Abstract Zearalenone (ZEN) contamination in grains poses a serious threat to human and animal health, yet existing imaging-based platforms for rapid visualized detection remain limited by insufficient sensitivity, limited luminescence efficiency, and poor signal stability, hindering further practical applications. Herein, a portable smartphone-assisted electrochemiluminescence (ECL) imaging platform was developed for ultrasensitive and visualized detection of ZEN based on protamine-enhanced 6-aza-2-thiothymine-stabilized gold nanoclusters (Prot/ATT-Au NCs) synthesized by a protein-assisted assembly strategy. The resulting Prot/ATT-Au NCs achieved a 108-fold enhancement in ECL intensity relative to bare ATT-Au NCs and demonstrated an ECL efficiency approximately 5.01-fold higher than that of the benchmark Ru(bpy)32+ system, enabling naked-eye-visible green ECL emission. This enhancement was attributed to Prot-induced hydrogen-bonding and electrostatic confinement that suppressed nonradiative decay and facilitated radiative transitions. Integrated with catalytic hairpin assembly amplification, the developed ECL imaging system allowed ZEN to be visually quantified over a wide linear range of 1 pg/mL to 10 μg/mL with a detection limit of 0.38 pg/mL, along with excellent specificity, stability, reproducibility, and real-sample recoveries. Furthermore, it was applied for the determination of ZEN in naturally contaminated corn samples. This work provides a facile strategy for engineering high-performance ECL emitters and offers a robust route toward portable food safety monitoring.
Simultaneous identification and quantification of multiple amino acid (AA) enantiomers remain as significant challenges. In this study, a multimode, six-channel optical sensor array was developed by three fluorescent/colorimetric carbon dots, which can provide distinctive fluorescence and UV-vis signals to d/l-glutamine, d/l-tryptophan, and d/l-glutamic acid. With the help of machine learning algorithms, this sensor array enabled the accurate qualitative identification and concentration prediction of the three AA enantiomers. The discrimination accuracy for binary and ternary mixtures achieves 95.8%, with an average error of concentration prediction below 10.1%. More importantly, the recognition and prediction performance of this sensor array have been validated using compound amino acid injection and honey samples. A visual assay can also be achieved by the RGB values of the dual-mode sensor array image and SK model, which provide a portable solution for on-site, high-throughput, and naked-eye detection of AA enantiomers.
Abstract The colony-stimulating factor 1 receptor (CSF1R) is a highly promising target for the noninvasive imaging of macrophage-driven inflammatory diseases. To develop a targeted molecular tracer, we first discovered a high-affinity peptide ligand (CP, KD = 0.188 μM) toward CSF1R using mRNA display. We then designed and synthesized a series of DOTA-conjugated peptides (CP0-CP4) featuring systematically extended hydrophilic (2-(2-aminoethoxy)ethoxy)acetic acid (AEEA) linkers. Upon 68Ga-labeling, linker elongation progressively enhanced the tracer’s hydrophilicity and dramatically improved its serum stability (intact fraction at 60 min increased from 12.0% for 68Ga-CP0 to 84.1% for 68Ga-CP4). The optimized candidate, 68Ga-CP4, demonstrated specific binding and internalization in CSF1R-overexpressing cells. In normal mice, increasing linker length effectively reduced nonspecific uptake and promoted rapid renal clearance. In a murine model of LPS-induced pulmonary inflammation, 68Ga-CP4 clearly visualized inflamed lungs, with significantly higher uptake (SUV 2.40 ± 0.39 at 60 min) compared to sham controls (1.81 ± 0.10). Specificity was confirmed by effective blockade with unlabeled peptide (SUV 0.89 ± 0.16) and ex vivo biodistribution. CSF1R protein upregulation in inflamed lungs was further validated by immunohistochemical staining and Western blot. This work presents 68Ga-CP4 as a novel, peptide-based PET tracer for imaging CSF1R-positive inflammation.
