Accurate Quantitative Analysis Research Articles

Hybrid modality dual-energy imaging aggregating complementary advantages of kV-CT and MV-CBCT: concept proposal and clinical validation

Megavoltage cone-beam CT (MV-CBCT) is advantageous in metal artifact reduction during Image-Guided Radiotherapy (IGRT), although it is limited by poor soft tissue contrast. This study proposed and evaluated a novel hybrid modality dual-energy (DE) imaging method combining the complementary advantages of kV-CT and MV-CBCT.
Approach: The kV-CT and MV-CBCT images were acquired on a planning CT scanner and a Halcyon linear accelerator respectively. After rigid registration, images of basis materials were generated using the iterative decomposition method in the volumetric images. The decomposition accuracy was quantitatively evaluated on a Gammex 1472 phantom. The performance of contrast enhancement and metal artifact reduction in virtual monochromatic images were evaluated on both phantom and patient studies.
Main results: Using the proposed method, the mean percentage errors for RED and SPR were 0.90% and 0.81%, outperforming the clinical single-energy mapping method with mean errors of 1.28% and 1.07%, respectively. The contrasts of soft-tissue insets were enhanced by a factor of 2~3 at 40 keV compared to kV-CT. The standard deviation in the metal artifact area was reduced by ~67%, from 42 HU (kV-CT) to 14 HU (150 keV monochromatic). The head and neck patient test showed that the percent error of soft-tissue RED in the metal artifact area was reduced from 18.1% (HU-RED conversion) to less than 1.0% (the proposed method), which was equivalent to the maximum dosimetric difference of 28.7% based on the patient-specific plan.
Significance: Without hardware modification or extra imaging dose, the proposed hybrid modality method enabled kV-MV DE imaging, providing improved accuracy of quantitative analysis, soft-tissue contrast and metal artifact suppression for more accurate IGRT.&#xD.

A Review of Analytical Methods for Microplastics in Soils

Microplastics (MPs), as an emerging environmental pollutant, pose a potential threat to ecosystems and human health, and the study of their analytical methods has become particularly important. In this paper, the current research progress of analytical methods for MPs in soil is reviewed. The sources, ecological impacts and possible health risks of MPs are introduced, and the urgency of accurate detection and quantitative analysis of MPs is emphasized. Subsequently, MPs’ analytical methods based on different principles, including visual analysis, chemical analysis, spectroscopic techniques, microscopic observation, and mass spectrometry, are systematically outlined in response to the wide range of sample sources and wide particle size distribution of MPs. For each method, the advantages, limitations and scope of application are highlighted and evaluated, and the directions and development trends for future improvement are proposed. The review of this paper is expected to promote the continuous improvement and innovation of MPs analytical methods and provide more effective technical support and scientific basis for solving the MPs’ pollution problems.

Novel Colorimetric and Near-Infrared Ratiometric Fluorescent Probe for Sensing Cysteine in Food Samples, Plants, and Living Cells.

Cysteine (Cys) is a crucial biothiol that acts a significant function in food samples and biological systems, including plant roots and living cells. Hence, we developed a novel colorimetric and near-infrared ratiometric fluorescent probe (CT), composed of coumarin and tetrahydroacridine-conjugated indole salt, for the detection of Cys. Upon reaction with Cys, the probe undergoes a specific N-substitution reaction, resulting in a notable colorimetric change and a significant ratiometric fluorescent response in both visible and near-infrared emission channels. These dual-channel ratiometric fluorescence changes are completely independent, enabling the probe to obtain great selectivity, sensitivity, and exceptional detection accuracy. Leveraging these attributes, the probe was employed to provide accurate quantitative analysis of Cys in food samples. Furthermore, confocal imaging demonstrated that the probe could monitor both exogenous and endogenous Cys levels in living cells and track Cys changes in plant roots under heavy metal stress. This work presents a dependable and accurate imaging solution for tracking and identifying Cys of real food, plants, and living cells.

