Microscopic hyperspectral imaging (MHSI) was employed to acquire particle-resolved hyperspectral information for the quantitative analysis of adulteration in powdered foods. Lotus root powder adulterated with maize starch and cassava starch was used as a model system. To characterize the complex spectral patterns associated with particle scattering, three hybrid deep sequence models, namely CNN-LSTM-Attention, CNN-GRU-Attention, and CNN-BiLSTM-Attention, were developed and systematically compared with conventional models. An optimal analytical workflow was further established by evaluating six spectral preprocessing techniques and four wavelength selection methods. Among the evaluated models, CNN-BiLSTM-Attention exhibited the best predictive performance, achieving an R2 value of 0.9457 and an RMSE value of 1.3871 on the prediction set. When combined with pseudo-color visualisation, the optimized model enabled pixel-wise mapping of predicted adulteration levels across the sample. Within regions exhibiting stable imaging quality, the pseudo-color distribution progressively shifted toward colors corresponding to higher predicted concentrations as the adulteration level increased, allowing the spatial variation in predicted adulterant concentration to be intuitively visualized. These results demonstrate the potential of MHSI coupled with deep learning for quantitative adulteration analysis and provide a promising analytical strategy for the quality control of lotus root powder and other high-value powdered food products.

Traditional cancer therapies are limited by severe side effects and therapeutic challenges, while noble metal nanozymes offer promising enzyme-like therapeutic potential but remain constrained by poor aqueous stability. In this work, palladium-gold bimetallic nanoparticles (Pd3Au-LCLP NPs) with peroxidase-like activity were constructed using Leonurus cardiaca L. polysaccharide as both a reducing agent and stabilizing template, and further loaded with doxorubicin (DOX) to form Pd3Au-LCLP NPs-DOX. The resulting nanoplatform exhibited excellent water dispersibility, structural stability, anti-protein adsorption capability, and enhanced photothermal conversion performance. Under 808 nm laser irradiation, Pd3Au-LCLP NPs-DOX achieved a cumulative DOX release of 81% at pH 5.5. In vitro, it showed significant cytotoxicity against A549 and HeLa cells. In vivo, the tumor inhibition rate reached 82.6%. Overall, this combined therapeutic strategy integrating chemodynamic therapy (CDT), photothermal therapy (PTT), and chemotherapy (CT) demonstrated strong synergistic anti-tumor efficacy.

Mycotoxins are toxic secondary metabolites produced by fungi that frequently contaminate grains, nuts, and other foodstuffs. Their widespread occurrence causes significant economic losses and poses a serious threat to human and animal health. Traditional analytical methods relying on complex instruments often fail to meet the requirements for rapid on-site screening and remain limited by cost, operational complexity, and restricted on-site deployment capabilities. Lateral flow immunoassays (LFIAs) are important tool for the rapid detection of mycotoxins due to their simplicity, portability, and low cost. This review provides a critical overview of the latest development in signal readout architecture for LFIA platforms, including single-signal platforms based on colorimetry, fluorescence, chemiluminescence, and surface enhanced Raman scattering (SERS); dual-mode system integrating colorimetric-fluorescence or colorimetric-photothermal readings; and multi-mode platforms that combines three or more orthogonal signals. It also discusses the mechanism of functional nanomaterials in signal amplification, with particular emphasis on gold and fluorescent nanomaterials, nanozymes, metal organic frameworks, magnetic nanoparticles, and layered assembled plasmonic probes. The integration of portable readers, smartphone imaging, and machine learning algorithms, and high-throughput multiplexing formats is also summarized. In summary, future priorities have been identified, including multifunctional nanoprobes, intelligent on-site deployment devices, and AI assisted data interpretation. This review provides a systematic framework for the rational design of highly sensitive, reliable, and field-deployable LFIA technologies for mycotoxin monitoring.

