Two-Dimensional Cobalt-Doped Ti3C2 MXene Nanozyme-Mediated Homogeneous Electrochemical Strategy for Pesticides Assay Based on In Situ Generation of Electroactive Substances.

Common homogeneous electrochemical (HEC) sensors usually suffer from the drawbacks of high background signal, low signal-to-noise ratio, and even false positive results due to the preaddition of electroactive substances. Thus, it is necessary to develop novel HEC sensors based on in situ generation of electroactive substances to overcome these shortcomings, which, however, is underexplored. In this work, two-dimensional (2D) nanozymes, i.e., cobalt-doped 2D Ti3C2 MXene nanosheets (CMNSs), with excellent peroxidase-like properties were utilized to develop HEC sensors based on the in situ generation of electroactive substances for organophosphate pesticides (OPs) detection. The 2D CMNSs were synthesized via a template-directed wet chemical approach and displayed outstanding features of hydrophilia and water dispersibility, which could catalyze the oxidation of o-phenylenediamine (OPD) to generate significantly increased reduction current. Interestingly, the 2D CMNSs with peroxidase-like properties exhibited a unique response to thiol compounds and were thus employed as highly efficient catalysts to develop HEC sensors for OPs based on the hydrolysis of acetylthiocholine (ATCh) to form thiocholine catalyzed by acetylcholinesterase (AChE) and the inhibition of AChE activity by OPs. The recovery for OPs analysis of pakchoi extract solutions ranged from 97.4% to 103.3%. The as-proposed HEC sensor based on in situ generation of electroactive substances will provide a new way for the development of high-performance electrochemical sensors and demonstrate potential applicability for the determination of pesticide residues in real samples.

One-Step Synthesis of Methylene Blue-Encapsulated Zeolitic Imidazolate Framework for Dual-Signal Fluorescent and Homogeneous Electrochemical Biosensing.

In vitro diagnosis requires target biomarkers to be reliably detected at an ultralow level. A dual-signal strategy permits self-calibration to overcome the interferences of experimental and environmental factors, and thus is regarded as a promising approach. However, currently reported works mainly concentrated on the same forms of energy of output signals. Herein, we propose a one-step strategy for synthesis of methylene blue-encapsulated zeolitic imidazolate framework-90 (MB@ZIF-90) with high loading, unique dual-signal property, exceptional recognition capability, and good stability, and we further pioneer MB@ZIF-90 as a dual-signal biosensor for label-free, enzyme-free, and ultrasensitive detection of adenosine triphosphate (ATP) by integration of fluorescence and homogeneous electrochemical techniques. The recognition of MB@ZIF-90 by target ATP spurs the decomposition of ZIF-90, subsequently permitting MB to be released into a supernatant. As compared to the case where ATP does not exist, obviously increased intensities in fluorescence and differential pulse voltammetry current are observed and both signals are directly proportional to ATP concentrations. Thus, the MB@ZIF-90-based biosensor achieved dual-signal detection of ATP in an ultrasensitive manner and displayed a more reliable diagnosis result than previously reported ATP biosensors. This dual-signal strategy provides a new opportunity to develop high-performance biosensors for in vitro diagnosis and demonstrates great potential for future applications in bioinformatics and clinical medicine.

Nucleic Acid-Functionalized Metal-Organic Framework-Based Homogeneous Electrochemical Biosensor for Simultaneous Detection of Multiple Tumor Biomarkers.

Simultaneous detection of multiple tumor biomarkers is in great demand for early and accurate cancer diagnosis. A homogeneous electrochemical biosensor has been proven to possess high sensitivity, but achieving simultaneous detection of multiple tumor biomarkers is still a challenge. Herein, we develop a novel homogeneous electrochemical biosensor for simultaneous detection of multiple tumor biomarkers based on the functionalized metal-organic frameworks (MOFs). The functionalized MOFs were prepared by using porous UIO-66-NH2 as nanocontainer to load electroactive dyes and dsDNA as a gatekeeper to cap MOFs. In this context, two functionalized MOFs (MB@UIO and TMB@UIO) were fabricated and applied to simultaneous detection of let-7a and miRNA-21, used as the proof-of-concept analytes. The recognition and hybridization of PX with target miRNAs impel the generation of RNA-DNA complexes, which separated from MOFs and allowed the electroactive dyes to be released. In comparison with the case when target miRNAs are absent, two stronger signals are recorded, and dependent on target miRNA concentrations. Thus, simultaneous detection of let-7a and minRNA-21 is achieved, with detection limits down to 3.6 and 8.2 fM, respectively, comparable or lower than those of reported strategies that concentrated on single miRNA detection. Moreover, the proposed biosensor has also been successfully applied for simultaneous detection of target miRNAs spiked in serum samples. Therefore, the proposed strategy was expected to provide more information for early and accurate cancer diagnosis and was an useful application in disease diagnosis and clinical biomedicine.

Paper-based fluorescent sensor via aggregation induced emission fluorogen for facile and sensitive visual detection of hydrogen peroxide and glucose.