Quantitative 13C-IS pyrolysis-GC-MS lignin analysis: Overcoming matrix effects in animal feed and faeces

Recently, a pyrolysis-GC-MS methodology for specific lignin quantification and structural characterisation was developed, relying on the use of uniformly 13C-labeled polymeric lignin isolate as internal standard (IS). The 13C-IS py-GC-MS method has been validated for grasses, woods, and applied in various showcases. To study the fate of lignin in animals, this method still requires careful validation in animal feeds and, especially complex faecal samples, hence the aim of this work. Hereto, faecal material was collected from pigs fed with wheat straw as lignin source and subjected to the py-GC-MS analytical platform for thorough examination of IS pyrolysis behaviour in terms of response and structural features. Next, 13C-ISpy-lignin contents and corrected Klason lignin contents were compared. Most importantly, we revealed that pyrolysis behaviour of 13C-IS lignin and 12C-sample lignin was differently affected in the faecal matrix, resulting in the ultimate underestimation of 13C-ISpy-lignin contents. In-depth examination and evaluation of matrix constituents showed that predominantly matrix ash was responsible for the effects observed. We further demonstrated that said matrix effects can be overcome by water extraction of the samples prior to analysis. Our validation and approach extend the use of the specific 13C-IS py-GC-MS methodology for accurate quantitative lignin analysis to biomass samples with complex matrices like pig faeces, and now call for application in future digestibility studies.

PTC-NH2@MOFs-AuNCs: A novel bifunctional signal probe for ratiometric electrochemiluminescence/colorimetry dual-mode aptasensing of lead ion

The development of a dual-mode sensing method for lead ions (Pb2+) is crucial and desirable. Herein, a single component-based bifunctional signal probe (perylene derivative-modified MOFs-gold nanoclusters, PTC-NH2@MOFs-AuNCs), which was capable of simultaneously generating an electrochemiluminescence (ECL) signal and exhibiting nanoenzyme activity, was prepared to fabricate a novel ratiometric ECL/colorimetry dual-mode platform for the visual, reliable, and sensitive analysis of Pb2+. Specifically, the PTC-NH2@MOFs-AuNCs were firstly synthesized via the electrostatic adsorption of AuNCs on the surface of PTC-NH2@MOFs. Upon the addition of Pb2+, the ECL signal of PTC-NH2@MOFs-AuNCs, which had been quenched by the adsorbed aptamer, could be restored at both the anode and cathode. Simultaneously, the peroxidase-like activity of PTC-NH2@MOFs-AuNCs was also recovered, accompanied by a distinct color transition from light blue to dark blue. The linear ranges of ratiometric ECL/colorimetric dual-modal assay for Pb2+ were 1 pM ∼ 5 nM and 1 nM ∼ 2 μM, with the limits of detection of 0.1 pM and 0.31 nM, respectively. More importantly, by introducing the screen-printed electrode and paper chip as sensing platform, a portable sensor for the convenient detection of Pb2+ was further established, demonstrating its promising practicability of rapid visual screening and accurate quantitative analysis in farmland soils.

Significant enhancment of the accuracy of impurity determination in vacuums using classification one-point laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy (LIBS) is a highly promising technique for the in-situ, real-time diagnosis of impurity deposits on the inner walls of tokamak devices. The deposited impurity on plasma-facing materials (PFCs) pose a significant risk to the steady-state operation of the tokamak. Under vacuum conditions, an accurate quantitative analysis of the thin co-deposition layers is a technical challenge. In this study, 30 co-deposited layer samples of tungsten (10.0–92.3 a.t.%), molybdenum (2.0–77.8 a.t.%), iron (2.9–12.1 a.t.%) and copper (1.2–18.7 a.t.%) were prepared to simulate the co-deposition layers found on PFCs in Experimental Advanced Superconducting Tokamak (EAST). A variation of the CF-LIBS algorithm, the so-called One Point Calibrated LIBS (OPC-LIBS), was employed to analyze these co-deposited layer samples under conditions of 5 × 10−5 mbar. It was found that the matrix matching degree among the measured samples and the selection of standard samples play a decisive role in the quantitative analysis capability of OPC-LIBS. In actual situations, the composition of the co-deposited impurity layers at different locations in the Tokamak will be quite different. We addressed this challenge by developing the Classified OPC-LIBS (COPC-LIBS) model, an enhanced version of OPC-LIBS with pre-classification to offset matrix effects in LIBS analysis. For tungsten in the co-deposition layers, the root mean square (RMSE) calculated by the CF-LIBS method was 14.7, the OPC-LIBS method was 11.5, and the newly invented COPC-LIBS was reduced to only 5.1. The COPC-LIBS method is a highly efficient technique that can precisely measure the distribution of co-deposited layers on the surface of inner wall materials. The diagnostic data obtained from this method will provide valuable insights into the interaction between plasma and wall materials during the operation of fusion devices.

