Abstract Cisplatin-based chemotherapy remains an attractive alternative for controlling advanced cancers. However, a subset of cancer patients exhibits resistance to cisplatin. Accurate and early prediction of individual responses to cisplatin treatment is critical to personalized medicine guidance. The development of an effective method to predict chemotherapy response in cancer patients still faces challenges. Based on extracellular lactate, a key indicator of cisplatin resistance, herein, we report a multivariate-activated DNA logic gate to achieve lactate imaging with high fidelity. This device is assembled by a PNA–peptide–PNA copolymer and a lactate-specific aptamer with a cell membrane-anchoring aptamer module. It operates in response to the matrix metalloproteinase in the tumor microenvironment (TME) after the device anchors to nucleolin; thereby, the liberated aptamer binds to lactate through structure switching, achieving “off–on” fluorescence imaging. Additionally, imaging characteristics in combination with the Logistic Regression algorithm enable noninvasive prediction of cancer responses to cisplatin-based chemotherapy. In 90 cisplatin-resistant tumor-bearing mice, this detection model effectively identifies 77 of the 90 samples, showing a satisfactory accuracy of 85.6%. We further monitor the lactate level decreases in the TME using our method after the drug-resistant tumor-bearing mice treatment by stiripentol and successfully predict the enhancement of the therapeutic effect and the extension of the survival period. This study highlights the combined power of the DNA logic gate and machine learning for the analysis of lactate, paving the way for clinical cisplatin resistance detection and facilitating the formulation of personalized medicine guidance.
Abstract Acute myocardial infarction is a life-threatening cardiovascular event, and point-of-care determination of cardiac troponin I (cTnI) is crucial for early diagnosis and timely intervention. Herein, a self-powered point-of-care sensing platform based on a light-driven bipolar electrode (BPE) for synchronous electrochromic and photoelectrochemical dual-readout detection of cTnI was reported. The BPE integrates a photocathodic sensing pole and an electrochromic reporting pole, enabling simultaneous generation of photocurrent and visual color change through a single photoinduced electron-transfer pathway. Magnetic molecularly imprinted microspheres (MMIPs) were employed as robust capture elements to selectively extract cTnI from biophysical samples. Subsequently, cTnI quantitatively bridges MMIPs with Cu-MOF@TiO2 nanoprobes to form a sandwich complex, which is immobilized onto the sensing pole. Upon light irradiation, the Cu-MOF@TiO2 composite generates photogenerated charge carriers, producing a photocurrent at the sensing pole while synchronously driving the reduction of Prussian blue (PB) to Prussian white (PW) at the reporting pole through the BPE circuit. The electrochromic response can be quantitatively analyzed by smartphone RGB imaging, providing visual detection of cTnI from 10–9 to 10–4 mg/mL, while the photoelectrochemical mode exhibits a wider linear range from 10–11 to 10–4 mg/mL. This work offers a portable, instrument-minimized, and self-powered dual-readout strategy for onsite cardiac biomarker analysis, and provides a versatile framework for future multiplexed biosensing applications.
Abstract The electronic nose system has attracted much attention due to its wide application in industrial detection, atmospheric monitoring and noninvasive disease diagnosis. However, the traditional methods are limited in accuracy and generalization due to the lack of selectivity and reliability of metal oxide semiconductor (MOS) sensors, as well as the synchronization requirements of gas qualitative and quantitative in applications. To address these challenges, this work introduces multitask learning based temporal convolutional bidirectional attention network (MTL-TCBANet), a novel model for gas recognition and concentration prediction. The proposed method harnesses MOS response dynamics by extracting peak-centered fragments with rising and recovery features and employs a temporal convolutional multitask framework with bidirectional cross-attention and dynamic weighting to jointly optimize recognition and regression. Experiments show that MTL-TCBANet attains 0.96 accuracy in gas recognition and an R2 of 0.93 in concentration prediction, surpassing seven traditional baselines and two single-task deep models. Remarkably, it preserves full-sample recognition accuracy with only 50% of the response sequence, demonstrating strong generalization to varying sample lengths. Ablation studies further confirm the essential roles of the multitask temporal convolution, cross-attention, and dynamic weighting modules in performance gains. To validate the generalization capability, three mixed-gas data sets with increasing complexity were introduced. The proposed method achieves consistently superior performance across different data distributions, with accuracy up to 0.97 and R2 up to 0.95. Even under turbulent conditions, the model maintains stable performance, demonstrating strong robustness and cross-data set generalization ability. This study offers an enhanced input processing mechanism for gas identification and concentration prediction, while also presenting a novel approach for multitask model architecture design.
