In the postpandemic era, the emergence of "long COVID" from SARS-CoV-2 has brought ongoing negative impacts on individual health and society. The development of more efficient methods for drug screening and monitoring viral activity remains a critical need. The main protease (M), due to its important role in the virus lifecycle, high conservation, and specificity, is considered an ideal biomarker for SARS-CoV-2. Herein, we have developed several chemiluminescence probes based on different substrates modified from covalent inhibitors targeted at M. Among these, the best probe, MPCL-2, exhibits a rapid response (<20 min), an extremely low limit of detection (LoD; 0.11 nM), great selectivity, and chemical stability. After validating the probe's mechanism of action, MPCL-2 can also be used for real-time, in-situ imaging of enzymes in cells infected with the authentic virus and has the potential for real-time, in-situ M imaging . Compared to other methods reported to date, the probe demonstrates superior performance and broader applicability, such as in drug screening or virus activity monitoring. Further, the unique design strategy for the substrate can be adopted to develop sensitive probes for other pathogens.

Outer membrane vesicles (OMVs) secreted by bacteria are emerging diagnostic markers for bacterial infection or disease detection due to their carriage of various signaling molecules. However, actual biological samples of patients are extremely complex, and applying OMVs to clinical diagnosis remains a major challenge. One of the major challenges is that there are still great difficulties in the enrichment of OMVs including tedious steps and lower concentration. And some commonly used exosome enrichment methods, such as ultracentrifugation, still have some shortcomings. Herein, we introduce lipopolysaccharide (LPS) molecularly imprinted polymer (MIP) for efficient capturing and analyzing OMVs, enabling a novel approach to bacterial disease diagnosis based on biorecognition materials. LPS, as a unique structure of Gram-negative bacteria, also widely expressed on the surface of OMVs, which will form cyclic hydrogen bonds with functional monomers of MIP with affinity interactions. The prepared MIP efficiently can isolate OMVs from 100 μL of culture broth via specific affinity LPS in less than 40 min with a recovery rate of over 95%. Moreover, MIP exhibits good reusability, with almost identical enrichment performance after 5 repeated cycles, contributing to reducing experimental costs in both time and economy. The captured OMVs can be detected using Western blotting with target protein antibodies or in combination with proteomic analysis, providing a proteomic biomarker platform for early bacteria disease diagnosis.

Clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (Cas) proteins have been widely applied in molecular diagnostics. Unlike the Ct value quantification method of PCR, the CRISPR system mainly relies on the rise of the rate of the fluorescence signal to indicate the concentration of the target nucleic acid, which is susceptible to system errors caused by various factors, such as reaction conditions and instrument performance. Therefore, establishing internal controls is essential to improve the accuracy, reliability, and commercial feasibility of the CRISPR system. However, the nonspecific -cleavage activity of Cas proteins presents a challenge in establishing internal controls. In this study, we developed unified nucleic acid detection with a single-tube, one-enzyme system (UNISON) for accurate nucleic acid detection with internal controls. By extending the crRNA and modifying it with different fluorophores and quenchers, we achieved that the specific target can only specifically cleave the corresponding folded crRNA and generate a corresponding fluorescence signal. With this design, we established an internal control, achieving accurate and reliable detection of clinical samples of the hepatitis B virus. Integrating internal controls into the CRISPR/Cas system demonstrates significant potential in medical diagnostics and virus monitoring.

Embracing strategies that circumvent the complexities and disordered structures of electrochemiluminescence (ECL) emitters to improve charge transfer efficiency is crucial for advancing ECL technology to the forefront. Here, heteroatom-involved cocrystal engineering was introduced, constructing an ECL system with controllability of the charge transfer process. Through the mutual recognition and coassembly between functional monomers, highly ordered cocrystal superstructures are formed. The layered donor-acceptor arrays in cocrystals accelerated charge transfer, producing a remarkable ECL performance. Furthermore, distinct heteroatoms possess the capability to modulate the charge distribution of monomers by either pushing or pulling electrons. This modulation ultimately affects the charge transfer pathways within cocrystals, enabling ECL emissions of varying intensities and wavelengths. Notably, the presence of UO would significantly inhibit the charge transfer in cocrystals, causing a quenching of ECL signal. This unique characteristic enables precise and selective detection of UO. The heteroatom-involved cocrystals hold immense potential to construct next-generation ECL emitters and create fresh opportunities for the advancement of ECL technology.

