A target-triggered fluorescence-SERS dual-signal nano-system for real-time imaging of intracellular telomerase activity.

Telomerase (TE) is a promising diagnostic and prognostic biomarker for many cancers. Quantification of TE activity in living cells is of great significance in biomedical and clinical research. Conventional fluorescence-based sensors for quantification of intracellular TE may suffer from problems of fast photobleaching and auto-fluorescence of some endogenous molecules, and hence are liable to produce false negative or positive results. To address this issue, a fluorescence-SERS dual-signal nano-system for real-time imaging of intracellular TE was designed by functionalizing a bimetallic Au@Ag nanostructure with 4-p-mercaptobenzoic acid (internal standard SERS tag) and a DNA hybrid complex consisted of a telomerase primer strand and its partially complimentary strand modified with Rhodamine 6G. The bimetallic Au@Ag nanostructure serves as an excellent SERS-enhancing and fluorescence-quenching substrate. Intracellular TE will trigger the extension of the primer strand and cause the shedding of Rhodamine 6G-modified complimentary strand from the nano-system through intramolecular DNA strand displacement, resulting in the recovery of the fluorescence of Rhodamine 6G and decrease in its SERS signal. Both the fluorescence of R6G and the ratio between the SERS signals of 4-p-mercaptobenzoic acid and Rhodamine 6G can be used for in situ imaging of intracellular TE. Experimental results showed that the proposed nano-system was featured with low background, excellent cell internalization efficiency, good biocompatibility, high sensitivity, good selectivity, and robustness to false positive results. It can be used to distinguish cancer cells from normal ones, identify different types of cancer cells, as well as perform absolute quantification of intracellular TE, which endows it with great potential in clinical diagnosis, target therapy and prognosis of cancer patients.

Rapid detection and quantification of adulteration in saffron by excitation-emission matrix fluorescence combined with multi-way chemometrics.

BACKGROUND: Saffron has gained people's attention and love for its unique flavor and valuable edible value, but the problem of saffron adulteration in the market is serious. It is urgent for us to find a simple and rapid identification and quantitative estimation of adulteration in saffron. Therefore, excitation-emission matrix (EEM) fluorescence combined with multi-way chemometrics was proposed for the detection and quantification of adulteration in saffron. RESULTS: The fluorescence composition analysis of saffron and saffron adulterants (safflower, marigold and madder) were accomplished by alternating trilinear decomposition (ATLD) algorithm. ATLD and two-dimensional principal component analysis combined with k-nearest neighbor (ATLD-kNN and 2DPCA-kNN) and ATLD combined with data-driven soft independent modeling of class analogies (ATLD-DD-SIMCA) were applied to rapid detection of adulteration in saffron. 2DPCA-kNN and ATLD-DD-SIMCA methods were adopted for the classification of chemical EEM data, first with 100% correct classification rate. The content of adulteration of adulterated saffron was predicted by the N-way partial least squares regression (N-PLS) algorithm. In addition, new samples were correctly classified and the adulteration level in adulterated saffron was estimated semi-quantitatively, which verifies the reliability of these models. CONCLUSION: ATLD-DD-SIMCA and 2DPCA-kNN are recommended methods for the classification of pure saffron and adulterated saffron. The N-PLS algorithm shows potential in prediction of adulteration levels. These methods are expected to solve more complex problems in food authenticity. © 2023 Society of Chemical Industry.

Anti-interference and non-destructive identification of textile fabrics using front-face excitation-emission matrix fluorescence spectroscopy combined with multi-way chemometrics.

