CRISPR/Cas12a-Powered Amplification-Free RNA Diagnostics by Integrating T7 Exonuclease-Assisted Target Recycling and Split G-Quadruplex Catalytic Signal Output.

Rapid and sensitive RNA detection is of great value in diverse areas, ranging from biomedical research to clinical diagnostics. Existing methods for RNA detection often rely on reverse transcription (RT) and DNA amplification or involve a time-consuming procedure and poor sensitivity. Herein, we proposed a CRISPR/Cas12a-enabled amplification-free assay for rapid, specific, and sensitive RNA diagnostics. This assay, which we termed T7/G4-CRISPR, involved the use of a T7-powered nucleic acid circuit to convert a single RNA target into numerous DNA activators via toehold-mediated strand displacement reaction and T7 exonuclease-mediated target recycling amplification, followed by activating Cas12a trans-cleavage of the linker strands inhibiting split G-Quadruplex (G4) assembly, thereby inducing fluorescence attenuation proportion to the input RNA target. We first performed step-by-step validation of the entire assay process and optimized the reaction parameters. Using the optimal conditions, T7/G4-CRISPR was capable of detecting as low as 3.6 pM target RNA, obtaining ∼100-fold improvement in sensitivity compared with the most direct Cas12a assays. Meanwhile, its excellent specificity could discriminate single nucleotide variants adjacent to the toehold region and allow species-specific pathogen identification. Furthermore, we applied it for analyzing bacterial 16S rRNA in 40 clinical urine samples, exhibiting a sensitivity of 90% and a specificity of 100% when validated by RT-quantitative PCR. Therefore, we envision that T7/G4-CRISPR will serve as a promising RNA sensing approach to expand the toolbox of CRISPR-based diagnostics.

Multivariate Silicification-Assisted Single Enzyme Structure Augmentation for Improved Enzymatic Activity-Stability Trade-Off.

The ability to finely tune/balance the structure and rigidity of enzymes to realize both high enzymatic activity and long-term stability is highly desired but highly challenging. Herein, we propose the concept of the "silicazyme", where solid inorganic silica undergoes controlled hybridization with the fragile enzyme under moderate conditions at the single-enzyme level, thus enabling simultaneous structure augmentation, long-term stability, and high enzymatic activity preservation. A multivariate silicification approach was utilized and occurred around individual enzymes to allow conformal coating. To realize a high activity-stability trade-off the structure flexibility/rigidity of the silicazyme was optimized by a component adjustment ternary (CAT) plot method. Moreover, the multivariate organosilica frameworks bring great advantages, including surface microenvironment adjustability, reversible modification capability, and functional extensibility through the rich chemistry of silica. Overall silicazymes represent a new class of enzymes with promise for catalysis, separations, and nanomedicine.

Synthetic Biohybrids of Red Blood Cells and Cascaded-Enzymes@ Metal-Organic Frameworks for Hyperuricemia Treatment.

Hyperuricemia, caused by an imbalance between the rates of production and excretion of uric acid (UA), may greatly increase the mortality rates in patients with cardiovascular and cerebrovascular diseases. Herein, for fast-acting and long-lasting hyperuricemia treatment, armored red blood cell (RBC) biohybrids, integrated RBCs with proximal, cascaded-enzymes of urate oxidase (UOX) and catalase (CAT) encapsulated within ZIF-8 framework-based nanoparticles, have been fabricated based on a super-assembly approach. Each component is crucial for hyperuricemia treatment: 1) RBCs significantly increase the circulation time of nanoparticles; 2) ZIF-8 nanoparticles-based superstructure greatly enhances RBCs resistance against external stressors while preserving native RBC properties (such as oxygen carrying capability); 3) the ZIF-8 scaffold protects the encapsulated enzymes from enzymatic degradation; 4) no physical barrier exists for urate diffusion, and thus allow fast degradation of UA in blood and neutralizes the toxic by-product H2 O2 . In vivo results demonstrate that the biohybrids can effectively normalize the UA level of an acute hyperuricemia mouse model within 2 h and possess a longer elimination half-life (49.7 ± 4.9 h). They anticipate that their simple and general method that combines functional nanomaterials with living cell carriers will be a starting point for the development of innovative drug delivery systems.

Metagenomics as New Tool for Diagnosis of Scrub Typhus: Two Case Reports.