Abstract Hepatic ischemia-reperfusion injury (HIRI) represents a severe complication in liver surgery and transplantation, thus early identification of ischemic damage, specifically the hallmark hypoxia, is pivotal for prognosis. However, the specific tools for detecting ischemic injury remain scarce. Herein, we present the first nitroreductase (NTR)-activatable, self-referenced surface-enhanced Raman scattering (SERS) nanoprobe (Au@MBN@Au@4-NTP, AMAN) for the quantitative monitoring of NTR activity, a key hypoxia biomarker, during hepatic ischemia. The nanoprobe integrates a physically shielded internal standard (4-mercaptobenzonitrile, 4-MBN) and an NTR-responsive reporter (4-nitrothiophenol, 4-NTP). Uniquely, the internal standard exhibits distinct vibrational fingerprints exclusively within the biologically Raman-silent window (1800–2800 cm–1), which substantially reduces spectral interference from complex endogenous biological matrices. Upon NTR activation, the reporter signal of 4-NTP at ∼1325 cm–1 diminishes, while the internal standard signal at 2217 cm–1 remains unchanged, yielding a robust ratiometric readout (I1325/I2217). This enables accurate quantification of NTR with a low detection limit of 0.406 μg/mL. Using this nanoprobe, we achieve accurate and sensitive quantification of hypoxia-induced NTR upregulation in living cells. More importantly, the nanoprobe realizes real-time monitoring of dynamic NTR fluctuations during hepatic ischemia in both 2D and 3D cellular models. This work establishes a novel, interference-free SERS strategy for accurate NTR quantification, providing a powerful tool for the real-time diagnosis and progression assessment of hypoxia-related pathologies.
Rapid and reliable detection of biological agents is crucial in both military and civilian contexts. Here, we present a promising field-deployable approach that combines a simple, self-built pyrolyzer (Py) with a gas chromatograph (GC) and an ultra-fast polarity switching ion mobility spectrometer (IMS). The Py-GC-IMS provides a sensitive method for detecting viruses and bacterial pathogens in liquid and solid samples. During pyrolysis, the sample is rapidly heated under an inert gas atmosphere in the absence of oxygen, leading to a transition of the sample from its nonvolatile solid or liquid state to the gas phase by breaking up the sample into its volatile constituents. Pyrolysis is followed by gas chromatography, separating the volatiles in a first dimension (retention time). For highly sensitive detection of the volatiles eluting from the GC, an ultra-fast polarity-switching IMS is used that also provides a second dimension of separation (drift time). To demonstrate the potential of this hyphenated device, five viruses and five bacteria, including two strains of the same bacterium, and simulants of biological warfare agents, were analyzed. Additionally, an unsupervised machine learning approach was applied to a representative subset of the available classes to showcase the potential of combining the device with state-of-the-art machine learning methods. So far, identification of bacteria at concentrations in the range of 108 colony-forming units per microliter (cfu/μL) and viruses at concentrations in the range of 106 viral particles/μL and lower is possible either by the GC-IMS fingerprints or by the unsupervised machine learning methods used.