Organophosphorus pesticides (OPs) are widely used in modern agriculture to ensure crop yields; however, their high chemical stability and environmental persistence pose significant risks to human health and ecosystems through food chain bioaccumulation. Traditional detection methods, such as chromatography and immunoassays, are often limited by high instrumental costs, complex sample pretreatment, and lengthy procedures, while conventional degradation strategies suffer from poor selectivity and harsh reaction conditions. Herein, we report a bifunctional nanozyme system that integrates polyvinylpyrrolidone-modified copper nanoclusters (PVP-CuNCs) with MnO2 nanosheets to achieve simultaneous dual-mode detection and catalytic degradation of OPs. The fluorescence mode exploits the acetylcholinesterase (AChE) inhibition mechanism, in the presence of OPs, thiocholine (TCh) production is suppressed, preserving the MnO2-mediated fluorescence quenching of PVP-CuNCs, whereas in OPs-free conditions, TCh reduces MnO2 to Mn2+, leading to fluorescence recovery. Concurrently, the colourimetric mode utilises the intrinsic peroxidase-like activity of the composite to catalyse the oxidation of 3, 3', 5, 5'-tetramethylbenzidine (TMB), enabling rapid visual readout. This dual-mode approach offers complementary signal validation, achieving detection limits as low as 0.06 nM (fluorescence) and 1.3 nM (colorimetry) across a broad linear range. Furthermore, the PVP-CuNCs@MnO2 composite efficiently activates peroxymonosulfate (PMS) to generate reactive oxygen species, enabling approximately 99.8% degradation of OPs within 12 min under ambient conditions. By bridging on-site detection with rapid catalytic remediation in a single platform, this integrated system provides a cost-effective, efficient, and sustainable solution for ensuring food safety and environmental remediation.

Accurate retention time (RT) prediction provides chromatographic evidence complementary to precursor mass and MS/MS spectra in LC-MS metabolomics. Such evidence is particularly valuable for metabolite annotation, where multiple structurally plausible candidates may exhibit similar spectral signatures. Here, we present TC-TopoRT, a tautomer-consistent and topology-aware framework for RT prediction in LC-MS metabolite annotation. TC-TopoRT constructs paired molecular views from the dataset-provided SMILES and a conservative tautomer-standardized representation generated under identity-preserving checks. Each view is lifted into a ring-aware cell complex and encoded by a cellular Weisfeiler-Lehman network (CWN) to learn topology-aware molecular representations. The resulting paired-view predictions are then integrated through a leakage-free out-of-fold fusion protocol without using independent-test labels for model selection, calibration, or stacker fitting. On the retained-compound SMRT benchmark, TC-TopoRT achieved a mean absolute error of 25.06 s. A five-seed ensemble further reduced the error to 24.92 s. Across six PredRet chromatographic datasets, TC-TopoRT reported lower MAEs than the cited reference values on four systems and remained within 1.92 s on the other two. With RT thresholds calibrated on fixed development queries held out from calibration-model fitting and disjoint from final evaluation, TC-TopoRT reduced candidate spaces by 41.05% on MetaboBase and 30.35% on RIKEN-PlaSMA while retaining 42/45 (93.33%) and 84/85 (98.82%) true candidates, respectively. It also maintained or improved Top-k prioritization relative to the original MS-FINDER ranking. These results support the use of tautomer-consistent and topology-aware RT prediction as complementary chromatographic evidence for LC-MS metabolite annotation.

The poor stability of natural enzymes and the limited catalytic activity of conventional nanozymes remain major challenges in the field of colorimetric glucose sensing. In this work, a high-entropy hierarchical nanosheet-assembled nanozyme (FeCoNiZnPt-HEHN) composed of Fe, Co, Ni, Zn, and Pt was successfully constructed through a solvothermal strategy. The as-prepared nanozyme exhibits a three-dimensional hierarchical microrod architecture assembled from ultrathin nanosheets, with multiple metal elements uniformly distributed throughout the structure. The synergistic catalytic centers involving Fe2+/Fe3+ mixed-valence species and low-valence Pt0 species were established within the high-entropy framework. Steady-state kinetic analysis demonstrated that FeCoNiZnPt-HEHN possesses excellent affinity toward both hydrogen peroxide (H2O2) and 3,3',5,5'-tetramethylbenzidine (TMB), exhibiting superior peroxidase-like catalytic activity compared with many previously reported nanozyme systems. This enhanced catalytic performance is attributed to the synergistic effects of multimetallic electronic coupling, Fe-based redox cycling, and Pt-mediated interfacial charge transfer, which collectively promote H2O2 activation. By integrating FeCoNiZnPt-HEHN with glucose oxidase (GOx), a cascade colorimetric glucose sensing platform was developed. The proposed system achieved a detection limit of 4.3 μM for glucose and satisfactory recoveries (95.0-101.9% with RSDs below 5.13%) in spiked artificial serum samples. The glucose specificity of the cascade sensing system is mainly provided by the substrate recognition capability of GOx. These results demonstrate that the proposed cascade system (GOx + high-entropy nanozyme) holds great potential for rapid glucose detection in bioanalytical applications.