Scrub typhus is a vector-borne infectious disease caused by Orientia tsutsugamushi. Accurate and timely diagnosis at the early infection stage could save the patients' lives. Traditional technologies were limited to rapidly and successfully detecting Orientia tsutsugamushi due to poor specificity, especially in the condition of atypical symptoms. The technology of Metagenomic next-generation sequencing (mNGS) is amenable to finding the real pathogen because it holds potential as a diagnostic platform for unbiased pathogen identification and precision medicine. Herein, we reported two clinical case reports relative to the Orientia tsutsugamushi infection diagnosed by mNGS. We hope these two cases will improve clinical diagnosis.

A new dual functional H2S donor for fluorescence imaging and anti-inflammatory application.

This study explored FL-H2S, a novel fluorescein-based H2S donor, as an anti-inflammatory agent. The results demonstrated the efficient release of H2S by FL-H2S, along with its biocompatibility, real-time intracellular H2S release and imaging capability. In vivo experiments using a rat model confirmed the anti-inflammatory effects of FL-H2S, evidenced by reduced foot swelling. We also successfully elucidated the anti-inflammatory mechanism through ELISA and WB analysis.

Kernel Path for ν-Support Vector Classification.

It is well known that the performance of a kernel method is highly dependent on the choice of kernel parameter. However, existing kernel path algorithms are limited to plain support vector machines (SVMs), which has one equality constraint. It is still an open question to provide a kernel path algorithm to ν -support vector classification ( ν -SVC) with more than one equality constraint. Compared with plain SVM, ν -SVC has the advantage of using a regularization parameter ν for controlling the number of support vectors and margin errors. To address this problem, in this article, we propose a kernel path algorithm (KP ν SVC) to trace the solutions of ν -SVC exactly with respect to the kernel parameter. Specifically, we first provide an equivalent formulation of ν -SVC with two equality constraints, which can avoid possible conflicts during tracing the solutions of ν -SVC. Based on this equivalent formulation of ν -SVC, we propose the KP ν SVC algorithm to trace the solutions with respect to the kernel parameter. However, KP ν SVC traces nonlinear solutions of kernel method rather than the errors of loss function, and it is still a challenge to provide the algorithm that guarantees to find the global optimal model. To address this challenging problem, we extend the classical error path algorithm to the nonlinear kernel solution paths and propose a new kernel error path (KEP) algorithm that ensures to find the global optimal kernel parameter by minimizing the cross validation error. We also provide the finite convergence analysis and computational complexity analysis to KP ν SVC and KEP. Extensive experimental results on a variety of benchmark datasets not only verify the effectiveness of KP ν SVC but also show the advantage of applying KEP to select the optimal kernel parameter.

Accelerating Sequential Minimal Optimization via Stochastic Subgradient Descent.

Sequential minimal optimization (SMO) is one of the most popular methods for solving a variety of support vector machines (SVMs). The shrinking and caching techniques are commonly used to accelerate SMO. An interesting phenomenon of SMO is that most of the computational time is wasted on the first half of iterations for building a good solution closing to the optimal. However, as we all know, the stochastic subgradient descent (SSGD) method is extremely fast for building a good solution. In this paper, we propose a generalized framework of accelerating SMO through SSGD for a variety of SVMs of binary classification, regression, ordinal regression, and so on. We also provide a deep insight about why SSGD can accelerate SMO. Experimental results on a variety of datasets and learning applications confirm that our method can effectively speed up SMO.

Scalable Kernel Ordinal Regression via Doubly Stochastic Gradients.

Ordinal regression (OR) is one of the most important machine learning tasks. The kernel method is a major technique to achieve nonlinear OR. However, traditional kernel OR solvers are inefficient due to increased complexity introduced by multiple ordinal thresholds as well as the cost of kernel computation. Doubly stochastic gradient (DSG) is a very efficient and scalable kernel learning algorithm that combines random feature approximation with stochastic functional optimization. However, the theory and algorithm of DSG can only support optimization tasks within the unique reproducing kernel Hilbert space (RKHS), which is not suitable for OR problems where the multiple ordinal thresholds usually lead to multiple RKHSs. To address this problem, we construct a kernel whose RKHS can contain the decision function with multiple thresholds. Based on this new kernel, we further propose a novel DSG-like algorithm, DSGOR. In each iteration of DSGOR, we update the decision functional as well as the function bias with appropriately set learning rates for each. Our theoretic analysis shows that DSGOR can achieve O(1/t) convergence rate, which is as good as DSG, even though dealing with a much harder problem. Extensive experimental results demonstrate that our algorithm is much more efficient than traditional kernel OR solvers, especially on large-scale problems.