Ratiometric fluorescence sensor based on dye-encapsulated Zn-MOF for highly sensitive detection of diquat in tap water and apple samples

The development of convenient and intelligent visualization of rapid pesticide detection methods is key to ensuring food quality and safety. This paper presents a smartphone-based ratiometric fluorescence sensor for smart field visualization and detection of diquat (DQ). Three-dimensional Zn-MOFs are prepared by the solvothermal method using the rigid ligand 4,4′,4″-s-triazine-2,4,6-triyltribenzoic acid (H3TATB), the flexible ligand 1,4-bis((1H-imidazol-1-yl)methyl)benzene (bimb), and the transition metal (Zn2+), which shows good structural and thermal stability. Zn-MOF is a three-dimensional framework crystallizing in a triclinic crystal system in the Pī space group. Then, RhB@Zn-MOF is obtained by introducing rhodamine B (RhB) into the Zn-MOF framework using the same method. Internal Filter Effect (IFE) quenches red fluorescence and Fluorescence Resonance Energy Transfer (FRET) enhances blue fluorescence, enabling high sensitivity and visual detection of DQ. The sensor has a low detection limit of 10.50 nM and is linear over the 0–70.00 μM concentration range. Compared to Zn-MOF (detection limit of 16.90 nM), the introduction of RhB significantly improved the precision and sensitivity of DQ detection. The combination of a smartphone and RGB analysis enables fast and accurate quantitative analysis of DQ in tap water and apple samples. The sensor platform has the advantages of convenience, intelligent real-time detection, etc. It has a wide prospect of practical application and consolidates a solid foundation for food safety control.

Dynamic Fault Tree Generation and Quantitative Analysis of System Reliability for Embedded Systems Based on SysML Models.

As embedded systems become increasingly complex, traditional reliability analysis methods based on text alone are no longer adequate for meeting the requirements of rapid and accurate quantitative analysis of system reliability. This article proposes a method for automatically generating and quantitatively analyzing dynamic fault trees based on an improved system model with consideration for temporal characteristics and redundancy. Firstly, an "anti-semantic" approach is employed to automatically explore the generation of fault modes and effects analysis (FMEA) from SysML models. The evaluation results are used to promptly modify the system design to meet requirements. Secondly, the Profile extension mechanism is used to expand the SysML block definition diagram, enabling it to describe fault semantics. This is combined with SysML activity diagrams to generate dynamic fault trees using traversal algorithms. Subsequently, parametric diagrams are employed to represent the operational rules of logic gates in the fault tree. The quantitative analysis of dynamic fault trees based on probabilistic models is conducted within the internal block diagram of SysML. Finally, through the design and simulation of the power battery management system, the failure probability of the top event was obtained to be 0.11981. This verifies that the design of the battery management system meets safety requirements and demonstrates the feasibility of the method.