The identification of trace textile fabrics discovered at crime scenes plays a crucial role in the case of forensic investigations. Additionally, in practical situations, fabrics may be contaminated, making identification more challenging. To address the aforementioned issue and promote the application of fabrics identification in forensic analysis, front-face excitation-emission matrix (FF-EEM) fluorescence spectra coupled with multi-way chemometric methods were proposed for the interference-free and non-destructive identification of textile fabrics. Common commercial dyes in the same color range under different materials (cotton, acrylic, and polyester) that cannot be visually distinguished were investigated, and several binary classification models for the identification of dye were established using partial least squares discriminant analysis (PLS-DA). The identification of dyed fabrics in the presence of fluorescent interference was also taken into consideration. In each kind of pattern recognition model mentioned above, the classification accuracy (ACC) of the prediction set was 100%. The alternating trilinear decomposition (ATLD) algorithm was executed to separate mathematically and remove the interference, and the classification model based on the reconstructed spectra attained an accuracy of 100%. These findings indicate that FF-EEM technology combined with multi-way chemometric methods has broad prospects for forensic trace textile fabric identification, especially in the presence of interference.

Front-face excitation-emission matrix fluorescence spectroscopy combined with interpretable deep learning for the rapid identification of the storage year of Ningxia wolfberry.

Ningxia wolfberry stored for many years may be disguised as fresh wolfberry by unscrupulous traders and sold for huge profits. In this work, the front-face excitation-emission matrix (FF-EEM) fluorescence spectroscopy coupled with interpretable deep learning was proposed to identify the storage year of Ningxia wolfberry in a lossless, fast and accurate way. Alternating trilinear decomposition (ATLD) algorithm was used to decompose the three-way data array obtained by Ningxia wolfberry samples, extracting the chemically meaningful information. Meanwhile, a convolutional neural network (CNN) model for the identification of the storage year of Ningxia wolfberry, called EEMnet, was proposed. The model successfully classified wolfberry samples from different storage years by extracting the subtle feature differences of the spectra, and the correct classification rate of the training set, test set and prediction set was more than 98%. In addition, a series of interpretability analyses were implemented to break the "black box" of the deep learning model. These results indicated that the method based on FF-EEM fluorescence spectroscopy combined with EEMnet could quickly and accurately identify the year of Ningxia wolfberry in a green way, providing a new idea for the identification of the storage years of Chinese medicinal materials.

Chemometrics-assisted excitation-emission matrix fluorescence spectroscopy for rapid identification of commercial reconstituted and sweetened grape juices.

As a common fruit juice, grape juice is delicious and nutritious, making it very popular among consumers. However, some illegal manufacturers used shoddy products to lower costs and obtain high profits, which seriously threatens the health and interests of consumers. Hence, this paper proposed excitation-emission matrix (EEM) fluorescence spectroscopy combined with chemometric methods for the rapid identification and classification of commercial grape juices. Spectral characterization of different samples was achieved using the alternating trilinear decomposition (ATLD) algorithm, and chemically meaningful information was obtained and analyzed. Although both reconstituted and sweetened grape juices contain methyl anthranilate (MA) and 2'-aminoacetophenone (o-AAP), the content of MA in sweetened grape juice far exceeds that in reconstituted grape juice, and the MA in sweetened grape juice mainly comes from artificially added grape essence. Then two chemometric methods of hierarchical cluster analysis (HCA) and partial least squares discriminant analysis (PLS-DA) were used for the classification of reconstituted and sweetened grape juices. The results showed that the supervised classification model had a higher correct classification rate (CCR) than the unsupervised classification model, with PLS-DA obtaining 100% CCRs in both training and prediction sets. Therefore, the proposed strategy can be used as a powerful analytical method for the identification and classification of reconstituted and sweetened grape juices and provides a reliable scientific means for ensuring the authenticity and safety of the juice market.

Telomerase-initiated three-dimensional DNAzyme motor for monitoring of telomerase activity in living cells.

