Magnetic beads and GO-assisted enzyme-free signal amplification fluorescent biosensors for disease diagnosis.

Cancer detection is still a major challenge in public health. Identification of oncogene is the first step toward solving this problem. Studies have revealed that various cancers are associated with miRNA expression. Therefore, the sensitive detection of miRNA is substantially important to solve the cancer problem. In this study, let-7a, a representative substance of miRNA, was selected as the detection target. With the assistance of magnetic beads commonly used in biosensors and self-synthesized graphene oxide materials, specificity and sensitivity detection of the target gene let-7a were achieved via protease-free signal amplification. The limit of detection (LOD) was as low as 15.015pM. The fluorescence signal intensity showed a good linear relationship with the logarithm of let-7a concentration. The biosensor could also detect let-7a in complex human serum samples. Overall, this fluorescent biosensor is not only simple to operate, but also strongly specificity to detect let-7a. Therefore, it has substantial potential for application in the early diagnosis of clinical medicine and biological research.

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.

Development of a separation platform comprising magnetic beads combined with the CRISPR/Cas12a system enabling ultrasensitive and rapid detection of miRNA-21.

A separation platform has been developed mediated by a combination of magnetic beads and the CRISPR/Cas12a system to achieve ultrasensitive and rapid detection of miRNA-21 at a low level. In this system, with the assistance of an auxiliary probe, the target miRNA-21 can be specifically combined with three-stranded probes to initiate the SDR reaction. Abundant aptamer A3 was added to the solution that can activate the CRISPR/Cas12a system and initiate the trans-cleavage reaction to recover the fluorescence signal. Using magnetic beads to mediate the separation considerably greatly improves the signal conversion efficiency and detection sensitivity. At the 492 nm excitation wavelength, and 502-650 nm scan range, through analyzing the fluorescence peak intensity at 520 nm, the biosensor's determination range and limit of detection is 8 fM-250 nM and 2.42 fM, respectively, and the RSD is 19.03-37.80. Compared with other biosensors, the biosensor developed exhibited superior performance and the signal recovered excellently in 1% human serum and the LOD is 12.12 fM. This method provides a novel highly sensitive scheme for detecting miRNA .

Biosensor model based on single hairpin structure for highly sensitive detection of multiple targets.

Nowadays, due to the genetic information carried by nucleic acids, they can serve as a biomarker for the early diagnosis of diseases, including tumors and cardiovascular disease, among others, making genetic testing a hotspot of biomedicine. Therefore, we have designed a universal fluorescence biosensor that can detect multiple DNA sequences with good performance. In our designed biosensor, λ exonuclease is used due to its ability to digest double-stranded DNA from the phosphorylated 5'- end and promote the targeted cycle. The exonuclease is introduced into a DNA hairpin containing a target recognition sequence. Hence, with the target, λ exonuclease-assisted targeted recycling can be activated. The hydrolyzed DNA hairpin triggers a strand displacement reaction between the hairpin probe (H1) and F-Q double DNA strand (F-Q), increasing the distance between the fluorescent chain (F) and quenching chain (Q); thus the fluorescence signal is emitted. It is exciting that the detection limit of the biosensor is 300 fM, which is relatively low, and there is an excellent linear relationship between fluorescence intensity and target concentration. Moreover, the biosensor we designed has universal applicability in the detection of other genes, and the range of RSD is 1.28-2.45%. Hence, it has good application prospects and practical value in the early detection of some diseases and the design of fluorescent biosensors.

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.

Entropy-driven DNA circuit with two-stage strand displacement for elegant and robust detection of miRNA let-7a.

MicroRNAs (miRNAs) research in cancer diagnosis is expanding, on account of miRNAs were demonstrated to be key indicator of gene expression and hopeful candidates for biomarkers. In this study, a stable miRNA-let-7a fluorescent biosensor was successfully designed based on an exonuclease Ⅲ-assisted two-stage strand displacement reaction (SDR). First, an entropy-driven SDR containing a three-chain structure of the substrate is used in our designed biosensor, leading to reduce the reversibility of the target recycling process in each step. The target acts on the first stage to start the entropy-driven SDR, which generates the trigger used to stimulate the exonuclease Ⅲ-assisted SDR in the second stage. At the same time, we design a SDR one-step amplification strategy as a comparison. Expectly, this developed two-stage strand displacement system has a low detection limit of 25.0 pM as well as a broad detection range of 4 orders of magnitude, making it more sensitive than the SDR one-step sensor, whose detection limit is 0.8 nM. In addition, this sensor has high specificity across members of the miRNA family. Therefore, we can take advantage of this biosensor to promote miRNA research in cancer diagnosis sensing systems.

Construction of hybridization chain reaction induced optical signal directed change of photonic crystals-DNA hydrogel sensor and its visual determination for aflatoxin B1.