Assessing the physiological properties of baker's yeast based on single-cell Raman spectrum technology

With rapid progress in the yeast fermentation industry, a comprehensive commercial yeast quality assessment approach integrating efficiency, accuracy, sensitivity, and cost-effectiveness is required. In this study, a new yeast quality assessment method based on single-cell Raman technology was developed and contrasted with traditional methods. The findings demonstrated significant associations (Pearson correlation coefficient of 0.933 on average) between the two methods in measuring physiological indicators, including cell viability and intracellular trehalose content, demonstrating the credibility of the Raman method compared to the traditional method. Furthermore, the sensitivity of the Raman method in viable but non-culturable cells was higher in measuring yeast cell viability (17.9 % more sensitive). According to the accurate quantitative analysis of metabolic activity level (MAL) of yeast cells, the cell vitality was accurately quantified at population and single-cell levels, offering a more comprehensive assessment of yeast fermentation performance. Overall, the single-cell Raman method integrates credibility, feasibility, accuracy, and sensitivity in yeast quality assessment, offering a new technological framework for quality assessments of live-cell yeast products.

Research on Problems and Solutions of Quantitative Investment in the Chinese Market

Quantitative investment has gradually become increasingly important in the Chinese finance market in recent decades. It improves the efficiency of investment, reduces the risk of investment, enhances the liquidity of some market assets and provides new opportunities to accumulate wealth for investors. As a new investment method, it raises some problems which lead to the turmoil of the market when it keeps developing and gaining traction. This paper will review the current situation of quantitative investment in the Chinese market from its development history and the sizes of funds. This study shows that there are mainly four problems existing in the application of quantitative investment in China. Listed below are problems. The evident government intervention in the market has a negative impact on the fitting effectiveness of quantitative models. Some kinds of assets and commodities face poor liquidity and for them, quantitative investment strategies can hardly be conducted accurately. The accuracy of quantitative analysis can be unreliable for the financial data is sometimes incomplete and unreliable in the Chinese market. The high-frequency trading affects the stability of the whole market because of lack of market supervision. This paper also puts forward relevant countermeasures and suggestions according to these problems. This study has great significance to the healthy development of quantitative investment in the Chinese market.

The development of machine learning approaches in two-dimensional NMR data interpretation for metabolomics applications

Metabolomics has been widely applied in human diseases and environmental science to study the systematic changes of metabolites over diverse types of stimuli. NMR-based metabolomics has been widely used, but the peak overlap problems in the one-dimensional (1D) NMR spectrum could limit the accuracy of quantitative analysis for metabolomics applications. Two-dimensional (2D) NMR has been applied to solve the 1D NMR overlap problem, but the data processing is still challenging. In this study, we built an automatic approach to process the 2D NMR data for quantitative applications using machine learning approaches. Partial least square discriminant analysis (PLS-DA), artificial neural network classification (ANN-DA), gradient boosted trees classification (XGBoost-DA), and artificial deep learning neural network classification (ANNDL-DA) were applied in combination with an automatic peak selection approach. Standard mixtures, sea anemone extracts, and mouse fecal samples were tested to demonstrate the approach. Our results showed that ANN-DA and ANNDL-DA have high accuracy in selecting 2D NMR peaks (around 90 %), which have a high potential application in 2D NMR-based metabolomics quantitively study, while PLS-DA and XGBoost-DA showed limitations in either data variation or overfitting. Our study built an automatic approach to applying 2D NMR data to routine quantitative analysis in metabolomics.