Lipid oxidation-induced imbalance in the redox system is one of the key causative factors leading to accelerated aging in living organisms and related diseases. Online sampling and analysis of the heterogeneous ozonolysis kinetics of lipid aerosols are highly important in revealing the oxidation-driven aging process of lipids. In this paper, an online detection method based on atmospheric pressure photoionization combined with ultrahigh resolution mass spectrometry (APPI-HRMS) is developed for real-time analysis of the heterogeneous reactions between lipid particles (oleic acid and squalene) and ozone. The online APPI-HRMS technique serves as an ideal platform for analyzing the heterogeneous oxidation of particles, exhibiting remarkable stability, sensitivity, and responsiveness across a wide range of particle concentrations. Owing to the distinctive characteristics of soft ionization, the heterogeneous effective oxidation rate of lipid aerosols was quantitatively measured. This has facilitated the detection of a series of fingerprint particle-phase products, including aldehydes, secondary ozonides, and hydroperoxides. Additionally, the kinetics evolution of these products with the ozone concentration was captured. Consequently, the ability of this online APPI-HRMS technique in assessing the multiphase oxidation of organic particles has been demonstrated, positioning it as a promising and feasible tool for revealing the heterogeneous reactions of particles.

Hydrogen sulfide (HS) is an important bioactive molecule that plays a significant role in various functions, particularly in the living brain, where it is closely linked to cognition, memory, and several neurological diseases. Consequently, developing effective detection methods for HS is essential for studying brain functions and the underlying mechanisms of these diseases. This study aims to construct a novel photoelectrochemical (PEC) microelectrode Ti/TiO@HSP for the quantitative monitoring of HS levels in the living brain. The PEC microelectrode Ti/TiO@HSP is formed by covalently bonding a specifically designed organic PEC probe HSP, which possesses a D-π- structure, to the surface of TiO nanotubes generated via in situ anodic oxidation of titanium wire. The PEC probe HSP can effectively react with HS and generate significant photocurrent response under long-wavelength excitation light (560 nm), thereby achieving quantitative detection of HS. The sensor demonstrates high sensitivity and good selectivity. In vivo experiments utilizing the PEC microelectrode Ti/TiO@HSP enable the monitoring of dynamic changes in HS levels across various regions of the mouse brain. The findings reveal that in normal mice, the concentration of HS in the hippocampus is significantly higher than in the striatum and cerebral cortex. Additionally, following propargylglycine drug stimulation, HS concentrations in different brain regions were observed to decrease, with the most substantial reduction noted in the hippocampus. This suggests that cystathionine γ-lyase (CSE) is the primary enzyme responsible for HS production in this area, while the striatum exhibits a less pronounced decrease in HS concentration, indicating a reliance on alternative enzymatic pathways for HS production. Therefore, this study not only successfully develops a high-performance HS detection sensor but also provides new experimental tools and theoretical foundations for further exploring the roles of HS in neurophysiological and pathological processes.

Proteins are characterized by their complex levels of structures, which in turn define their function. Understanding and evaluating these structures is therefore crucial to illuminating biological processes. One of the possible analytical methods is vibrational circular dichroism (VCD), which expands the structural sensitivity of classical infrared (IR) absorbance spectroscopy by the chiral sensitivity of circular dichroism. While this technique is powerful, it is plagued by low signal intensities and long measurement times. Here we present an optical setup leveraging the high brilliance of a quantum cascade laser to measure proteins in DO at a path length of 204 μm. It was compared to classical Fourier-transform infrared spectroscopy (FT-IR) in terms of noise levels and in its applicability to secondary structure elucidation of proteins. Protein concentrations as low as 2 mg/mL were accessible by the laser-based system at a measurement time of 1 h. Further increase of the time resolution was possible by adapting the emission to cover only the amide I' band. This allowed for the collection of spectral data at a measurement time of 5 min without a loss of performance. With this high time resolution, we are confident that dynamic processes of protein can now be monitored by VCD, increasing our understanding of these reactions.