Telomerase (TE) is recognized as a potential biomarker for early diagnosis, monitoring and treatment of cancer. At present, most of the methods for TE detection are only applicable to in vitro assays, and unsuitable for in vivo applications. Though a few intracellular probes have been reported to have good specificity for TE, they do not involve signal amplification, which hinders their applicability in scenarios requiring high sensitivity. It is rather challenging to develop highly sensitive biosensors for intracellular TE detection due to the difficulty in design TE probes with both high specificity and compatibility with signal amplification in living cells. Herein, a highly sensitive and selective three-dimensional DNAzyme motor for monitoring of TE activity in living cells was developed by innovatively integrating TE-mediated chain replacement reaction with a three-dimensional DNA walker. Specifically, the DNAzyme motor was constructed by assembling both DNAzyme substrates and swing arms made up of a hairpin-structured DNAzyme and a telomeric primer onto gold nanoparticles. TE in cells can activate the DNAzyme motor to carry out continuous chain replacement and substrate cutting reactions, and hence realize signal amplification in living cells. The DNAzyme motor was successfully utilized to monitor the dynamic changes of TE activity in four types of cells. Due to the advantages of simple synthesis, good biocompatibility and high sensitivity and specificity for TE, the proposed DNAzyme motor is expected to have great application potential in the early diagnosis of cancer.

Intelligent analysis of excitation-emission matrix fluorescence fingerprint to identify and quantify adulteration in camellia oil based on machine learning.

Camellia oil (CAO) is a premium edible vegetable oil with medical value and biological activity, but it is susceptible to adulteration. Therefore, the demand for intelligent analysis to decipher the category and proportion of adulterated oil in CAO was the main driver of this work. Excitation-emission matrix fluorescence (EEMF) spectra of 933 vegetable oil samples were characterized by a chemometric method to obtain chemically meaningful information. Authenticity identification models were constructed using four machine learning methods to realize the discrimination of oil species adulterated in CAO mixtures. Meanwhile, quantitative models were established aiming at the fraud of CAO proportion in blended oil. Results showed that the specially constructed CNN obtained the optimal performance when evaluating unseen real-world samples, with a classification accuracy of 95.8% and 92.2%, and mean-absolute quantitative errors between 2.6 and 6.7%. Therefore, EEMF fingerprints coupled with machine learning are expected to provide intelligent and accurate analysis for authenticity detection of CAO.

Ultrasensitive detection of multiple cancer biomarkers by a triple cascade amplification strategy in combination with single particle inductively coupled plasma mass spectrometry.

A versatile triple cascade amplification strategy was developed for ultrasensitive simultaneous detection of multiple cancer biomarkers using single particle inductively coupled plasma mass spectrometry (spICP-MS). The triple cascade amplification strategy consisted of an enhanced RecJf exonuclease-assisted target recycling amplification module, a hybridization chain reaction amplification module, and a signal amplification module based on DNA-templated multiple metal nanoclusters. In the enhanced RecJf exonuclease-assisted target recycling amplification module, the DNA bases at the 5' ends of aptamers for specific recognition of biomarkers were deliberately replaced by the corresponding RNA bases to enhance amplification efficiency. The signal amplification module based on DNA-templated multiple metal nanoclusters was innovatively used to amplify the signals measured by spICP-MS and at the same time effectively suppress possible background interferences. The proposed spICP-MS platform achieved satisfactory quantitative results for both carcinoembryonic antigen (CEA) and a-fetoprotein (AFP) in human serum samples with accuracy comparable to that of the commercial ELISA kits. Moreover, it has wide dynamic ranges for both CEA (0.01-100 ng/mL) and AFP (0.01-200 ng/mL). The limit of detection for CEA and AFP was 0.6 and 0.5 pg/mL, respectively. Compared with conventional biomarkers detection methods, the proposed spICP-MS platform has the advantages of operational simplicity, ultra-high sensitivity, wide dynamic range, and low background. Therefore, it is reasonable to expect that the proposed spICP-MS platform can be further developed to be a promising alternative tool for biomarker detection in fields of clinical diagnosis and biomedical research.

Chemometrics-enhanced high-performance liquid chromatography-diode array detection strategy to quantify protoberberine alkaloids in varying Coptidis Rhizoma-related medicines.