Automatic reorientation to generate short-axis myocardial PET images

BackgroundAccurately redirecting reconstructed Positron emission tomography (PET) images into short-axis (SA) images shows great significance for subsequent clinical diagnosis. We developed a system for automatic redirection and quantitative analysis of myocardial PET images.MethodsA total of 128 patients were enrolled for 18 F-FDG PET/CT myocardial metabolic images (MMIs), including 3 image classifications: without defects, with defects, and excess uptake. The automatic reorientation system includes five modules: regional division, myocardial segmentation, ellipsoid fitting, image rotation and quantitative analysis. First, the left ventricular geometry-based canny edge detection (LVG-CED) was developed and compared with the other 5 common region segmentation algorithms, the optimized partitioning was determined based on partition success rate. Then, 9 myocardial segmentation methods and 4 ellipsoid fitting methods were combined to derive 36 cross combinations for diagnostic performance in terms of Pearson correlation coefficient (PCC), Kendall correlation coefficient (KCC), Spearman correlation coefficient (SCC), and determination coefficient. Finally, the deflection angles were computed by ellipsoid fitting and the SA images were derived by affine transformation. Furthermore, the polar maps were used for quantitative analysis of SA images, and the redirection effects of 3 different image classifications were analyzed using correlation coefficients.ResultsOn the dataset, LVG-CED outperformed other methods in the regional division module with a 100% success rate. In 36 cross combinations, PSO-FCM and LLS-SVD performed the best in terms of correlation coefficient. The linear results indicate that our algorithm (LVG-CED, PSO-FCM, and LLS-SVD) has good consistency with the reference manual method. In quantitative analysis, the similarities between our method and the reference manual method were higher than 96% at 17 segments. Moreover, our method demonstrated excellent performance in all 3 image classifications.ConclusionOur algorithm system could realize accurate automatic reorientation and quantitative analysis of PET MMIs, which is also effective for images suffering from interference.

Research on batch multielement rapid quantitative analysis based on the standard curve-assisted calibration-free laser-induced breakdown spectroscopy method

This study proposes a batch rapid quantitative analysis method for multiple elements by combining the advantages of standard curve (SC) and calibration-free laser-induced breakdown spectroscopy (CF-LIBS) technology to achieve synchronous, rapid, and accurate measurement of elements in a large number of samples, namely, SC-assisted CF-LIBS. Al alloy standard samples, divided into calibration and test samples, were applied to validate the proposed method. SC was built based on the characteristic line of Pb and Cr in the calibration sample, and the contents of Pb and Cr in the test sample were calculated with relative errors of 6% and 4%, respectively. SC built using Cr with multiple characteristic lines yielded better calculation results. The relative contents of ten elements in the test sample were calculated using CF-LIBS. Subsequently, the SC-assisted CF-LIBS was executed, with the majority of the calculation relative errors falling within the range of 2%–5%. Finally, the Al and Na contents of the Al alloy were predicted. The results demonstrate that it effectively enables the rapid and accurate quantitative analysis of multiple elements after a single-element SC analysis of the tested samples. Furthermore, this quantitative analysis method was successfully applied to soil and Astragalus samples, realizing an accurate calculation of the contents of multiple elements. Thus, it is important to advance the LIBS quantitative analysis and its related applications.

Prior image-based generative adversarial learning for multi-material decomposition in photon counting computed tomography

BackgroundPhoton counting detector computed tomography (PCD-CT) is a novel promising technique providing higher spatial resolution, lower radiation dose and greater energy spectrum differentiation, which create more possibilities to improve image quality. Multi-material decomposition is an attractive application for PCD-CT to identify complicated materials and provide accurate quantitative analysis. However, limited by the finite photon counting rate in each energy window of photon counting detector, the noise problem hinders the decomposition of high-quality basis material images. MethodsTo address this issue, an end-to-end multi-material decomposition network based on prior images is proposed in this paper. First, the reconstructed images corresponding to the full spectrum with less noise are introduced as prior information to improve the overall signal-to-noise ratio of the data. Then, a generative adversarial network is designed to mine the relationship between reconstructed images and basis material images based on the information interaction of material decomposition. Furthermore, a weighted edge loss is introduced to adapt to the structural differences of different basis material images. ResultsTo verify the performance of the proposed method, simulation and real studies are carried out. In simulation study of structured fibro-glandular tissue model, the results show that the proposed method decreased the root mean square error by 67 % and 26 % on adipose, 66 % and 28 % on fibroglandular, 52 % and 8 % on calcification, compared to butterfly network and dual interactive Wasserstein generative adversarial network. ConclusionExperimentally, the proposed method shows certain advantages over other methods on noise suppression effect, detail retention ability and decomposition accuracy.