Carbohydrate antigen 19-9 is a well-known malignancy biomarker, and its sensitive detection is particularly crucial in the diagnosis and assessment of pancreatic cancer. In this study, an ultrasensitive CA19-9 immunosensor was constructed using the Zn-regulated CdSSe (Zn-CdSSe) nanospheres (NSs) as the electrochemiluminescence (ECL) emitter and FeCoS nano octahedrons (NOs) as a coreactant enhancer. The microstructure of ternary transition metal chalcogenide CdSSe was precisely tuned by Zn doping to avoid aggregation and thus enable stable and efficient cathodic ECL responses. The bimetallic sulfide FeCoS was synthesized using a metal organic framework (MOF) as the template by ion permeation. It was able to catalyze the coreactant efficiently due to the synergistic effect of the Fe and Co. The immunosensor exhibited low detection limit (7.6 × 10 U mL) in the wide linear range of 0.0001-100 U mL, offering a sensitive CA19-9 detection method.

Improving the selective and sensitive binding properties of macrocyclic hosts to target guests is always an interesting challenge. Herein, we introduce a novel "bis-clamp-cavity synergy" strategy to enhance the selectivity and binding sensitivity of pillararenes toward target guests. To achieve this goal, we designed and synthesized ,'-bis-hydroxynaphthoylhydrazone-functionalized conjugated pillar[5]arene (), in which bis-hydroxynaphthoylhydrazone plays the role of clamps, while the pillar[5]arene provides the macrocyclic cavity. The bis-clamps and macrocyclic cavity could supply synergistic binding for target guests through multicoordination interactions, multihydrogen bonds, C-H···π and cation···π interactions, and so on. Furthermore, the introduction of the conjugated pillar[5]arene can enhance the signal transmission ability, thereby improving the sensitivity for guest recognition. Benefiting from the bis-clamp-cavity synergy, exhibits efficient selective recognition for Arg and Al. It achieves colorimetric and fluorescent dual-channel recognition for Arg (with the LOD of 2.99 × 10 M) and ultrasensitive recognition of Al (with the LOD of 7.94 × 10 M). This strategy can be effectively applied to detect Arg and Al in aqueous solution and live cells.

As medical advancements turn most cancers into manageable chronic diseases, new challenges arise in cancer recurrence monitoring. Detecting circulating tumor cells (CTCs) is crucial for monitoring cancer recurrence, but the current methods are cumbersome and costly. This study developed a new CTC detection system combining DNA aptamer recognition, hybridization chain reaction (HCR) technology, and DNA logic devices, enabling the one-step recognition of CTCs by identifying multiple membrane proteins. After catalytically active Au nanoparticles were attached through reduction synthesis in situ onto the DNA hybridization strands of the CTCs surface, a 3,3',5,5'-tetramethylbenzidine (TMB) colorimetric reaction was used to detect CTCs concentration via peroxidase-like catalysis. With this CTCs detection reporting system, we achieved an LOD of 4 cells/mL using an ultraviolet-visible (UV-vis) spectrophotometer. At certain concentrations, CTCs could even be detected visually without the need for an instrument. The development of this CTCs detection reporting system provided a convenient, reliable, and cost-effective detection strategy for widespread CTCs-based cancer recurrence monitoring.