Due to the role of formaldehyde (FA) in environmental pollutants and as an important intracellular signaling molecule, there is a pressing need to come up with highly sensitive and selective detection methods that can be applied to diverse fields. In this case, we have rationally developed and synthesized a new naphthalimide fluorescent probe Np-FA that is designed and customized to multi-scenario FA sensing. Probe Np-FA has good selectivity, very sensitive sensing capability and responds quickly (less than 10 s). The suggested sensing mechanism was verified through density functional theory (DFT) calculations and high-resolution mass spectrometry (HRMS) analysis. Leveraging its impressive performance, probe Np-FA was effectively made into portable test strips and fluorescent sensing films to visually detect gaseous FA and aqueous FA, respectively. Moreover, Np-FA was also used successfully to measure trace FA in actual water samples. The probe has great biocompatibility and it can be used in real time fluorescence imaging of both endogenous and exogenous FA within living cells. It is important to note that Np-FA proved useful to monitor dynamic changes in FA concentrations in a drug-induced model of hepatotoxicity and therefore, it has a good potential to investigate the functions of FA in physiological and pathological states. The probe created in this work is highly versatile and trustworthy, and its application prospects are very promising, starting with environmental surveillance, and ending with sophisticated biological systems.

Diabetic retinopathy (DR) is a major microvascular complication of diabetes, yet accessible molecular tools for early-stage screening remain limited. Circulating microRNAs are promising non-invasive biomarkers for DR, but their reliable quantification in serum is hindered by low abundance, matrix interference, and limited robustness of single-signal assays. Here, we report a Cas12a-RCA ratiometric sensing platform (CRRSP) for the quantitative detection of the DR-associated biomarker miR-1281. The assay is constructed using a target-triggered cascaded strand-displacement circuit that enforces sequential molecular recognition prior to amplification, thereby improving specificity. Upon target recognition, rolling circle amplification (RCA) generates DNA products that simultaneously produce a thioflavin T (ThT) fluorescence signal via G-quadruplex (G4) formation and sequester the Cas12a/gRNA Activator strand to suppress ROX fluorescence. This dual-channel ThT/ROX readout enables intrinsic ratiometric normalization, effectively compensating for matrix effects and experimental variability. The workflow operates under sequential constant-temperature steps without PCR-type thermal cycling. The assay achieves a detection limit of 0.83 pM with high selectivity and shows excellent agreement with RT-qPCR in clinical serum samples from healthy controls, glaucoma patients, and patients with non-proliferative diabetic retinopathy (NPDR) or proliferative diabetic retinopathy (PDR).

Hydroquinone (HQ) is a typical toxic phenolic pollutant that can severely disrupt the ecological balance of aquatic ecosystems and threaten human health. Therefore, it is imperative to develop simple and portable analytical techniques for the rapid on-site monitoring of HQ in aquatic environments. Here, the carbon-supported cobalt-nickel oxide nanozymes (CoNiOx@C) with enhanced peroxidase-like activity was synthesized using a CoNi bimetallic metal-organic framework (CoNi-MOF) as a precursor followed by annealing process. The significantly enhanced peroxidase-like activity of CoNiOx@C was derived from the synergistic effect of highly exposed Co/Ni-O-C bimetallic active sites and the in situ constructed carbon layer network, which effectively overcomes the inherent performance limitations of conventional nanozymes. Meanwhile, a catalytic signal amplification strategy was proposed based on the peroxidase-like mimetic mechanism of CoNiOx@C and the reducibility of the phenolic hydroxyl group, enabling the colorimetric detection of HQ. The colorimetric sensor developed showed good linearity in the range of 0-200 μM, with a detection limit as low as 0.068 μM. Interestingly, a portable visual detection platform was developed by integrating paper-based chips with smartphone, which achieves the sensitive the detection of HQ in real water samples. This study not only provides a facile synthetic route for MOF-derived bimetallic nanozymes but also offers a new approach for the development of low-cost, easy-to-operate visual on-site detection strategies.