Abstract Ion-pair reversed-phase high performance liquid chromatography (IP-RP-HPLC) is widely employed for the characterization of therapeutic oligonucleotides and mRNA vaccines. However, the retention behavior of linear double-stranded DNA (dsDNA), especially very large fragments, is not well-defined under IP-RP-HPLC conditions. This work systematically evaluates separations of dsDNA from 35 bp to 20 kbp using a monodisperse supermacroporous polystyrene-divinylbenzene polymer resin. Various parameters, including column length, flow rate, temperature, and ion-pairing reagent were investigated under fixed and varied gradient profiles, to determine their impact on dsDNA retention. A size-dependent behavior emerged, with a practical threshold near 1.5 kbp. Smaller fragments (≤1.5 kbp) exhibited traditional on–off IP-RP-HPLC behavior. Under fixed gradients, higher flow rates together with shorter columns and shallow gradients yielded the highest resolution and peak capacity values. Stronger ion-pairing reagents and elevated temperatures further increased retention and improved resolution in this range. Beyond this length threshold, behavior became less uniform, and the largest fragments (≥5.0 kbp) showed the opposite trend: longer columns, steep gradients, and lower flow rates improved resolution. In contrast to smaller fragments, weaker ion-pairing reagents provided better separations for large dsDNA, with pair-specific optima that depended on temperature and flow. Additionally, very large fragments exhibited measurable retention and resolution even in the absence of IP-RP-driven retention. This supports the presence of a secondary retention mechanism, which is concurrently present during IP-RP-HPLC separation of large dsDNA. These results demonstrate size-dependent duality of dsDNA and provide practical guidelines for IP-RP-HPLC method optimization for both small and large dsDNA molecules.
Abstract The pursuit of a new rationale in an organic photoelectrochemical transistor (OPECT) has been ongoing, but to date, the research remains understudied. The existing OPECT generally suffers from complex materials fabrication and functionalization, leading to tedious procedures and low application potential. Here, we propose a new paradigm of an OPECT by directly manipulating the color change of the electrolyte, thereby avoiding the complex modification procedures. In a proof of concept, pyrophosphatase (PPase) triggers the release of Cu2+ from the Cu2+–PPi complex, which converts o-phenylenediamine (OPD) into the yellow product 2,3-diaminophenazine (DAP) in the electrolyte, which absorbs and attenuates the incident blue excitation light, inducing a measurable change in the channel current (IDS). This paradigm offers the advantages of simple operation and rapid detection, with a detection limit as low as 5.4 μU. This work is expected to stimulate a universal color-gated OPECT platform.
Abstract Dissolved methane in deep-sea cold seeps can vary from background levels to highly enriched seep water over short spatial scales, which requires in situ measurements with both high sensitivity and a wide dynamic range. Single-transition laser absorption measurements often face a trade-off: strong absorption lines improve low-concentration detection but can saturate at high methane concentrations, whereas weak lines extend the measurable range but reduce the sensitivity. We developed a dual-wavelength off-axis integrated cavity output spectroscopy analyzer for the continuous in situ measurement of dissolved methane in deep-sea environments. The system uses methane transitions near 1651 and 1658 nm to combine trace-level detection with high-concentration quantification. Gas-phase calibration showed excellent linearity for both channels (R2 > 0.999), with the 1651 nm channel calibrated over 2–20 ppm and the 1658 nm channel calibrated over 10–2000 ppm. During continuous measurement of a 10.06 ppm of CH4 standard gas, the concentration precision improved from 0.016 ppm at 20 s to 1.95 ppb at 400 s for the 1651 nm channel and from 0.69 ppm to 52 ppb for the 1658 nm channel. A membrane-based gas extraction module was then calibrated to convert optical measurements to dissolved methane concentrations. Field deployment on a manned submersible in the northern South China Sea resolved near-bottom methane enrichment, rapid vertical attenuation, and localized hotspots exceeding 250 ppm. These results show that the dual-wavelength OA-ICOS strategy can support continuous, quantitative methane measurements across strongly heterogeneous deep-sea seepage environments.