Recent advances in mRNA (mRNA) design and manufacturing have allowed novel approaches to fighting disease. When mRNA is administered as a vaccine the host's cell machinery reads the mRNA and translates into proteins that aid in preventing infection and disease. mRNA translation is tightly regulated, with the 5' cap and the poly(A) tail playing important roles. The 5' cap protects mRNA from 5' to 3' exoribonucleases, while the length of the poly(A) tail determines the 3' to 5' exonucleolytic decay. As the use of mRNA treatments expands, analytical techniques are required to confirm identity and stability at every stage of their development. Mass spectrometry (MS) is a robust technique used to confirm the identity of small molecules and traditional biotherapeutics such as monoclonal antibodies, but mRNAs are beyond the size range of conventional MS. While mRNA can be enzymatically cleaved into fragments that can be measured by conventional MS, information on truncations and/or partial sequences can be lost. Charge detection mass spectrometry (CDMS) is an emerging technique that has shown significant utility for mass measurements of large heterogeneous biomolecules. Herein, we describe the development of a CDMS method for intact mRNA analysis. Sample preparation was optimized for mRNAs varying in size, containing both natural and modified bases, giving intact length determination with an uncertainty of 0.5%. Four constructs, ranging from 997 to 4522 nts (nucleotides), were examined both with and without methoxyuridine substitution to assess the impact of nucleotide modification on the measured mass. Furthermore, CDMS successfully resolved mRNAs encoding influenza hemagglutinin (HA) that varied in poly(A) tail length. Finally, HA mRNAs were encapsulated into lipid nanoparticles (LNPs) and reanalyzed following extraction to assess the effects of LNP packaging on the mRNA.
Abstract The detection of viruses is of great significance for the diagnosis of related diseases. However, current virus detection often faces two challenges: first, due to the complex matrix of physiological samples, detection methods often fail to provide accurate quantitative results; second, conventional PCR-related signal amplification procedures are often challenged by issues such as primer interference and non-specific amplification. Herein, we developed an absolute and amplified virus detection strategy by coupling isotope dilution and catalytic hairpin assembly (CHA). The high-risk cervical cancer-related biomarkers were detected with a wide linear detection range (5-30,000 pM) and detection limits in the pM level. To our best knowledge, this is the first amplified metal isotope dilution quantification strategy for biomolecules.
Abstract Surrogate in vitro assays have enabled the scalable testing of neutralizing antibodies to SARS-CoV-2 as a toolbox to manage the post-COVID-19 situation and future outbreaks. However, most surrogate assays use a competitive format to mimic the blocking event between the receptor binding domain (RBD) and the human angiotensin-converting enzyme 2 (ACE2) receptor. This signal “turn-off” format has a narrow dynamic range and is semiquantitative at best. In this proof-of-concept work, we introduce a signal “turn-on” dual antigen assay based on the proximity activation of a pair of RBD-oligonucleotide initiator probes upon bridging by the target antibody. Three well-characterized SARS-CoV-2 variants, i.e., 2019 (wild-type), Delta (B.1.617.2), and Omicron (B.1.1.529), were tested against three commercial antibodies with known RBD-binding and ACE2-blocking profiles. The DNA proximity assay (DPA) results correlated with the ability of the antibodies to block ACE2 instead of their binding to the RBD. Moreover, the single-step, wash-free property of DPA enabled quantification using the kinetic rate of fluorescence signal generation which was more robust than conventional end-point measurements. The DPA achieved good analytical performance with ng/mL detection limit and high assay precision (<5% interassay coefficient of variation), as well as good spike-and-recovery and dilution linearity in serum matrix. The test outcome from three sets of validated vaccine time-series samples were concordant with reference values, demonstrating the potential clinical utility of the DPA platform.