Data efficient contrastive learning in histopathology using active sampling

Deep learning (DL) based diagnostics systems can provide accurate and robust quantitative analysis in digital pathology. These algorithms require large amounts of annotated training data which is impractical in pathology due to the high resolution of histopathological images. Hence, self-supervised methods have been proposed to learn features using ad-hoc pretext tasks. The self-supervised training process uses a large unlabeled dataset which makes the learning process time consuming. In this work, we propose a new method for actively sampling informative members from the training set using a small proxy network, decreasing sample requirement by 93% and training time by 62% while maintaining the same performance of the traditional self-supervised learning method. The code is available on github.

Determination and study of correlations of relative sensitivity factors in metal and metal oxide samples by glow discharge mass spectrometry

The determination of relative sensitivity factor (RSF) plays a crucial role in achieving accurate quantitative analysis in glow discharge mass spectrometry (GD-MS). In this study, the RSF of typical elements in 14 metal and metal oxide matrices were determined, and their correlation with discharge parameters, matrix effects, the first ionization energies and sample shapes were investigated. It was found that discharge gas flow exerted a minor influence on RSF values in a nickel (Ni) metal sample, whereas discharge current significantly affected RSF, with variations up to 57.00 % for cobalt (59Co). The results revealed that matrix effects were more pronounced in metal oxides, with the relative standard deviation (RSD) of RSF for typical elements ranging from 25.17 % to 101.04 %. RSF values for the same element across different metal oxides showed a correlation with lattice binding energy (U0). Particularly, RSF values for 12 elements including 23Na, 27Al, 28Si, 45Sc, 52Cr, 56Fe, 59Co, 88Sr, 130Te, 138Ba, 139La and 140Ce within 7 metal oxides showed a significant correlation with U0 of the metal oxides. In terms of sample shape, pin samples generally yielded higher RSF values than flat samples in both metals and oxides. RSF accuracy was confirmed with standard samples of zirconium (Zr 73 MR10002 and Zr IARM 705–18) and iron oxides (Fe2O3-1 and Fe2O3-2) with relative errors within 17.50 % except for 12C. Subsequently, these RSF values were effectively utilized for the quantitative analysis of samples collected from the China Space Station (CSS). This comprehensive analysis elucidates the multifaceted influences on RSF values, enhancing the precision of trace element quantification in GD-MS.

Quantitative determination of oxides in cement raw meal based on near-infrared spectroscopy and hybrid feature selection strategy

Determining the content of components in cement raw meal is vital in Portland cement manufacturing. Near-infrared spectroscopy (NIRS)enables rapid, safe, and accurate quantitative analysis of the primary constituents of cement raw meal (CaO, SiO2, Al2O3, and Fe2O3), thereby assisting the cement industry in reducing the time delay in raw meal proportioning control and enhancing the quality of the raw meal. This study employed a hybrid feature selection strategy to identify effective feature points in the NIRS of raw meal samples. Effective feature points from the raw meal were integrated with partial least squares (PLS) to develop a regression model. Initially, NIRS data from raw meal samples were obtained using a Fourier NIR spectrometer. Subsequently, sample set partitioning based on joint X-Y distance (SPXY) was utilized to divide the samples into a calibration set (70 samples) and a validation set (22 samples). Next, A backward interval partial least squares (BiPLS) approach, along with various preprocessing algorithms, was then employed to initially screen the optimal waveband combinations of the NIRS. Within these combinations, effective feature points were further identified using competitive adaptive reweighted sampling (CARS), variable combined population analysis (VCPA) and weighted fusion variable space shrinkage approach (WFVSSA). Finally, we developed and compared PLS models based on the full spectrum and three hybrid feature selection methods. The results demonstrated that.WFVSSA offers a comprehensive assessment of wavelength importance, effectively preserving feature points while eliminating redundant information. BiPLS-WFVSSA-PLS outperformed BiPLS-CARS-PLS and BiPLS-VCPAPLS on the validation set, with R2, RMSEP, and RPD values of 0.8676, 0.1431, and 2.8129 for CaO; 0.9212, 0.1216, and 3.6461 for SiO2; 0.9198, 0.0607, and 3.5618 for Al2O3; and 0.8717, 0.0166, and 2.8614 for Fe2O3, respectively. These findings underscore the superior accuracy and stability of BiPLS-WFVSSA-PLS in determining cement raw meal component contents.