Advanced Nanomaterials-Assisted Cell Cryopreservation: A Mini Review.

Cell cryopreservation is of vital significance both for transporting and storing cells before experimental/clinical use. Cryoprotectants (CPAs) are necessary additives in the preserving medium in cryopreservation, preventing cells from freeze-thaw injuries. Traditional organic solvents have been widely used in cell cryopreservation for decades. Given the obvious damage to cells due to their undesirable cytotoxicity and the burdensome post-thaw washing cycles before use, traditional CPAs are more and more likely to be replaced by modern ones with lower toxicity, less processing, and higher efficiency. As materials science thrives, nanomaterials are emerging to serve as potent vehicles for delivering nontoxic CPAs or inherent CPAs comparable to or even superior to conventional ones. This review will introduce some advanced nanomaterials (e.g., organic/inorganic nanoCPAs, nanodelivery systems) utilized for cell cryopreservation, providing broader insights into this developing field.

AND logic gate based fluorescence probe for simultaneous detection of peroxynitrite and hypochlorous acid.

Hypochlorous acid (HOCl) and peroxynitrite (ONOO-) are two of the most important reactive species and associated with various diseases in various physiological and pathological processes. Nonetheless, many of their roles are still vague due to the shortage of methods for simultaneously detecting HOCl and ONOO-. Herein, three simple yet useful fluorogenic probes, LG-1, LG-2 and LG-3, have been fabricated with facile synthesis route and used to monitor the coexistence of HOCl and ONOO- as AND-based logic gate fluorescent probe firstly. LG-1 and LG-2, which consists of 1,3-oxathiolane group and boronate group respectively, were designed to verify the capacity of monitoring HOCl and ONOO- without interference from each other. The result showed that these two groups are perfect reaction sites of detecting HOCl and ONOO- respectively via specific analyte-induced reactions. Hence, LG-3, which is attached by these two groups to suppress the fluorophore core, can response to HOCl and ONOO- simultaneously without mutual interference and generate the significant time-dependent fluorescence enhancement. By investigating the absorption and fluorescence properties of LG-3 towards HOCl and ONOO- individually and collectively, the result confirmed clearly that LG-3 has the capacity of monitoring the coexistence of HOCl and ONOO-, which could act as a two-input AND-based logic gate fluorescent probe.

Efficient inexact proximal gradient algorithms for structured sparsity-inducing norm.

Structured-sparsity regularization is popular for sparse learning because of its flexibility of encoding the feature structures. This paper considers a generalized version of structured-sparsity regularization (especially for l1∕l∞ norm) with arbitrary group overlap. Due to the group overlap, it is time-consuming to solve the associated proximal operator. Although Mairal et al. have proposed a network-flow algorithm to solve the proximal operator, it is still time-consuming, especially in the high-dimensional setting. To address this challenge, in this paper, we have developed a more efficient solution for l1∕l∞ group lasso with arbitrary group overlap using inexact proximal gradient method. In each iteration, our algorithm only requires to calculate an inexact solution to the proximal sub-problem, which can be done efficiently. On the theoretic side, the proposed algorithm enjoys the same global convergence rate as the exact proximal methods. Experiments demonstrate that our algorithm is much more efficient than the network-flow algorithm while retaining similar generalization performance.

An ultra-sensitive ratiometric fluorescent probe for hypochlorous acid detection by the synergistic effect of AIE and TBET and its application of detecting exogenous/endogenous HOCl in living cells.

An ultra-sensitive and ratiometric fluorescent probe for hypochlorous acid (HOCl) detection based on the mechanism of aggregation induced emission (AIE) and through-bond energy transfer (TBET) has been reported herein. By exploiting the advantages of AIE and TBET, which eliminates emission leakage from dark donors, the probe exhibits ultra-high sensitivity towards HOCl by an enhancement of over 7000-fold in the fluorescence ratio (I589 nm/I477 nm), which is one of the highest recorded so far. The reaction mechanism has been discussed in detail, and the effects of interferents and the reaction kinetics have also been investigated. Lastly, the successful result of exogenous/endogenous HOCl imaging detection in different cell lines indicates the potential use of the probe in living systems.

An ultra-sensitive and ratiometric fluorescent probe based on the DTBET process for Hg2+ detection and imaging applications.