Quantitation of protoberberine alkaloids is an essential guarantee for efficacy control and medication safety of Coptidis Rhizoma (CR) related medicines. Traditional univariate chromatography faced challenges with co-elution, unknown interferences, and retention time shift when analyzing isomeric analytes in varying sample matrices. We presented a chemometrics-enhanced high-performance liquid chromatography-diode array detection (HPLC-DAD) strategy for simultaneous quantification of six protoberberine alkaloids and processed multi-channels chromatographic-spectral data with four second-order calibration algorithms. Chromatographic conditions were firstly optimized. Four groups of predicted samples were modeled individually with the designed calibration set. Mathematical resolutions were then obtained, and pseudo-univariate regression gave the quantitative concentration of each analyte. Four models were scored on fit, linearity, recovery, and robustness, where alternating trilinear decomposition assisted multivariate curve resolution (ATLD-MCR) exhibited an optimal and stable performance. Besides, the resolved spectra presented high consistency with the actual spectra (r≥0.9993). Limits of quantification (LOQ) fully met the pharmacopoeia stipulation and were 0.17, 0.60, 0.19, 0.74, 0.15, and 0.38 µg mL-1 for columbamine, epiberberine, jatrorrhizine, coptisine, palmatine, and berberine, respectively. The importance of this strategy is to exploit collinearity resolution and additional selectivity that permit accurate quantitation at poor chromatographic resolutions, avoiding individual pretreatment and HPLC optimizations for different samples. This study provides a universal alternative for routine quality assessment of protoberberine alkaloids in CR-related medicines.

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry combined with chemometrics to identify the origin of Chinese medicinal materials.

Geographical origin and authenticity are two core factors to promote the development of traditional Chinese medicine (TCM) herbs perception in terms of quality and price. Therefore, they are important to both sellers and consumers. Herein, we propose an efficient, accurate method for discrimination of genuine and non-authentic producing areas of TCM by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Take Atractylodes macrocephala Koidz (AMK) of compositae as an example, the MALDI-TOF MS spectra data of 120 AMK samples aided by principal component analysis-linear discriminant analysis (PCA-LDA), partial least squares discriminant analysis (PLS-DA) and random forest (RF) successfully differentiated Zhejiang province, Anhui province and Hunan province AMK according to their geographical location of origin. The correct classification rates of test set were above 93.3%. Furthermore, 5 recollected AMK samples were used to verify the performance of the classification models. The outcome of this study can be a good resource in building a database for AMK. The combined utility of MALDI-TOF MS and chemometrics is expected to be expanded and applied to the origin traceability of other TCMs.

Analysis of active compounds and geographical origin discrimination of Atractylodes macrocephala Koidz. by using high performance liquid chromatography-diode array detection fingerprints combined with chemometrics.

In this work, a simple and effective strategy for the determination of 12 active compounds of Atractylodes macrocephala Koidz. (AM) was proposed by using high performance liquid chromatography-diode array detection (HPLC-DAD) combined with alternating trilinear decomposition (ATLD) algorithm. Utilizing the "second-order advantage", three common problems in HPLC could be resolved, namely baseline drifts, peak overlaps, and unknown interferences. 12 compounds were rapidly eluted within 12.5 min, and the average spiked recoveries were 80.8-109.9%. The figures of merit reflected the feasibility of the proposed method. Compared with the results of the traditional univariate calibration method based on HPLC-UV technique, the proposed strategy further verified the reliability and simplicity of the mathematical separation. On this basis, partial least squares-discriminant analysis (PLS-DA) was applied to discriminate 113 AM samples from different geographical origins, and variable importance in projection (VIP) was used to further screen the main differential components that affect the regional division of AM. A series of results show that the AM samples from the three regions have obviously different clustering trends. Overall, the strategy is expected to provide a scientific basis for the modern research of medicinal materials, and it is also conducive to the clinical use and market supervision of AM.

Single Molecule-Level Detection via Liposome-Based Signal Amplification Mass Spectrometry Counting Assay.