Abstract It is highly demanded to establish effective and sensitive method for detecting pathogenic bacteria because of their high morbidity. The conventional molecular recognition-based methods rely on heterogeneous mode, for which the passive diffusion of bacterial cells and signal tracer results in low transport efficiency and unsatisfied sensitivity. Herein, Fe3O4/polydopamine Janus micromotors were prepared and conjugated with bacteriophage acting as capture agent for bacteria and urease acting as propulsion fuel for Janus micromotors. With a bubble-driven approach, this strategy significantly accelerated transport and improved enrichment of bacterial cells onto the Janus micromotors, achieving a capture efficiency over 98%. By using MoS2/Pt conjugated with polymyxin B as the signal tracer, sensitive colorimetry of Pseudomonas aeruginosa was achieved with a dynamic range of 1.0 × 102 to 1.0 × 107 cfu mL–1. The whole procedure including incubation, washing, color development, and signal detection can be accomplished within 17 min. Usage of bacteriophage as capture agent led to satisfied selectivity for the target bacteria even in complex matrixes. The results for detecting Pseudomonas aeruginosa in artificial cerebrospinal fluid and contaminated milk demonstrated its reliability for real application. This work provides an ideal technical tool for dynamic enrichment and sensitive detection of pathogens based on functional self-propulsed micro/nanomotors.
Accelerating multidimensional NMR acquisition underpins many advances in chemical and biomolecular research by enabling timely chemical insight, high-throughput spectral analysis, and access to transient or chemically evolving systems. Nonuniform sampling (NUS) offers a powerful route to accelerating multidimensional NMR experiments, but poses substantial challenges for accurate spectral reconstruction, especially in weak-peak regions. To overcome these limitations, we present a physics-guided deep learning framework, Consistency-guided Long-range Enhanced Attention for Reconstruction (CLEAR), which integrates convolutional layers with Transformer-based multi-head self-attention in a cascaded refinement architecture, enabling simultaneous modeling of local and global spectral correlations while enforcing strict data consistency with acquired samples. Comprehensive evaluations across multiple biomolecular NMR experiments demonstrate that CLEAR consistently outperforms state-of-the-art reconstruction methods, reducing reconstruction errors (RLNE) by approximately 16-25% while exhibiting overall superior or competitive performance across multiple quantitative metrics, including weak-peak preservation, under severe nonuniform sampling conditions (down to 5%). These results establish CLEAR as a robust and generalizable framework for high-fidelity NUS NMR spectral reconstruction.
Advances in mass spectrometry instrumentation resulted in richer MSn spectra and additional ambiguity in peak assignments. Internal fragment ions (IFs) are often prevalent during top-down mass spectrometry (TDMS) and are particularly difficult to assign─to the extent many practitioners currently forgo their assignment. Accounting for IFs during high-yield dissociation can increase the depth of sequence coverage by an order of magnitude but also results in a geometric increase in the size of the database and ambiguous peak assignments. The ambiguity in TDMS fragment ion assignments comes from the potential for one experimental peak to be assigned to two or more theoretical molecules─most often in the form of isomeric internal fragment ions from distinct sequence regions. The disambiguation of such peaks remains a major challenge in TDMS and is the focus of this work. Ion mobility (IM) mass spectrometry can often separate isomers, including peptides. Starting from a TDMS spectrum containing both unambiguously and ambiguously assigned peaks, we address the hypothesis that ion mobility, or ion mobility followed by fragmentation, can help disambiguate fragment ion assignments by resolving isomeric ambiguity in the mobility domain. We demonstrate that mobility-aligned fragmentation (MAF) can disambiguate TDMS and peptide MS/MS peak assignments of isomeric terminal and internal ions. Consistent with previous studies, we also demonstrate that a single isomeric species (e.g., an individual b-, a-, or internal ion) can be comprised of multiple conformational isomers with different collisional cross-section (CCS) values.