Abstract Conventional analysis of extracellular vesicle (EV)-derived microRNAs relies on vesicle lysis and RNA extraction, which disrupt native vesicular structure and eliminate intravesicular molecular cargoes. Here, we report a rigid DNA tetrahedral nanoprobes-based logic-gated Förster resonance energy transfer (FRET) platform for in situ, amplification-free detection of dual miRNAs directly within intact EVs. Tetrahedral DNA nanostructures (TDNs) targeting miR-21 and miR-141 were engineered with concealed sticky ends that become exposed upon target binding, triggering conditional interparticle assembly and FRET activation exclusively under dual-input conditions. This design physically implements an AND logic operation, converting miRNA coexpression into unified ratiometric output. The rigid tetrahedral scaffold constrains fluorophore spacing and reduces conformational entropy, enabling precise distance-modulated signal transduction with a detection limit of 12.86 pM for dual miRNAs without enzymatic amplification. In clinical plasma samples from 20 prostate cancer (PCa) patients and 20 healthy controls, the ratiometric FRET signal achieved an area under the curve (AUC) of 0.903 and 85.0% diagnostic accuracy, outperforming single-channel measurements. By integrating structural programmability with molecular logic sensor, this strategy transforms EV biomarker analysis from independent signal acquisition into intrinsic nanoscale information processing, establishing a robust platform for noninvasive liquid biopsy.
Abstract Highly sensitive quantification of biomarkers is essential for disease diagnosis, treatment monitoring, and prognosis evaluation. Recently, nanomaterial-based immunosensors have attracted increasing attention due to their high sensitivity. However, the preparation of nanomaterials is often complex, costly, and environmentally unfriendly. Here, we employed Escherichia coli O157:H7 as a nanoreporting probe, leveraging its dyeable genomic DNA to achieve direct signal amplification of target biomarkers. A multifunctional hairpin-shaped aptamer was designed to convert the target into measurable fluorescence signals from E. coli O157:H7 through bispecific binding and allosteric effects of the aptamer. Under optimized conditions, the E. coli O157:H7-based biosensor could detect 0.05 pg/mL prostate-specific antigen (PSA), with a linear relationship ranging from 0.1 to 50 pg/mL (correlation coefficient R2 = 0.988). The method also demonstrated excellent specificity and recovery rates (93–107%), confirming its applicability for real-sample detection. A small-scale clinical study illustrated that the serum PSA levels were significantly elevated in patients with polycystic ovary syndrome compared to healthy controls. Hence, low-abundance PSA analysis may expand the utility of PSA in female-related diseases.
Digital PCR is considered a highly sensitive technique for detecting low-abundance DNA in samples containing excess background DNA. This is particularly relevant for Chlamydia pneumoniae infections, as this obligatory intracellular bacterium can be disseminated by phagocytes from the respiratory tract throughout the whole body. Although persistent bacteria are associated with chronic inflammatory diseases, currently, no diagnostic tools for persistent C. pneumoniae infections exist. In this study, an EvaGreen-based digital PCR assay targeting the chlamydial 16S rRNA gene was initially developed for the QX200 Droplet Digital PCR system; however, poor target recovery, insufficient restriction enzyme digest, and incompatibility with the DNA isolation workflow limited assay performance. Consequently, the assay was redesigned to target the chlamydial groL gene. Analytical performance was determined according to the Clinical and Laboratory Standards Institute (CLSI) EP17-A guideline and the ISO 20395:2019 standard. Notably, the limit of blank varied up to 4-fold between different PCR reagent lots, substantially affecting limit of detection estimates and assay interpretation at low-level. These findings demonstrate the critical importance of assay specification and reagent validation for diagnostics with DNA-binding dye-based assays. Nested PCR exhibited superior analytical sensitivity over the digital PCR assays, yet the limited throughput and increased handling complexity of nested PCR restrict its suitability for routine diagnostic applications. Overall, this study highlights important practical limitations and validation requirements for highly sensitive molecular diagnostic assays.