Dynamic spectral extraction method with approximately the same optical path length of non-invasive quantitative analysis of human blood components

The non-invasive quantitative analysis of blood components using spectroscopic methods based on Beer-Lambert law, and dynamic spectroscopic theory needs to meet the four applicable conditions of this law. However, in practical applications, due to the scattering characteristics of human tissue, it is difficult to meet the two conditions (parallel monochromatic light and non-scattering homogeneous system of absorbing substances), which will reduce the accuracy of quantitative analysis. To reduce the principle error caused by scattering, this paper proposes an extraction method for dynamic spectra with approximately the same optical path length. This method extracts approximately the same absorbance values from photoplethysmographic (PPG) spectral signals. A smaller absorbance value corresponds to a thinner blood layer, in which light can be approximated as parallel light, thus maximizing the satisfaction of the conditions of Beer-Lambert law. In the experimental, compared with the traditional single-edge extraction method, the proposed method has higher prediction accuracy. The correlation coefficients of the total cholesterol and triglycerides increased by 31.22% and 38.05%, and the root mean square errors decreased by 29.63% and 37.14%. The results show that the methods can significantly enhance the robustness of non-invasive quantitative analysis of blood components based on dynamic spectral theory.

Construction of a mitochondria-targeted probe to monitor cysteine levels in cancer cells and zebrafish.

Cysteine (Cys) plays an indispensable role as an antioxidant in the maintenance of bioredox homeostasis. We have constructed an efficient fluorescent probe Mito-Cys based on the binding of indole and naphthol. The acrylic ester group serves as a recognition switch for specific detection of Cys, which undergoes Michael addition and intramolecular cyclization reactions, thereby ensuring the chemical kinetics priority of Cys compared to other biothiols. The probe has good water solubility, large Stokes shift (137nm), with a detection limit of 21.81nM. In addition, cell imaging experiments have shown that the probe has excellent mitochondrial targeting ability (R = 0.902). The probe can distinguish between Cys, homocysteine (Hcy) and glutathione (GSH), and can detect Cys specifically and quickly (100s) to ensure accurate quantitative analysis of Cys changes in cells. More importantly, the probe confirms that ferroptosis inducing factors trigger thiol starvation in mitochondria, which helps to gain a deeper understanding of the physiological and pathological functions related to Cys and ferroptosis.

High sensitivity profiling of N-glycans from mouse serum using fluorescent imidazolium tags by HILIC electrospray ionisation spectrometry

N-linked glycosylation is a ubiquitous protein post-translational modification in which aberrant glycan biosynthesis has been linked to severe conditions like cancer. Accurate qualitative and quantitative analysis of N-glycans are crucial for investigating their physiological functions. Owing to the intrinsic absence of chromophores and high polarity of the glycans, current detection methods are restricted to liquid chromatography and mass spectrometry. Herein, we describe three new imidazolium-based glycan tags: 2’GITag, 3’GITag, and 4’GITag, that significantly improve both the limit of detection and limit of quantification of derivatized oligosaccharides, in terms of fluorescence intensity and ionisation efficiency. Our top-performing derivatisation agent, 4’GITag, shifted the detection sensitivity range from high femtomole to sub-femtomole levels in ESI-MS compared to traditional glycan label, 2AB, enabling the identification of 24 N-glycans in mouse serum, including those bearing sialic acids. Additionally, 4’GITag stabilized Na-salt forms of sialic acids, simplifying the simultaneous analysis of neutral and negative charged N-glycans significantly, avoiding the need for complex derivatisation procedures typically required for the detection of sialylated species. Overall, the favorable performance of imidazolium tags in the derivatisation and sensitive profiling of glycans has the potential for labeling tissue or live cells to explore disease biomarkers and for developing new targeted therapeutic strategies.