Because of the low atomization and/or ionization efficiencies of many biological macromolecules, the application of mass spectrometry to the direct quantitative detection of low-abundance proteins and nucleic acids remains a significant challenge. Herein, we report mass spectrum tags (MS-tags) based upon gold nanoparticle (AuNP)-templated phosphatidylcholine phospholipid (DSPC) liposomes, which exhibit high and reliable signals via electrospray ionization (ESI). Using these MS-tags, we constructed a liposome signal amplification-based mass spectrometric (LSAMS) "digital" counting assay to enable ultrasensitive detection of target nucleic acids. The LSAMS system consists of liposomes modified with a gold nanoparticle core and surface-anchored photocleavable DNA. In the presence of target nucleic acids, the modified liposome and a magnetic bead simultaneously hybridize with the target nucleic acid. After magnetic separation and photolysis, the MS-tag is released and can be analyzed by ESI-MS. At very low target concentrations, one liposome particle corresponds to one target molecule; thus, the concentration of the target can be estimated by counting the number of liposomes. With this assay, hepatitis C (HCV) virus RNA was successfully analyzed in clinical samples.

Piecewise direct standardization assisted with second-order calibration methods to solve signal instability in high-performance liquid chromatography-diode array detection systems.

In this paper, in order to solve signal instability of high-performance liquid chromatography-diode array detection (HPLC-DAD) data and maintain the second-order advantage, this work proposed piecewise direct standardization (PDS) assisted with second-order calibration methods to analyze two different complex HPLC-DAD data with signal instability, including simulated HPLC-DAD data and the data of pesticide residues in saffron. Accurate quantitative results of target analytes can be obtained by PDS combined with alternating trilinear decomposition algorithm (ATLD) and alternating trilinear decomposition assisted multivariate curve resolution algorithm (ATLD-MCR) in the presence of signal instability with time shifts and changes of peak shape. Quantitative results of the model after calibration transfer are better than those of the model established by calibration sets and prediction sets with signal instability using ATLD algorithm and ATLD-MCR algorithm under different situations. Meanwhile, t-test was used to judge whether there are significant differences between these quantitative results of models. The performances of MCR-ALS algorithm were compared with that of PDS-ATLD method and PDS-ATLD-MCR method and the proposed methods have greater potential in dealing with the case of signal instability with time shifts and changes of peak shape. In a word, this methodology can reduce the number of calibration samples for recalibration and modeling, improve the efficiency of experiment, conform to the principle of green chemistry, and obtain satisfactory quantitative results in the presence of signal instability with time shifts and changes of peak shape.

Quantitative analysis of carbaryl and thiabendazole in complex matrices using excitation-emission fluorescence matrices with second-order calibration methods.

In this paper, a fast and efficient analytical strategy was proposed that chemometrics assisted with excitation-emission fluorescence matrices was used to quantify carbaryl (CAR) and thiabendazole (TBZ) in peach, soil and sewage. Even if there are serious overlapped peaks and unknown interferences in fluorescence analysis, the second-order calibration method based on alternating trilinear decomposition (ATLD) algorithm can be used to analyze CAR and TBZ in peach, soil and sewage. The recoveries of CAR and TBZ in peach are 110.4% and 99.7% and their standard deviations are lower than 2.1% and 0.3%, respectively. In addition, the accuracy of the method was assessed with figures of merit as well as intra-day and inter-day precision. The limit of detection, the limit of quantitation of CAR and TBZ in peach are 1.2 ng mL-1 and 0.3 ng mL-1, 3.5 ng mL-1 and 0.8 ng mL-1, respectively. And their root-mean-square error of prediction are 17.0 ng mL-1 and 5.0 ng mL-1 and there are high sensitivity and selectivity in this method. Meanwhile, the results obtained by ATLD algorithm were compared with those obtained by the self-weighted alternate trilinear decomposition algorithm (SWATLD) and the parallel factor analysis (PARAFAC) algorithm, and statistical methods such as the t-test, F-test and the elliptic joint confidence region were used to evaluate for analysis. There were no significant differences among these methods. At last, high performance liquid chromatography-fluorescence detector (HPLC-FLD) was used to evaluate the accuracy and reliability of the proposed method. These results are satisfactory and indicate that the proposed method can be used for accurate and rapid determination of pesticides in complex systems.