Abstract Understanding protein–protein interactions (PPIs) and the architecture of protein complexes is essential for elucidating cellular functions and drug action mechanisms. Co-fractionation mass spectrometry (CF-MS) has emerged as an effective tool for analyzing protein complexes under near-native conditions, however, its resolution and structural fidelity are often compromised by complex dissociation during biochemical processing. Here, we present an analytical strategy that integrates in vivo formaldehyde cross-linking with reversed-phase cofractionation mass spectrometry (XL-RP-CF-MS) to identify protein complexes in living cells. Cross-linking stabilizes native interactions, preserving complex integrity during denaturing separation. Reversed-phase liquid chromatography provides high-resolution, reproducible fractionation, enabling robust detection of coelution patterns. Applying this platform to RS4;11 leukemia cells, we identified 6042 proteins and detected 1753 CORUM complexes together with 487 high-confidence EPIC-predicted complexes. The workflow also demonstrated excellent quantitative performance, exhibiting highly linear correlations between chromatographic peak areas and protein loading (R2 = 0.9972) as well as between MS signal intensities and loading amounts (R2 > 0.9). This strategy enables sensitive and physiologically relevant identification of protein complexes, offering a reliable approach for characterizing the composition of potential protein assemblies within specific biological systems.
Abstract With the growing demand for on-site testing of foodborne pathogens, rapid and sensitive detection methods are urgently needed. Here, we developed a split-crRNA-based CRISPR/Cas12a assay integrated with a lateral flow assay (sCR-LFA) for rapid, sensitive detection of bacterial 16S rRNA within 1 h, without pre-amplification. To detect highly structured 16S rRNA, we used RNA fragmentation via DNA-helper-mediated RNase H pretreatment. When short RNA targets interact with corresponding RNA blockers, unblocked RNA spacers hybridize with a DNA activator to form spacer/activator duplexes, which activate split-crRNA/Cas12a complexes. The sCR-LFA achieved a detection limit of 104 CFU/mL for single-site targeting. Using an enhanced sCR-LFA that integrates a multi-site targeting strategy, we achieved a lower detection limit of 102 CFU/mL. Furthermore, the enhanced sCR-LFA was successfully validated in simulated milk and mushroom samples spiked with bacteria. Collectively, this work presents a promising approach for effective food safety monitoring and prevention of infectious diseases.
Abstract Non-Pauling hydrogen bonds involving sulfur, such as amide-NH···S(thioether) interactions, are difficult to probe experimentally despite their potential influence on biologically relevant oxidation processes. In this work, we present a fluorescence-isotope strategy for probing non-Pauling hydrogen bonding during thioether oxidation. A naphthalimide-derived fluorescent reporter, FI-Aminde-SPh, was rationally designed to couple three processes within one molecular framework: thioether oxidation, hydrogen-bond transformation, and fluorescence activation. In the initial state, an electron-rich thioether quenches naphthalimide emission through photoinduced electron transfer, while the adjacent amide group forms a weak non-Pauling NH···S interaction. Oxidation of the thioether to sulfoxide suppresses photoinduced electron transfer and converts the weak NH···S interaction into a stronger NH···O═S hydrogen bond, producing a pronounced fluorescence turn-on response. Time-resolved fluorescence analysis in H2O/D2O revealed a measurable kinetic isotope effect for FI-Amide-SPh, whereas the non-hydrogen-bonding reference FI-AP-SPh showed only a minimal isotope effect, supporting the participation of hydrogen-bond reorganization in the oxidation process. This mechanism was further supported by authentic product comparison, NMR analysis, and DFT calculations. In living cells and zebrafish, FI-Amide-SPh enabled fluorescence reporting of hypochlorite-triggered thioether oxidation, demonstrating that the oxidation-coupled readout can operate in complex biological environments. This work establishes fluorescence-coupled isotope-effect analysis as a practical approach for translating weak non-Pauling hydrogen-bond interactions into quantifiable optical and kinetic outputs, providing a useful platform for studying sulfur-centered redox processes.