Hydrogen peroxide (H2O2), an important reactive oxygen species (ROS), is related to the oxidative stress in organisms, and plays important roles in a variety of cellular activities as well. So it is of crucial importance to develop sensitive and accurate sensing strategies to detect H2O2 in biological systems. Herein, by taking advantage of the unique emission characteristics of aggregation induced emission (AIE) fluorogens, we proposed a non-enzymatic fluorescence platform for facile and sensitive detection of H2O2, both in solution state using fluorescence spectrometer and on paper-based sensor via visual inspection. Through the reaction between L-cysteine and H2O2, the fluorescence of TPE-M-L, an AIE fluorogen formed between maleimide-functionalized tetraphenylethene (TPE-M) and L-cysteine, is quenched, and highly sensitive non-enzymatic H2O2 assay is readily carried out. The limit of detection (LOD) of 10nM in solution state and 2.5µM on paper-based sensor were obtained for H2O2 detection, which were superior or comparable to those previously reported in literature. Moreover, by integrating glucose oxidase with the AIE fluorogen of TPE-M-L, highly sensitive and selective glucose detection was also conveniently achieved both in solution state and on paper-based sensor by the as-proposed strategy, with the LODs of 50nM in solution state and 10µM via visual observation, much better than those obtained by other fluorescence methods. The as-proposed sensing strategy was also successfully applied to assay glucose in human serum samples. Therefore, the paper-based fluorescence sensor exhibits the advantages of simple fabrication, high sensitivity and portability, and has great potential to be applied in on-site assay of H2O2 and glucose in real samples.

Label-Free and Ultrasensitive Biomolecule Detection Based on Aggregation Induced Emission Fluorogen via Target-Triggered Hemin/G-Quadruplex-Catalyzed Oxidation Reaction.

Fluorescence biosensing strategy has drawn substantial attention due to their advantages of simplicity, convenience, sensitivity, and selectivity, but unsatisfactory structure stability, low fluorescence quantum yield, high cost of labeling, and strict reaction conditions associated with current fluorescence methods severely prohibit their potential application. To address these challenges, we herein propose an ultrasensitive label-free fluorescence biosensor by integrating hemin/G-quadruplex-catalyzed oxidation reaction with aggregation induced emission (AIE) fluorogen-based system. l-Cysteine/TPE-M, which is carefully and elaborately designed and developed, obviously contributes to strong fluorescence emission. In the presence of G-rich DNA along with K+ and hemin, efficient destruction of l-cysteine occurs due to hemin/G-quadruplex-catalyzed oxidation reactions. As a result, highly sensitive fluorescence detection of G-rich DNA is readily realized, with a detection limit down to 33 pM. As a validation for the further development of the proposed strategy, we also successfully construct ultrasensitive platforms for microRNA by incorporating the l-cysteine/TPE-M system with target-triggered cyclic amplification reaction. Thus, this proposed strategy is anticipated to find use in basic biochemical research and clinical diagnosis.

Paper-based fluorescent sensor for rapid naked-eye detection of acetylcholinesterase activity and organophosphorus pesticides with high sensitivity and selectivity.

Various strategies have been proposed for the sensing of acetylcholinesterase (AChE) activity and organophosphorus pesticides (OPs). However, the practical application of most methods is restricted by their intrinsic drawbacks such as complexity, long analysis time, and high cost. Thus, it is highly desirable to develop simple, fast and sensitive approaches for AChE activity and OPs detection. Herein, we reported a simple paper-based fluorescent sensor (PFS) based on the aggregation induced emission (AIE) effect of tetraphenylethylene (TPE) and the addition reaction capability of maleimide, which has been used as a powerful tool for rapid naked-eye detection of AChE activity and OPs. The introduction of TPE provides the probe with unique fluorescence property in solid state and is of great importance for improving the sensitivity of PFS. The hydrolysis product of acetylthiocholine catalyzed by AChE induced the maleimide ring destruction and activated the fluorescence performance of TPE. Given that AChE activity can be specifically inhibited by OPs, the as-proposed PFS can also be utilized for sensitive detection of OPs. Meanwhile, the variation of fluorescence signal can be readily detected by naked eyes, and low detection limits of 2.5mUmL(-1) and 0.5ngmL(-1) for AChE activity and OPs are obtained, respectively. Moreover, it has been successfully applied for AChE activity and OPs detection in diluted human serum samples, showing its great potential to be applied in real samples. Thus, this strategy possesses considerable advantages of simplicity, rapid detection, portability, cost efficiency and visualization.

Automated extraction of precise protein expression patterns in lymphoma by text mining abstracts of immunohistochemical studies.

BACKGROUND: In general, surgical pathology reviews report protein expression by tumors in a semi-quantitative manner, that is, -, -/+, +/-, +. At the same time, the experimental pathology literature provides multiple examples of precise expression levels determined by immunohistochemical (IHC) tissue examination of populations of tumors. Natural language processing (NLP) techniques enable the automated extraction of such information through text mining. We propose establishing a database linking quantitative protein expression levels with specific tumor classifications through NLP. MATERIALS AND METHODS: Our method takes advantage of typical forms of representing experimental findings in terms of percentages of protein expression manifest by the tumor population under study. Characteristically, percentages are represented straightforwardly with the % symbol or as the number of positive findings of the total population. Such text is readily recognized using regular expressions and templates permitting extraction of sentences containing these forms for further analysis using grammatical structures and rule-based algorithms. RESULTS: Our pilot study is limited to the extraction of such information related to lymphomas. We achieved a satisfactory level of retrieval as reflected in scores of 69.91% precision and 57.25% recall with an F-score of 62.95%. In addition, we demonstrate the utility of a web-based curation tool for confirming and correcting our findings. CONCLUSIONS: The experimental pathology literature represents a rich source of pathobiological information, which has been relatively underutilized. There has been a combinatorial explosion of knowledge within the pathology domain as represented by increasing numbers of immunophenotypes and disease subclassifications. NLP techniques support practical text mining techniques for extracting this knowledge and organizing it in forms appropriate for pathology decision support systems.