Geographical origin traceability of traditional Chinese medicine Atractylodes macrocephala Koidz. by using multi-way fluorescence fingerprint and chemometric methods.

Atractylodes macrocephala Koidz. (AM) is an important plant of traditional Chinese medicine (TCM), and its status can be comparable with ginseng in China. The efficacy and quality of AM are closely related to the place of origin. Hence, we proposed a simple and fast strategy to classify AM from different geographical origins by using multi-way fluorescence fingerprint combined with chemometric methods. AM samples with different dilution levels have different fluorescence characteristics, resulting from different content of fluorescence components and chemical microenvironment. Therefore, AM samples were diluted 5-fold, 10-fold, and 20-fold with 40% ethanol aqueous solution to obtain excitation-emission matrix data, and multi-way (three-way and four-way) data arrays were constructed. And then, the fluorescence fingerprints of AM samples were characterized by three-way and four-way parallel factor analysis (PARAFAC). In addition, four pattern recognition methods were used to classify AM from different provinces. The results show that the four-way data array can provide more abundant information than three-way data arrays, so it is more conducive to sample classification. According to the results obtained from the analysis of four-way data array, the correct classification rate (CCR) of the cross-validation and prediction set obtained by partial least squares-discrimination analysis (PLS-DA) were 90.5% and 100%, respectively. To sum up, the proposed method can be regarded as a powerful, feasible, convenient, reliable, and universal classification tool for the classification of AM samples from different provinces and can be used as a promising method to realize the geographical origin traceability of other TCMs.

Label-free and sensitive microRNA detection method based on the locked nucleic acid assisted fishing amplification strategy.

Herein, a label free and sensitive miRNA detection method with enhanced practical applicability was developed based on the locked nucleic acid (LNA) assisted repeated fishing amplification strategy. The working mechanism of the proposed method is as follows: 1) a DNA probe (i.e, L-DNA) with LNA bases is immobilized onto the surface of a gold foil. The L-DNA hybridizes with the 3' terminus of the first strands of complementary deoxyribonucleic acid (cDNA) of the target miRNA in the test samples; 2) The protruding 5' terminus of the cDNA serves as a 'fishhook' to repeatedly fish the products of a hybridization chain reaction (HCR) out from a 'reaction tube'; 3) The HCR products can be unloaded from the gold foil into a 'product tube' through temperature-controlled dehybridization; 4) The concentration of the target miRNA is determined based on the fluorescence intensity generated by the addition of SYBR-Green I (SG) into the 'product tube'. The proposed platform was applied to the detection of miRNA-122 in cell lysate samples and obtained quantitative results with accuracy comparable to the quantitative reverse transcription PCR method (qRT-PCR). It is worth pointing out that the proposed platform achieved a limit of detection value of 2.9 fM for miRNA-122 by a simple but effective LNA-assisted repeated fishing amplification strategy instead of complicated enzyme-based amplification techniques. It is reasonable to expect that the proposed method provides a competitive alternative for designing practically applicable, cost-effective and label-free miRNA detection methods.

Rapid determination of sulfamethoxazole and trimethoprim illegally added to health products using excitation-emission matrix fluorescence coupled with the second-order calibration method.

In this work, a simple and fast analytical method based on a self-weighted alternating trilinear decomposition (SWATLD) algorithm coupled with excitation-emission matrix (EEM) fluorescence was developed for the simultaneous determination of sulfamethoxazole (SMZ) and trimethoprim (TMP) illegally added to health products. With the second-order advantage, the proposed method obtained satisfactory results in the presence of peak overlap and unknown interferences. The analysis time for a single sample is only 0.8 minutes. The average spiked recoveries of SMZ and TMP in three health product spiked samples were in the range of 91.0-106.2% and 86.8-107.8%, respectively. The relative standard deviations (RSDs) were lower than 8.6%. In addition, verification parameters including sensitivity (SEN), selectivity (SEL), the limit of detection (LOD), the limit of quantification (LOQ), intra-day precision, and inter-day precision were calculated, and the results show that the proposed method is feasible. The quantitative results of the proposed method were further confirmed by the LC-MS/MS method, which proved that the proposed method was efficient and green for drug-abuse monitoring of SMZ and TMP in health products.