Multiplex detection of metal ions is often challenged by their similar charge states and ionic radii, which interfere with for simultaneous analysis and cause the low selectivity of conventional lock-and-key recognition elements. Herein, a label-free and intelligent fluorometric array has been developed for simultaneously probing multiplexed metal ions by utilizing the conformational diversity of DNA structures, including G-triplex, G-quadruplex, and dimeric G-quadruplex DNA. The sensing mechanism relies on thioflavin T intercalation, which converts metal-binding-induced DNA conformational changes into quantifiable fluorescence signals. The resulting fluorescence fingerprints were first visualized using principal component analysis and subsequently classified using supervised machine-learning algorithms. A comparative evaluation of multiple models reveals that the support vector machine achieves the highest classification performance for selective identification of multiple metal ions. The developed fluorometric array can accurately distinguish the samples containing various metal species (Fe3+, Co2+, Hg2+, Ni2+, Zr4+, Ag+, and Zn2+) over a wide range of concentrations (10-200 μM), with a 100% accuracy in blind validation tests. Subsequently, the feasibility of the array for detecting multiple metal ions in mixtures, as well as in lake water and tap water, is also demonstrated. As compared with most current analysis technologies, this method offers the desirable advantage of simultaneous discrimination of multiple metal ions including the heavy and non-heavy ones. Therefore, this study establishes a powerful, efficient, and cost-effective platform for the simultaneous and intelligent detection of multiple metal ions, showing strong potential as a rapid on-site screening tool for preliminary risk assessment of metal contamination.

Accurate and sensitive determination of selenium (Se) in tea leaves is analytically challenging due to its low natural abundance and matrix-derived spectral interferences. This study evaluated the performance of inductively coupled plasma triple quadrupole mass spectrometry (ICP-QQQ-MS) under different collision and reaction cell gas conditions for isotope-specific Se determination in tea leaves. Four Se isotopes, including 77Se, 78Se, 80Se, and 82Se, were assessed using He, high-energy He (HEHe), H2, O2, and O2 + H2 modes. He mode showed poor accuracy, with a substantial over-recovery of 125.82-318.37%, suggesting unresolved polyatomic interferences. Increasing the helium flow in HEHe mode improved recoveries to 91.28-127.59%, but isotope-dependent variability remained. In contrast, H2 mode achieved acceptable recoveries of 92.43-104.48%, demonstrating effective interference removal during on-mass measurement. The O2-based mass-shift approaches, including O2 and O2+H2 modes, further improved selectivity by converting Se+ to SeO+ product ions, enabling measurement at shifted masses and reducing direct mass overlap from REE-related interferences. The optimized method achieved limits of detection at 0.011-0.71 μg/L and limits of quantification at 0.039-2.35 μg/L. Method accuracy was validated using green tea certified reference material, with measured Se content consistent with the certified value of 0.090 ± 0.030 μg g-1. Application to tea leaf samples enabled reliable Se quantification in a complex plant matrix. Overall, ICP-QQQ-MS cell gas optimisation and isotope selection are essential for accurate Se quantification, impacting sensitivity in tea leaves. The developed approach provides a robust analytical basis for Se analysis in tea and potentially other plant matrices.

Accurate quantification of H2O2 released by tumor cells is important for characterizing cancer-associated oxidative stress, yet conventional biosensors often face an intrinsic trade-off between catalytic activity and structural stability. Herein, we present a rational strategy for real-time electrochemical monitoring of cancer-associated oxidative stress species through the in-situ fabrication of ultrasmall Pt nanozymes confined within the mesoporous silica shell of gold bipyramids (AuPt@SiO2). In this architecture, the Au core provides conductive nucleation and anchoring sites, the mesoporous SiO2 shell regulates Pt growth and affords structural stabilization and molecular-sieving protection, and Pt serves as the catalytic center for H2O2 reduction. The resulting AuPt@SiO2 nanozyme exhibited pronounced peroxidase-like activity with a favorable Michaelis-Menten constant (Km) of 6.08 mM toward H2O2. After integration into a Nafion-based sensing interface, the enzyme-free sensor provided complementary linear ranges of 0.25-60 mM by cyclic voltammetry and 1-2100 μM by amperometry with a detection limit at 0.3 μM of H2O2. The electrochemical sensor based on AuPt@SiO2 also demonstrated excellent repeatability and exceptional stability, retaining over 85% of its initial response after 30 days. Importantly, this platform enabled label-free monitoring of phorbol 12-myristate 13-acetate (PMA)-triggered H2O2 efflux from MGC-803 gastric cancer cells and differentiated their oxidative-stress phenotype from that of normal gastric epithelial cells. The apparent H2O2-equivalent response normalized to cell number was approximately 1.72 × 10[-13] M per cell. This work highlights the potential of confined nanozymes for high-performance H2O2 sensing and precise pathological profiling at the single-cell level.