Tandem mass spectral similarity is widely used to infer molecular structural relatedness, yet the fidelity of this relationship has not been systematically quantified using authenticated reference spectra. Here, we introduce Structure-Spectrum Fidelity (SSF), describing how faithfully spectral similarity preserves structural relatedness. Using 12,432 authenticated compounds from METLIN Core, we compared structural similarity across 77.3 million pairs with spectral similarity in 4000 pairs spanning the structural similarity distribution at four collision energies in both polarities. Spectral similarity preserved structural information, but SSF was moderate (Spearman rs = 0.37-0.75) and the underlying distributions were not unimodal, causing inference to fail in both directions. Across the full database at 20 eV in positive mode, 42.5% of pairs sharing an exact molecular formula and near-identical structure (ECFP4 Tanimoto ≥ 0.9; n = 15,157) produced modified cosine scores below 0.7, including 24.1% below 0.2. Conversely, when a modified cosine threshold of 0.7 was applied alone to exact-formula candidates, only 27.1% of accepted pairs were close structural analogs, despite a 27-fold enrichment over background. Increasing the threshold to 0.9 raised this proportion only to 31.5%. Discrimination peaked at intermediate collision energy and declined at higher energy as fragmentation increased similarity among unrelated compounds. Spectral similarity is therefore structurally informative but inherently probabilistic: closely related molecules do not invariably produce similar spectra, and highly similar spectra do not uniquely indicate close structural relatedness. SSF provides a quantitative basis for evaluating similarity-based structural inference.
Ferroptosis has emerged as a significant contributor to ischemia-reperfusion injury after ischemic stroke; however, the redox events linking oxidative stress to ferroptotic injury remain poorly understood. To this end, we developed DX, a fluorescent probe targeting lipid droplets and capable of reversibly tracking the HOCl/GSH redox cycle in living systems. By incorporating a selenomorpholine-based redox switch into a rationally engineered BODIPY scaffold, DX achieves an on-off-on dynamic fluorescence response to oxidative and reductive stimuli. In oxygen-glucose deprivation/reperfusion cell models and mouse models of ischemic stroke, DX facilitated real-time detection of HOCl accumulation during reperfusion. Imaging results combined with transcriptomic and biochemical analyses revealed a close correlation between HOCl signaling and ferroptosis progression. Notably, inhibition of ferroptosis significantly reduced HOCl production in vivo and alleviated IS-induced tissue damage. These findings uncover the HOCl-ferroptosis regulatory axis in ischemic stroke and demonstrate the utility of reversible molecular imaging in studying redox-regulated cell death.
Interactions between plastic nanoparticles (PNPs) or nanoplastics and lipid membranes are governed by a coupled interplay of membrane composition and ionic environment, yet how lipid unsaturation and ion specificity regulate PNP diffusion remains poorly understood. Here, we investigate the effects of acyl-chain saturation and Hofmeister cations on the interactions of carboxylated polystyrene (PS) nanoparticles with phosphatidylcholine membranes composed of dipalmitoylphosphatidylcholine (DPPC), palmitoyloleoylphosphatidylcholine (POPC), and dioleoylphosphatidylcholine (DOPC) lipids. Langmuir isotherms show that lipid packing decreases with increasing unsaturation and that divalent cations, particularly Ca2+, induce pronounced membrane condensation across all lipid types, while monovalent cations produce weaker effects. To relate membrane structure to nanoparticle diffusion, we combine single-particle tracking (SPT) with fluorescence correlation spectroscopy super-resolution optical fluctuation imaging (fcsSOFI) as an analytical method to quantify nanoparticle confinement and diffusion on supported lipid bilayers. Salt addition modulates diffusion and confinement independently, in a manner that depends strongly on lipid identity. On POPC, confinement follows a divalent/monovalent distinction consistent with the Hofmeister series, while diffusion remains unchanged. On DPPC, diffusion is reduced by all salts without systematic confinement changes. On DOPC, both properties are largely insensitive to ion identity. These results show that nanoplastic dynamics at membrane interfaces cannot be predicted from membrane fluidity alone and are governed by the interplay between membrane phase state, mechanical compliance, and ion-specific headgroup interactions.