Boronate carbon nanoparticles featuring efficient FRET for activatable two-photon fluorescence imaging of sialic acid surface-abundant tumor cells.

Two-photon carbon-based nanoprobes hold great potential for biomedical applications as a result of their advantages of low fluorescence background, deep tissue imaging penetration and enhanced spatial resolution. However, the development of an activatable two-photon fluorescence carbon-based nanoprobe that simultaneously has the ability to target desired organs or cells is highly desired but remained a largely unsolved challenge. Herein, we developed boronate affinity BCNP@MnO2 nanocomposites, constructed by one step in situ growth of MnO2 nanosheets on the surface of aminophenylboronic acid-functionalized CNPs (BCNPs) via a redox reaction, which can feature efficient fluorescence energy transfer quenching to the BCNPs, allowing for tumor-specific affinity recognition and two-photon fluorescence activation imaging. By utilizing the inherent two-photon optical properties and sialic acid (SA) specific targeting ability of the BCNPs, good biocompatibility of the nanocomposites as well as highly sensitive and selective responses of MnO2 nanosheets towards GSH, the developed nanocomposites have demonstrated specific two-photon fluorescence activation imaging in target cancer cells and nude mouse tissues. Therefore, our proposed novel strategy could be used for monitoring GSH-triggered two-photon fluorescence activation events in SA-overexpressed cancer cells and has promising applications in both biological exploration and clinical diagnosis.

Simultaneous determination of nine tyrosine kinase inhibitors in three complex biological matrices by using high-performance liquid chromatography-diode array detection combined with a second-order calibration method.

An intelligent chemometric second-order calibration method called alternating trilinear decomposition- assisted multivariate curve resolution combined with high-performance liquid chromatography-diode array detection was used for the simultaneous quantification of nine tyrosine kinase inhibitors in three complex biological systems. The method allows simultaneous quantification of the components in different biological matrices without the need for cumbersome pre-treatment steps, complex elution conditions, and complete peak separation. Even with the varying time shift, severe peak overlap, and various unknown interferences, the proposed method can extract pure chromatographic and spectroscopic information for each analyte, while providing accurate qualitative and quantitative results of nine common tyrosine kinase inhibitors in three different biological matrices. All the drugs were eluted in 7 min. The results showed that the nine drugs in each matrix showed good linearity (r > 0.984) in the calibration range with a root mean square error of calibration less than 0.9 µg/mL. The average spiked recoveries of the target analytes were all in the range of 83.4-110.0%, with standard deviations less than 9.0%. Finally, the classical method was used to validate the proposed method. In comparison to the traditional method, the proposed strategy is accuracy, simultaneous, and interference-free.

Single-Nanoparticle ICP-MS for Sensitive Detection of Uracil-DNA Glycosylase Activity.

Single-nanoparticle inductively coupled plasma mass spectrometry (SP-ICP-MS) has demonstrated unique advantages for the detection of biological samples. However, methods for enzyme activity detection based on SP-ICP-MS technology have been rarely explored. Here we report the development of a novel SP-ICP-MS assay for uracil-DNA glycosylase (UDG) activity detection based on its ability to specifically recognize and remove uracil to induce the cleavage of the DNA probe. Our design allows the generation of single gold nanoparticles correlated to the specific enzymatic reaction for a highly sensitive SP-ICP-MS measurement. The developed assay enables sensitive UDG activity detection with a detection limit of 0.0003 U/mL. The cell lysate analysis by the developed assay reveals its applicability for the detection of UDG activity in real samples. It is envisioned that our design may provide a new paradigm for developing the SP-ICP-MS assay for enzyme activity detection in biological samples.