Abstract Selective discrimination of glycosaminoglycans (GAGs) is crucial for drug safety and quality control but remains challenging because GAGs share similar disaccharide repeating units and overlapping polyanionic features. Nanozyme-based sensor arrays offer a cross-reactive fingerprinting strategy, yet many require multiple nanozymes with single readouts and correlated responses, increasing operational complexity and limiting pattern separability. Herein, we constructed a dual-channel sensor array by assembling two cationic near-infrared (NIR) porphyrins with a multifunctional Pt–Ni/rGO nanozyme that integrated efficient fluorescence quenching and oxidase-like catalysis. Upon GAG binding, each sensing element generated dual outputs, including NIR fluorescence response driven by competitive association between anionic GAGs and cationic porphyrins, and characteristic UV–vis absorption changes arising from Pt–Ni/rGO-catalyzed TMB oxidation to TMBox followed by TMBox–GAG assembly. Machine learning analysis of the four cross-reactive signals generated by the two sensing elements enabled 100% accurate discrimination of hyaluronic acid, heparin, dextran sulfate, and chondroitin sulfate in PBS over 25–500 μg/mL. Its practical utility was further demonstrated by identifying trace GAG contaminants in Hep down to 1%, classifying unknown samples, and discriminating GAGs in serum. This work expands the analytical utility of multifunctional nanozymes and provides a simple strategy for fingerprint-based GAG analysis with enhanced signal dimensionality and separability.
Abstract Reliable hyperspectral measurement of microplastics in soil is compromised by structured mineral interference. Here, we identify free iron oxides (Fed) as a directional, gradient-dependent masking factor in iron-rich purple soil and develop target-orthogonal, gradient-weighted external parameter orthogonalization (TO-GW-EPO) to remove iron-associated spectral variation while preserving polymer information. A 5 × 5 factorial design covered measured Fed contents of 0.08–7.84 wt % and polyethylene (PE) loadings of 0–5 wt %. Increasing Fed depressed the reflectance continuum, attenuated C–H-related PE features near 1200 and 1395 nm, and weakened the concentration–peak-depth relationship; the 1395 nm masking index reached 79.0%, and r(PE,D1395) decreased from 0.94 to 0.13. Relative to standard EPO, TO-GW-EPO increased target-signal retention from 65.0% to 82.5%. Coupled with competitive adaptive reweighted sampling and a one-dimensional convolutional neural network, the workflow achieved Rp2 = 0.89, RMSEP = 0.63 wt %, RPD = 2.98, and an estimated LOD of 0.95 wt % for PE. These results support rapid screening and semiquantitative-to-quantitative analysis of relatively high-load samples under iron-rich mineral backgrounds. Extending accurate prediction to lower environmental concentrations remains a priority for future optimization. PET-specific reconstruction and retraining achieved Rp2 = 0.78 and RPD = 2.12, supporting workflow-level transferability to PET. This study establishes a physically constrained and interpretable strategy for reliable hyperspectral measurement under structured mineral-matrix interference.
Abstract The fibrinolytic system serves as the core mechanism for maintaining vascular patency and cardiovascular homeostasis, and precise, rapid fibrinolysis analysis is crucial for the clinical prediction of thrombosis or bleeding. In this study, the fibrin-loaded fibrinolytic responsive interference layer was constructed using tubular SiO2–TiO2 hybrid inverse opal (IO) as the scaffold and was combined with ordered porous layer interferometry (OPLI) technology for real-time and rapid fibrinolysis analysis. The tubular IO prepared by the sol–gel co-growth method can load more fibrin and endow them with optical interference effects. The fibrinolysis triggered by the model enzyme nattokinase leads to the migration of the interference fringes of the interference layer, which could be reflected by optical thickness changes (ΔOT) tracked in real-time by the OPLI system. Compared with the silica colloidal crystal film with opal structure, the tubular SiO2–TiO2 hybrid IO exhibited higher sensitivity and shorter detection times in fibrinolysis analysis. Furthermore, the introduction of lysine into the fibrinolytic responsive interference layer can further shorten the detection time of fibrinolytic activity of nattokinase to 10 min. Importantly, both the fibrinolytic interference sensing models with and without lysine adsorption demonstrated high reliability in fibrinolysis analysis of nattokinase-spiked whole blood samples. A parallel comparison with the thromboelastography method further validated the accuracy of this method.