Top-down proteomics (TDP) enables direct characterization of intact proteoforms, providing protein-level insights into molecular diversity arising from post-translational modifications and sequence variations. Despite this advantage, proteome coverage in TDP remains limited relative to bottom-up proteomics (BUP). To expand coverage, we developed an integrated multidimensional approach combining sequential protein extraction, size-exclusion chromatography (SEC) fractionation, and capillary zone electrophoresis (CZE)-tandem mass spectrometry (MS/MS) and reversed-phase liquid chromatography (RPLC)-MS/MS. This approach identified 743 proteoform families and 10,613 proteoforms from E. coli cells through hundreds of MS runs. By incorporating previous E. coli TDP data sets from our group, we identified 14,932 proteoforms from 985 proteoform families, covering 43% of the E. coli proteome. The data represent the highest proteome coverage of cells by MS-based TDP, creating a draft map of E. coli proteoforms. The results offer strong evidence that MS-based TDP can reach high proteome coverage.
The increasing frequency of harmful algal blooms (HABs) driven by climate change has raised concerns regarding the detection of toxins such as okadaic acid (OA), cylindrospermopsin (CY), microcystins (MC), and anatoxin (ANA) in environmental samples. These toxins pose significant risks to human health, ecosystem, and water safety. This study presents AquaQuanta, a portable multiplex photonic aptasensor based on cholesteric liquid crystal network (CLCN) technology for real-time, simultaneous detection of these four major toxins. The sensor incorporates four distinct aptamer-functionalized CLCN biosensor chips, each tailored to detect a specific toxin. The device operates as a battery-free, label-free, and fully portable platform, enabling easy and rapid toxin detection without the need for complex instrumentation. The sensor demonstrated good selectivity and sensitivity, with clear colorimetric shifts observable to the naked eye, corresponding to toxin concentration changes. The AquaQuanta aptasensor offers a cost-effective, practical solution for environmental monitoring, especially in remote areas where traditional laboratory facilities are not accessible. Its high portability and ease of use make it a promising tool for global water safety and toxin monitoring.
Reliable screening of per- and polyfluoroalkyl substances (PFASs) in complex environmental matrices remains challenging due to severe spectral congestion and matrix-derived interferences under nontargeted conditions. Accurate-mass criteria from high-resolution mass spectrometry (HRMS) alone are often insufficient to suppress putative PFAS candidates arising from natural organic matter (NOM), leading to elevated false-positive rates. Here, we develop a mobility-resolved PFAS screening strategy by integrating direct-infusion gated trapped ion mobility spectrometry with Fourier transform ion cyclotron resonance mass spectrometry (gTIMS FTICR MS). By defining the mobility-resolved chemical space and characteristic mobility behavior of PFAS reference standards, ion mobility is implemented as a physically based decision constraint rather than a supplementary descriptor. Evaluation across chemically diverse NOM systems and environmental samples (e.g., landfill leachate and wastewater) demonstrates that enforcing mobility-m/z consistency reduces the space of putative PFAS candidates by more than 90% relative to mass-only screening. The mobility constraint further enables discrimination of near-isobaric interferences differing by only a few millidaltons (e.g., 2.54 mDa) and supports higher-confidence prioritization of PFAS candidates through agreement with homologous mobility trends. Rather than aiming for definitive structural identification, this work establishes a transferable mobility-constrained screening framework that improves the reliability of nontargeted PFAS detection in complex environmental matrices.