Deciphering the pattern and abundance of -glycosylation of mucin domain proteins, glycoproteins heavily implicated in cancer and other diseases, remains an ongoing challenge. Both the macro- and microheterogeneity of glycosylation complicates the analysis, motivating the development of new strategies for structural characterization of this diverse class of glycoproteins. Here we combine digestion of mucin domain proteins using a targeted protease, Enhancin from (SmE), with ultraviolet photodissociation (UVPD) mass spectrometry to advance glycan mapping and elucidation of -glycosylation trends of densely glycosylated mucin proteins. UVPD facilitates identification of -glycoforms of mucin domain proteins TIM-1, MUC-1 and MUC-16. Additionally, UVPD elucidates several glycoforms of MUC-16 and contributes to the discovery of -glycosylation across tandem repeats of MUC-1.

Planar DNA biosensors employ surface-tethered oligonucleotide probes to capture target molecules for diagnostic applications. To improve the sensitivity and specificity of biosensing, hybridization affinities should be enhanced, and cross-hybridization with off-targets must be minimized. To this end, assays can be designed using the thermodynamic properties of hybridization between probes and on-targets or off-targets based on Gibbs free energies and melting temperatures. However, the nature of heterogeneous hybridization between the probes on the surface and the targets in a solution imposes challenges in predicting precise hybridization affinities and the degree of cross-hybridization due to indeterminable thermodynamic penalties induced by the solid surface and its status. Herein, we suggest practical and convenient guidelines for designing oligonucleotide probes based on data obtained from planar magnetic biosensors and thermodynamic properties calculated by using easily accessible solution-phase prediction. The suggested requirements comprised Gibbs free energy ≥ -7.5 kcal mol and melting temperature ≤10 °C below the hybridization temperature, and we validated for the absence of cross-hybridization. Additionally, the effects of secondary structures such as hairpins and homodimers were investigated for better oligonucleotide probe designs. We believe that these practical guidelines will assist researchers in developing planar magnetic biosensors with high sensitivity and specificity for the detection of new targets.

The microsomal epoxide hydrolase (mEH) is important in the detoxification of carcinogens in the liver and other tissues but is also a blood biomarker of hepatitis and liver cancer. Improved analytical methods are needed for the study of its role in the metabolism of xenobiotics and endogenous roles as a blood biomarker of diseases. The development of a double nanobody sandwich ELISA offers significant improvements over traditional polyclonal or monoclonal antibody-based assays, enhancing both the homogeneity and the stability of assay production. This study focuses on selecting and optimizing nanobody pairs for detecting human mEH. Four high-affinity nanobodies were identified and tested for thermal stability. Combinations of these nanobodies were evaluated, revealing that the MQ4-MQ30 pair achieved the best performance with a limit of detection (LOD) of 1 ng/mL. Additionally, polyHRP was also employed for signal amplification, enhancing detection capabilities despite challenges related to the small size and single epitope recognition of the nanobodies. Comparative studies using microplates and NHS@MF membranes were also performed. The superior performance of the NHS@MF membranes highlighted their potential as a promising alternative for point-of-care testing. The assay exhibited high specificity for human mEH and minimal cross-reactivity with related enzymes and effectively addressed matrix effects in plasma and tissue samples. These findings underscore the potential of double nanobody sandwich ELISAs for reliable and sensitive biomarker detection.

Peptide-engineered nanoparticles have great potential for biomedical research and application. In this work, we have designed and fabricated an electrochemical biosensor based on peptide-guided assembly of silver nanoparticles (AgNPs), in which a peptide is endowed with dual functions to recognize target and guide assembly of AgNPs. As a proof of concept, the performance of this biosensor is validated by quantifying human epidermal growth factor receptor 2 (HER2) protein. In detail, the end of the HER2-specific binding peptide is grafted with a positively charged peptide, which can guide the orderly assembly of AgNPs, while electrochemical signals can be obtained through phosphine-silver coordination. Using this electrochemical biosensor, HER2 protein can be quantified with high sensitivity and specificity, and the limit of detection can be as low as 0.05 pg/mL. Moreover, the antifouling electrode surface prepared by the modification of a layer of antifouling zwitterionic peptide allows this biosensor to be used for the detection of serum HER2 protein from breast cancer patients, which provides the clear evidence for the distinction of HER2+ breast cancer patients and HER2- breast cancer patients.