Herein, we have introduced hybridization chain reaction (HCR) into the photonic crystals (PhCs) hydrogel, for the first time, realizing HCR for inducing the change of the optical signal of PhCs hydrogel and using this hydrogel as a sensor for determination of the aflatoxin B1 (AFB1). By using specific sequences as the cross-linker, the extension of the cross-linker by HCR drives the swelling of the hydrogel, and the optical property of 2D PhCs array converts this swelling into a change of the Debye diffraction ring. Moreover, by further selecting the aptamer to construct the cross-linker, the hydrogel is also endowed with a unique capability for AFB1, making the hydrogel a novel sensor based on the signal amplification strategy. The results show that the designed hairpin DNAs can effectively trigger the HCR and cause the swelling of hydrogel, and the hydrogel sensor has a good determination performance and high specific recognition for AFB1.

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.

A fully automatic target detection and quantification strategy based on object detection convolutional neural network YOLOv3 for one-step X-ray image grading.

Methods for automatic image analysis are demanded for dealing with the explosively increased imaging data in clinics. Osteoarthritis (OA) is a typical disease diagnosed based on X-ray imaging. Herein, we propose a novel modeling strategy based on YOLO version 3 (YOLOv3) for automatic simultaneous localization of knee joints and quantification of radiographic knee OA. As an advanced deep convolutional neural network (CNN) algorithm for target detection, YOLOv3 enables simultaneous small object detection and quantification due to its unique residual connection and feature map merging. Hence, a unified CNN model is built for the elegant integration of knee joint detection and corresponding OA severity grading using the YOLOv3 framework. We achieve desirable accuracy in knee OA grading using the public and clinical datasets. It provides improvements in the precision, recall, F1 score and diagnostic accuracy of knee OA as well. Because of the fully automatic target detection and quantification, the time of handling an image is merely 40 ms from inputting the image to getting its label, supporting quick clinic decisions. It, thus, affords convenient and efficient image analysis for daily clinical diagnosis.

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.

Fluorescence biosensing of the leukemia gene by combining Target-Programmed controllable signal inspiring engineering.

Clinical diagnosis urgently requires ultrasensitive, accurate and rapid monitoring of low-abundance biomarkers. A biosensing strategy capable of detecting target genes at the femtomolar scale was designed in this work. In the biosensing strategy, the target can induce the specially designed hairpin probe H1 to self-fold and form a 3' blunt-ended structure. When there are the hybrid double-stranded P1-T1, ligase, polymerase and nickase, the target gene was recycled, and at the same time the system produces a lot of T1 and T2. T1 and T2 can simultaneously trigger HCR, causing the modified fluorophore FAM on the DNA strand to move away from the quencher group BHQ. The amplified fluorescent signal can be captured by a fluorescence instrument. It is exciting for us that three signal amplifications are involved to achieve femtomolar detection of target genes, namely target recycling, dual-triggered HCR of T1 and T2, and HCR. In addition, it still has good detection ability in actual samples simulated by serum. We expect that the sensing strategy proposed in this paper offers great potential for biomarker detection of leukemia for early clinical diagnosis.

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.

A Pax-5a gene analysis approach enabled by selective digestion with lambda exonuclease.

Owing to the rapid increase in acute leukemia patients, the detection of Pax-5a, which is a tumor marker, is very important for the early diagnosis of patients. Therefore, by combining the selective digestion function of lambda exonuclease and the hybridization chain reaction (HCR) enzyme-free amplification system, we design a biosensor to detect the Pax-5a gene with high sensitivity. Lambda exonuclease can cleave the blunt end formed by the hairpin probe and the Pax-5a gene, which exposes the nucleic acid sequence that can initiate the HCR. When the HCR is triggered, the fluorophore and quencher on H1 and H2 move away from each other, so that the fluorescence signal of the quenched fluorophore can be recovered. Under optimal experimental conditions, a good linear relationship was established between the fluorescence intensity and the logarithm of the target concentration, and the limit of detection (LOD) of Pax-5a was calculated to be 7.6 pM. In addition, the biosensor can not only discriminate the base mismatch sequences of the Pax-5a gene, but also be suitable for target detection in complex human serum samples. Therefore, this biosensor, with the advantages of simple operation, high sensitivity, and good selectivity, has a good application prospect and guiding role in the diagnosis of acute B lymphocytic leukemia and the design of biosensors.

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.

Partial induced reorientation of 5CB in a liquid crystal microarray and a signal-on sensing assay for the detection of aflatoxin B1.

Herein, a signal-on liquid crystal microarray (LCM) sensor is designed for the first time with a micro-spectral optical sensing signal. Depending on the change of the orientation of the LC molecules in the LCM films and the intensity of the spectral peaks of the PhCs, the signal-on LCM biosensor achieves the detection of AFB1 and the Partial Response Mechanism (PSM) of the LCM films is discovered.