Alkaline phosphatase (ALP) is a pivotal enzyme in diverse physiological processes, and its activity serves as an essential biomarker in clinical diagnostics and biomedical research. The development of reliable and highly sensitive assays for monitoring ALP is therefore of great importance. Herein, we present a luminol-artemisinin (ART) chemiluminescence (CL) platform for the ultrasensitive determination of ALP activity. The assay relies on ALP-catalyzed hydrolysis of ascorbic acid 2-phosphate (AA2P) to produce ascorbic acid (AA), which efficiently quenches luminol-ART CL. Systematic optimization, sensitivity, and selectivity studies revealed a strong correlation between ALP concentration and the quenching of the CL intensity, enabling quantitative analysis with high sensitivity and excellent selectivity. The assay was further validated using human serum samples from healthy donors and patients with clinically documented elevated ALP levels. In healthy diluted serum samples, the standard addition method yielded endogenous ALP activities of 0.104-0.118 U L-1 with recoveries of 97.01-107.89%. Furthermore, the ALP activities in diluted serum samples from three clinical patients were determined to be 0.347, 0.183, and 0.221 U L-1, in close agreement with the corresponding clinical reference values of 0.353, 0.171, and 0.226 U L-1, demonstrating the accuracy and practical applicability of the proposed assay. Compared with existing analytical techniques, the luminol-ART CL assay provides superior sensitivity, operational simplicity, and compatibility with complex biological matrices, offering a promising platform for clinical diagnostics and biochemical analysis.
Organophosphorus pesticides (OPs) are widely used in agriculture but pose severe neurotoxic threats to human health. Conventional OPs detection assays rely on the inhibition of acetylcholinesterase (AChE) activity by suppressing thiocholine (TCh) generation from acetylthiocholine hydrolysis. However, structurally similar OPs often exhibit overlapping inhibition responses, making the AChE inhibition assay insufficient to distinguish them. Herein, based on the bifunctional CuBi2O4 aerogel nanozyme, a machine learning-assisted orthogonal discrimination strategy is proposed by converting the AChE inhibition response of OPs into photoelectrochemical-colorimetric (PEC-CL) fingerprints for identification of four OPs. The PEC-CL dual-signal outputs are realized through catalytic oxidation and photoelectric conversion processes, while the orthogonal sensing relies on the dual regulatory functions of TCh. In the PEC channel, TCh acts as an interfacial electron donor to accelerate charge transfer, thereby regulating the photocurrent generation. In the CL channel, TCh competes with the chromogenic substrate for reactive oxygen species, thereby modulating the chromogenic reaction. This orthogonal regulation enables a single enzymatic inhibition event to simultaneously produce two independent analytical responses, thereby generating characteristic fingerprints for different OPs. Through linear discriminant analysis (LDA), the coupled PEC-CL responses are transformed into discriminative feature maps, enabling accurate classification and concentration prediction of structurally similar OPs over a linear range of 0.01-5 μg/mL with low detection limits of 1.57-3.46 ng/mL. The sensor achieves 100% discrimination accuracy of four OPs and enables reliable identification in real agricultural samples. By integrating orthogonal dual-mode sensing with supervised machine learning, this strategy offers a promising avenue for intelligent and practical pesticide residue screening and risk assessment in complex samples.
Transient absorption spectroscopy (TAS) is a cornerstone for investigating dynamical mechanisms in quantum dots, photovoltaics, and photosynthesis. A primary challenge in the field is the development of high-sensitivity techniques capable of probing species in low-signal regimes, such as single-molecule detection, spatially resolved TAS, and the tracking of short-lived intermediates. While advancements in instrumentation have pushed the physical limits of detection, extracting meaningful dynamics from noise-limited data remains a bottleneck. In this work, we propose a residual U-Net framework integrated with a spectral-temporal decoupling module, namely TS-ResUNet for denoising and reconstruction of transient maps. Quantitative evaluations demonstrate that TS-ResUNet consistently outperforms conventional algorithms and standard U-Net architectures in denoising, while maintaining high fidelity in the extracted spectral and kinetic information. Furthermore, sim-to-real transfer learning performed on experimental data sets indicates that effective adaptation is achievable with a small number of paired training data. This framework provides a robust methodology for refining low signal-to-noise ratio measurements and significantly accelerating data acquisition in ultrafast spectroscopy.