Liquid chromatography-mass spectrometry (LC-MS)-based metabolomics identification relies heavily on high-quality MS/MS data; MS/MS prediction is a good way to address this issue. However, the accuracy of the prediction, resolution, and correlation with chemical structures have not been well-solved. In this study, we have developed a MS/MS prediction method, PPGB-MS2, which transforms the MS/MS prediction into fragment intensity prediction, and the concept of precursor-product ion pair graph bags (PPGBs) was introduced to represent fragments, achieving uniform representation of precursor and product ion structures and MS/MS fragmentation information. The chemical structure information is kept before it is incorporated into machine learning models. Due to the PPGB representation, graph neural networks (GNNs) can be utilized to achieve MS/MS fragment intensity prediction. The system was trained and evaluated using [M+H]+ and [M-H]- data acquired by an Agilent QTOF 6530 in the NIST 20 tandem MS database. Results demonstrated that the average cosine similarity is 0.71 in the test set, which is higher than classical MS/MS prediction methods. PPGB-MS2 also achieves high-resolution MS/MS prediction due to its effective management of the correspondence between fragments and structures.

Liquefied petroleum gas (LPG) is a modern fuel for kitchens, vehicles, and industry. Leakage of LPG is extremely fatal for humans and the atmosphere; therefore, quick detection is a vital need. The sol-gel self-combustion process was applied to synthesize the calcium-doped praseodymium orthoferrite (PrFeO) nanomaterials. Synthesized nanoparticles were analyzed by powder X-ray diffraction (PXRD) for phase and crystallite size, energy dispersive X-ray (EDX) for elemental composition and field emission scanning electron microscopy (FESEM) for surface morphology, high-resolution transmission electron microscopy (HR-TEM) for structural and morphology, ultraviolet-visible (UV-vis) spectroscopy for absorption behavior and energy band gap, Brunauer-Emmett-Teller (BET) for surface analysis, and Fourier transform infrared spectroscopy (FTIR) for the vibrational study. The PXRD illustrates that the crystallite size reduces from 27.72 to 20.49 with the rising content of calcium. The FESEM and EDX interpret the morphology and elemental composition/mapping. The UV-vis spectroscopy reveals that the band gap is decreasing from 2.25 to 1.87 eV with the increasing concentration of calcium. The optimized nanomaterials were explored for LPG sensing. Recovery time, response time, sensor response, etc., were determined and discussed. This study divulges that the composition PrCaFeO has optimum sensor response, selectivity, and least response and recovery times of 7.5 and 7.1 s, respectively. The designed sensor shows good selectivity for LPG at ambient temperature. The current study points out that the developed sensor outperforms in terms of response and recovery times when compared with other LPG sensors based on perovskite materials. The gas sensing mechanism has been explained.

Infrared (IR) spectroscopy is an efficient method for identifying unknown chemical compounds. To accelerate IR spectrum analysis, various calculation and machine learning methods for simulating IR spectra of molecules have been studied in chemical science. However, existing calculation and machine learning methods assumed a rigid constraint that all molecules are in the gas phase, i.e., they overlooked the phase dependency of the IR spectra. In this paper, we propose an efficient phase-aware machine learning method to generate phase-conditioned IR spectra from 2D molecular structures. To this end, we devised a phase-aware graph neural network and combined it with a transformer decoder. To the best of our knowledge, the proposed method is the first IR spectrum generator that can generate the phase-conditioned IR spectra of real-world complex molecules. The proposed method outperformed state-of-the-art methods in the tasks of generating IR spectra on a benchmark dataset containing experimentally measured 11,546 IR spectra of 10,288 unique molecules. All implementations of the proposed method are publicly available at https://github.com/ngs00/PASGeN.

