target recycling, polymerase-assisted extension, and nicking endonuclease-mediated exponential amplification, ultimately generating a G-quadruplex-enhanced fluorescence readout. Under optimized conditions, this method achieves a detection limit of 0.82 fM with a dynamic range from 1 fM to 10 nM, and excellent discrimination against mismatched sequences. The assay shows outstanding robustness in human serum, with recovery rates of 96.7-103% and strong correlation with qRT-PCR. Key innovations include the stabilized ternary probe that minimizes background noise and the multi-layered amplification cascade that enhances sensitivity, overcoming the trade-off between stability and efficiency in self-priming systems. This isothermal and robust platform holds great promise for early diagnosis of Kawasaki disease and can be extended to other nucleic acid biomarkers.
Biosensing technologies play a critical role across the healthcare, environmental monitoring, and food safety sectors. The in vivo sensing of biomolecules is challenging due to the non-biocompatibility of nano-microelectrodes. In this regard, lignocellulosic materials will have a significant impact on sensors owing to their outstanding properties. Although lignocellulose lacks conductivity, it can be modified with other metal nanoparticles or conductive polymers to improve its conductivity. By leveraging functionally applied nanomaterials with lignocellulose, promising flexible biosensors can be developed with enhanced sensitivity, selectivity, and versatility. This integration of lignocellulosic materials with nanomaterials enables advanced biosensors with improved performance, facilitated by their high surface area-to-volume ratios and suitability for biomolecule immobilization. Lignocellulosic nanofibrils exhibit thermal stability, absorption in the ultraviolet-visible (UV-vis) region, water stability, and reduced moisture sensitivity and enhance sensor performance. Lignocellulosic materials have emerged as promising substrates for the development of next-generation biosensors. This review explores the suitability of lignocellulose for biosensing applications. Here, we discuss how plant-based materials have been used for biomolecule sensing. Lignocellulose has outstanding mechanical properties, which is why it can be used as a base material and sensing electrode to fabricate brain-on-chip and organ-on-chip devices. Because it is a plant-derived material, it also exhibits microfluidic properties. A cellulose skin-substituted natural polymer shows promise as a substrate for wearable sensors.
Seeds storage-year have a significant impact on high-oleic peanut seed vigor and quality. Therefore, it is essential to identify different storage-year seeds for planting, direct consumption, industrial processing, and marketing. In this study, hyperspectral images with 616 spectral bands (from visible light to near-infrared) were employed to classify different storage-year peanut seeds. To extract characteristic information for classification, we proposed a hybrid band selection (HBS) method based on the successive projection algorithm (SPA) by fusing the color-sensitive bands and moisture-sensitive bands. Then three classifiers, support vector machine (SVM), extreme learning machine (ELM), and K-nearest neighbors (KNN), were selected for storage-year classification. The experimental results demonstrated that the features extracted with the HBS method can obtain higher classification accuracy than other methods'. Specifically, the HBS-ELM model achieved the highest classification performance, with accuracy of 90.22%.
The detection of Influenza A Virus (IAV), Influenza B Virus (IBV), and Respiratory Syncytial Virus (RSV) presents significant diagnostic challenges due to the high similarity of clinical symptoms with other respiratory infections, leading to the need for multiplexed, rapid testing. Herein, we have established a one-pot, multiplex CRISPR/Dx detection system based on reverse transcription-recombinase polymerase amplification (RT-RPA) and CRISPR/Cas12a. It completes the detection within 30 minutes at a constant temperature of 40 °C, with the ability to detect 10 copies per µL of IAV, 10 copies per µL of IBV, and 8 copies per µL of RSV. No cross-reactivity between these respiratory viruses was observed. When equipped with our customized miniature device, it allows visual fluorescence readout without specialized equipment. Compared with conventional RT-qPCR and two-tube RT-RPA-CRISPR/Cas12a approaches, this one-pot detection system offers a simplified workflow and shorter detection time and enables visual detection, making it especially suitable for point-of-care testing and field deployment. In essence, our CRISPR/Dx system provides a novel and practical molecular diagnostic strategy for rapid and multiplex detection of respiratory pathogens to improve patient management, rational antiviral use, and epidemic control.
Urinary tract infections rank among the most frequent bacterial infections globally, with the majority caused by uropathogenic strains of Escherichia coli (E. coli). Effective management requires timely and reliable diagnostic tools. This work introduced a compact smartphone-coupled microfluidic platform that incorporated dual filtration for sample preprocessing and Au@Pt nanozyme-catalyzed colorimetric immunoassay for sensitive and rapid detection of uropathogenic E. coli in urine. The dual-membrane microfluidic chip enables efficient bacterial separation and enrichment, while Au@Pt nanozyme probes conjugated with E. coli-specific antibodies catalyze the chromogenic substrate to generate a visible blue signal. The smartphone application quantitatively analyzes the chromogenic intensity. The proposed platform achieves a limit of detection of 105 CFU mL-1 within 15 min. Validation using 40 clinical urine samples demonstrated 100% agreement with the standard quantitative urine culture method. Owing to its simplicity, portability, and high diagnostic accuracy, the detection platform provides a promising point-of-care testing approach for rapid bacterial diagnostics. Furthermore, its modular design allows easy adaptation for enrichment and detection of other pathogenic bacteria or viruses in diverse biological samples, paving the way for intelligent, field-deployable biosensing in clinical and public health applications.
Urinary metabolites and their concentrations serve as biomarkers for identification of metabolic pathways that relate to specific diseases; therefore, fast and accurate quantification of the metabolites in urine is essential in health assessment and diagnosis. As many urinary metabolites are of polar nature, hydrophilic interaction liquid chromatography (HILIC) has been used over the last several years because it offers faster and more reproducible analyses compared to traditional techniques such as reversed-phase chromatography or capillary electrophoresis. In our study, we developed a HILIC method by using a 3 cm analytical column in connection with tandem mass spectrometry detection for quantification of 10 urinary metabolites including creatinine as the reference for normalization. As all tested metabolites contain ionizable functional groups, pH of the mobile phase was optimized to achieve baseline separation of 2 isomeric pairs (1-methyl-4-imidazoleacetic acid/1-methyl-5-imidazoleacetic acid and 1-methylhistidine/3-methylhistidine) and to obtain overall better separation efficiency resulting in a 7 min analysis. The developed method was validated in terms of sensitivity, carry-over, linearity, matrix effects, accuracy, and precision. The metabolite concentrations in healthy subjects determined by the developed method correspond well with the normal reference values found in the literature. Moreover, the method was tested on a small cohort of COVID-19 patients, where it enabled identification of differences in metabolite levels. Thus, the developed method has potential to be used routinely in a diagnostic field for high-throughput analysis of urine samples.