8-Oxoguanine (8-oxoG) is one of the most biologically relevant oxidative lesions of nucleic acids and is widely investigated as a biomarker of oxidative stress and oxidative nucleic acid damage. Its reliable monitoring in biological matrices is important; however, conventional chromatographic and mass spectrometric methods require expensive instrumentation, trained personnel, and laborious sample preparation. In this work, we report a hybrid electrochemical strategy based on carbon black-modified printed sensors coupled with a wax-patterned paper disk for the rapid determination and passive preconcentration of free 8-oxoguanine in serum. In the proposed configuration, the printed electrochemical sensor acts as the transducing element, while the paper disk serves as a functional sample-treatment interface that modulates sample delivery, partially mitigates serum-related signal suppression, and enables passive analyte preconcentration ahead of voltammetric detection. The platform was systematically optimized by evaluating the carbon black modification, the role of the paper disk, and the differential pulse voltammetry parameters for 8-oxoguanine oxidation. Analytical performance was assessed in buffer and spiked human serum. Following five-fold preconcentration, the paper-assisted configuration achieved a limit of detection of 0.3 μM and a limit of quantification of 1 μM in serum, while partially counteracting matrix-related signal suppression. The quantitative performance of the platform was preliminarily evaluated through spiked-sample analysis and comparison with LC-MS/MS measurements. Overall, this strategy demonstrates that coupling printed electrochemical transduction with the paper disk can improve sensitivity and compatibility with serum analysis, providing a simple and cost-effective platform for free 8-oxoguanine determination in biological matrices.

Malachite green (MG) is a persistent organic contaminant that poses serious ecological and environmental hazards, underscoring the need for effective, long-term remediation techniques. In this study, a g-C3N4/Fe-Mg LDH/ZIF-67 photocatalyst was successfully synthesized via co-precipitation. Structural, optical and morphological analysis of the materials synthesized was done using XRD, FTIR, UV-DRS, BET, FESEM-EDX, XPS, PL, EIS and Mott-Schottky. A pseudo-first-order model adequately represented the dyes' degradation dynamics. The effects of irradiation duration and catalyst dosage on the photocatalytic degradation of MG were thoroughly examined. Under visible-light irradiation, the g-C3N4/Fe-Mg LDH/ZIF-67 composite achieved a substantial degradation efficiency of 94.74%. Scavenger experiments suggest that photogenerated electrons play the predominant role in MG degradation, while holes and superoxide radicals also contribute to the photocatalytic process. The positive slope values acquired through the Mott Schottky plot suggested the existence of n-type semiconductor in the sample. The EIS analysis revealed that charge transfer occurs at the interface. The results of electrochemical and optical analyses supported the formation of a successful heterojunction and the charge transfer process. Although the individual Fe-Mg LDH and ZIF-67 materials exhibited slightly higher degradation efficiencies, the g-C3N4/Fe-Mg LDH/ZIF-67 composite demonstrated high photocatalytic activity and provides a versatile heterostructure platform for further optimization and wastewater treatment applications. Moreover, the photocatalyst showed fair reusability by maintaining photocatalytic ability during three consecutive reuses although it gradually declined in degradation performance.

Emerging and priority organic contaminants, including per- and polyfluoroalkyl substances (PFAS), pharmaceuticals and personal care products, endocrine-disrupting compounds, pesticides, and phenolic pollutants, are often present in natural and engineered water systems at trace or ultratrace levels. Their detection is affected by matrix components, including natural organic matter, inorganic ions, suspended particles, structurally related compounds, and background optical or electrochemical signals. These interferences hinder conventional single-mode sensors from providing stable and reliable responses in real water samples. Molecularly imprinted polymers (MIPs), with tailor-made recognition sites, chemical robustness, and compatibility with enrichment and sensing interfaces, provide a platform for improving selectivity in complex matrices. When integrated with dual-mode readout, MIP-based sensors can improve analytical reliability through signal cross-validation, internal correction, or coupled amplification. This review summarizes recent advances in molecularly imprinted dual-mode sensors for water pollutant detection, emphasizing their adaptation to complex water matrices. After outlining research trends through bibliometric analysis, we discuss MIP-based pretreatment and recognition interfaces, recognition-induced signal transduction, and the synergistic logic of representative dual-mode systems. The applications of these systems to PFAS, phenolic and halogenated phenolic compounds, pesticides and herbicides, and antibiotics are compared in terms of analytical demands, real-sample validation, signal division, and application boundaries. Finally, we discuss challenges related to interfacial antifouling, long-term recognition stability, dual-signal cross-talk, standardized evaluation, green preparation, portable devices, and intelligent data analysis. This perspective highlights reliability-oriented evaluation as a central criterion for developing MIP-based dual-mode sensors in complex water matrices, with particular attention to pollutant-specific recognition chemistry, matrix-induced signal bias, and regulatory-oriented validation.