Surface-enhanced Raman scattering (SERS) has emerged as a powerful tool for contamination detection. Fabricating efficient nanostructures with hotspots for signal enhancement and concentrating diluted target analyte molecules to the hotspots are critical for ultrasensitive SERS detection, which generally requires advanced instruments and intricate manipulations. Herein, we report a simple, low-cost, and high-efficiency paper device that can simultaneously concentrate the analytes and generate SERS hotspots rapidly with the assistance of laser-induced thermophoresis. After dropping the target- and plasmonic nanoparticle-containing solution on a paper substrate, the evaporative gradient created by the laser-induced thermophoresis can promote the delivery of the analytes and plasmonic nanoparticles simultaneously to the tiny area of the laser spot, forming compact SERS hotspots to significantly amplify the analyte's Raman scattering signals. This convenient thermophoretic strategy can be accomplished rapidly within ∼4 min and exhibits more than 10-times higher sensitivity than that without the assistance of laser-based thermophoresis. This elegant paper device is successfully applied to the detection of contaminants such as pesticides and nanoplastics in fruit and water samples, holding the potential to provide a simple, fast, and cost-effective approach for on-site detection of environmental contaminants.

Solid preconcentrated ore samples used in pyrometallurgical copper smelters are analyzed by flame emission spectroscopy using a specialized flame optical emission spectroscopy (OES), system. Over 8500 complex spectra are categorized using an artificial neural network (ANN) that was optimized to have 10 hidden layers with 40 nodes per layer. The ANN was able to quantify the elemental content of all samples to within better than 1.5 mass% and was able to identify the prevalent minerals to within better than 2.5 mass%. The flame temperature was obtained with an uncertainty of σ < 3 K and the particle size to within 2 μm. The results are found to be superior to those obtained to a nonlinear partial least-squares fit model, which is equivalent to an ANN having no hidden layers.

Penicillin G (PG) is a common antibiotic, and its accumulation in the environment can pose a threat to the ecological system and ultimately impact human health. Nanozymes have emerged as highly stable enzyme mimics that can be utilized as sensors to achieve the sensitive detection of specific antibiotics. Herein, we report on a dual single-atom Fe/Zn nanozyme (DSAzyme) synthesized from Fe-imidazole as the guest and zeolite imidazole framework-8 as the host. The DSAzyme exhibits intriguing properties that mimic the activities of two natural enzymes: peroxidase and lactamase. Both activities are utilized for the design of a colorimetric sensor for the specific detection of PG: the peroxidase activity enables color generation from 3,3',5,5'-tetramethylbenzidine and HO, and the lactamase activity provides the recognition of PG. The nanozyme consists of many Fe-N and Zn-N site and mechanistic characterizations by experimental investigations and theoretical calculations identify Fe-N as the main active center for the peroxidase activity and Zn-N as the main binding site for PG. The sensor can achieve a limit of detection of 47 nM, is able to detect PG from real-life samples, remains fully functional after 8-month storage, and retain high activities after reuse for fives times. Taken together, our study provides a new approach to the detection of antibiotics in environmental samples.

The development of small-molecule fluorescent probes has revolutionized the monitoring of physicochemical parameters, offering unprecedented insights into biological processes. In this perspective, we critically examine recent advances and trends in the design and application of fluorescent probes for real-time monitoring of subcellular environments. Traditional concepts such as membrane potential, microviscosity, and micropolarity have been superseded by more biologically relevant parameters like membrane voltage, tension, and hydration, enhancing the accuracy of physiological assessments. This redefinition not only presents an evolved concept with broader applications in monitoring subcellular dynamics but also addresses the unmet needs of subcellular biology more effectively. We also highlight the limitations of commonly used probes in providing specific information about the redox environment, noting their nonspecificity to oxidants and the influence of various chemical interactions. These probes typically rely on free radical mechanisms and require metal catalysts to react with hydrogen peroxide. They include naphthalimide, fluorescein, BODIPY, rhodamine, cyanine cores to cover the UV-vis-near-infrared window. The motif of this perspective is to provide critical insights into trending fluorescent-based systems employed in real-time or physicochemical-responsive monitoring, thus aiming to inform and inspire further research in creating robust and efficient fluorescent probes for comprehensive monitoring applications.