Abstract Electrochemical sensing enables high-temporal-resolution measurements of neurochemical dynamics, yet the impact of sensing waveforms on local neural excitability has not been systematically examined. We combined in vivo two-photon Ca2+ imaging in Thy1-GCaMP6s mice with sensing waveform application through carbon fiber microelectrodes implanted in cortex to determine how sensing waveforms affect neuronal activation. Standard fast-scan cyclic voltammetry (FSCV) waveforms for dopamine and serotonin, as well as square-wave voltammetry and amperometry waveforms, produced Ca2+ activity indistinguishable from no-stimulation controls even over 30 min, confirming established parameters are physiologically silent. We then systematically explored parameters often proposed to increase sensitivity, enhance specificity, or detect new analytes. Increasing the anodic switching potential of the dopamine FSCV waveform produced strong neuronal activation beginning at ∼2.1 V with rapid saturation in activation area beyond ∼2.3 V. Anodic excursions produced stronger activation than cathodic excursions, and slower scan rates and higher scan frequencies increased activation. Unlike conventional microstimulation, the response to sensing waveforms was delayed by 52 ± 26 s and persisted after waveform cessation, consistent with the diffusion of electrochemical reaction byproducts rather than direct membrane depolarization. These results define a parameter space, corresponding to a power dose of <0.5 mW, where FSCV does not perturb neural activity. This establishes a practical threshold to guide waveform development and help avoid unintended neural activation during electrochemical sensing.
Abstract Peptide bond hydrolysis plays a crucial role in protein structure determination. As promising alternatives to natural enzymes, nanoproteases have garnered substantial attention for overcoming their inherent limitations. However, the catalytic performance of most reported nanoproteases remains unsatisfactory, primarily due to their insufficient catalytic activity. In this work, a two-dimensional bimetallic metal–organic framework (2D MOF, CeCuBDC) is developed as a highly efficient nanoprotease for peptide bond hydrolysis. The catalytic mechanism and the hydrolysis pathway are systematically investigated by comprehensive experiments and characterizations. The 2D MOF maximizes active site accessibility and reduces mass-transfer resistance. Meanwhile, bimetallic doping and the electron-withdrawing effect of the organic ligand further enhance the Lewis acidity of metal sites, thereby significantly promoting the catalytic performance toward peptide bond hydrolysis. Moreover, the organic ligand promotes protein conformational changes via hydrophobic interactions, exposing more protein cleavage sites and accelerating the hydrolysis rate. Consequently, the CeCuBDC nanoprotease exhibits a 3–5-fold enhancement in protein hydrolysis efficiency relative to conventional proteases reported in the literature. The as-prepared CeCuBDC exhibits excellent stability and recyclability during protein hydrolysis. Furthermore, it displays high efficiency toward the hydrolysis of various proteins and protein mixtures, while showing a preference for cleaving peptide bonds containing hydrophobic residues. This study enables the rational design of nanoproteases with superior hydrolytic activity and relative selective cleavage ability, offering new strategies for constructing high-performance nanoproteases toward applications in proteomic research.
Accurate alignment of one-dimensional 1H NMR spectra is a prerequisite for reliable metabolomics, but chemical shift variability, line shape asymmetry, and multiplet overlap continue to compromise conventional warping algorithms. In this study, we propose a fully automated, cluster-based alignment framework that enforces the physical constraints of scalar coupling. Within a single optimization loop, zero- and first-order phase parameters are refined, while the baseline is dynamically re-estimated. Peaks are extracted with a matched filter derived from an in-spectrum singlet, and multiplets are recognized by a jump-detection criterion applied to a cluster-distance vector. Optimal peak-to-peak correspondence is then established under coupling-constant, binomial-intensity and coherent chemical-shift-variability rules, and a shift-corrected spectrum is reconstructed by cubic-spline interpolation. Validation on simulated spectra exhibiting severe "crossing chemical-shift variability" demonstrates accurate recovery of multiplet patterns. When applied to 81 1H NMR spectra of Lycium barbarum L. from three geographical origins, PCA demonstrates improved alignment accuracy and enhanced variance interpretation and geographical group discrimination. It is implemented in open-source Python for vendor format import, with demonstrated applicability to metabolomics or food-quality workflows.