In this article, we present an ultra-sensitive and ratiometric fluorescent probe (TR-Hg) for Hg2+ detection based on the mechanism of aggregation induced emission (AIE) and dark through-bond energy transfer (DTBET). The probe was constructed using tetraphenylethene as the dark donor and rhodamine B thiolactone as the acceptor. By exploiting the advantages of DTBET, which eliminates emission leakage from dark donors and provides nearly 100% energy transfer efficiency, TR-Hg exhibits more than a 30 000-fold fluorescence ratio enhancement after reacting with Hg2+. TR-Hg demostrates (i) a detection limit of 43 pM, which is the lowest among the reported ratiometric Hg2+ probes, (ii) excellent selectivity, fast response-time (<10 s) and a wide pH application range, (iii) strong applicability for paper-based colorimetric assay, where readout can be performed by the naked eye, and (iv) fluorescent imaging of Hg2+ in onion epidermal tissues, indicating its potential use in living organisms.

Ultra-sensitive fluorescent probes for hypochlorite acid detection and exogenous/endogenous imaging of living cells.

Two fluorescent probes (Naph-1 and Naph-2), which can be prepared via a facile process, have been developed to detect hypochlorite acid (HOCl). The N,N-dimethyl thiocarbamate group quenches the fluorescence of the probes through intramolecular charge transfer (ICT). Upon reaction with HOCl in aqueous buffer, Naph-1 shows ultra-high sensitivity towards HOCl through a 4600-fold increase in fluorescence intensity, as well as a detection limit of 2.37 nM. The probes have been applied to confocal fluorescence imaging of exogenous and endogenous HOCl in living cells.

A fast-response fluorescent probe for hypochlorous acid detection and its application in exogenous and endogenous HOCl imaging of living cells.

A facile fluorescent probe (NBD-DOP) has been developed to detect hypochlorous acid (HOCl) in this study. The probe consists of a NBD fluorophore and a dopamine moiety that reacts with HOCl specifically. The dopamine group quenches the fluorescence of NBD efficiently through a photoinduced electron transfer (PET) effect. Experimental data showed that NBD-DOP could detect HOCl with ultrafast response, high sensitivity and high selectivity in a wide pH range. The probe could also be used to detect the myeloperoxidase enzyme that produces HOCl. Moreover, NBD-DOP has been applied in the imaging of exogenous and endogenous HOCl in living cells by confocal fluorescence microscopy.

A reaction-based near-infrared fluorescent sensor for Cu2+ detection in aqueous buffer and its application in living cells and tissues imaging.

Copper (II) is one of the most of important cofactors for numerous enzymes and has captured broad attention due to its role as a neurotransmitters for physiological and pathological functions. In this article, we present a reaction-based fluorescent sensor for Cu2+ detection (NIR-Cu) with near-infrared excitation and emission, including probe design, structure characterization, optical property test and biological imaging application. NIR-Cu is equipped with a functional group, 2-picolinic ester, which hydrolyzes in the presence of Cu2+ with high selectivity over completed cations. With the experimental conditions optimized, NIR-Cu (5µM) exhibits linear response for Cu2+ range from 0.1 to 5µM, with a detection limit of 29nM. NIR-Cu also shows excellent water solubility and are highly responsive, both desirable properties for Cu2+ detection in water samples. In addition, due to its near-infrared excitation and emission properties, NIR-Cu demonstrates outstanding fluorescent imaging in living cells and tissues.

Accurate on-line ν-support vector learning.

The ν-Support Vector Machine (ν-SVM) for classification proposed by Schölkopf et al. has the advantage of using a parameter ν on controlling the number of support vectors and margin errors. However, comparing to standard C-Support Vector Machine (C-SVM), its formulation is more complicated, up until now there are no effective methods on solving accurate on-line learning for it. In this paper, we propose a new effective accurate on-line algorithm which is designed based on a modified formulation of the original ν-SVM. The accurate on-line algorithm includes two special steps: the first one is relaxed adiabatic incremental adjustments; the second one is strict restoration adjustments. The experiments on several benchmark datasets demonstrate that using these two steps the accurate on-line algorithm can avoid the infeasible updating path as far as possible, and successfully converge to the optimal solution. It achieves the fast convergence especially on the Gaussian kernel and is faster than the batch algorithm.

Regularization path for ν-support vector classification.

The v-support vector classification (v-SVC) proposed by Schölkopf has the advantage of using a regularization parameter v for controlling the number of support vectors and margin errors. However, compared to C-SVC, its formulation is more complicated, and to date there are no effective methods for computing its regularization path. In this paper, we propose a new regularization path algorithm, which is designed on the basis of a modified formulation of v-SVC and traces the solution path with respect to the parameter v. Through theoretical analysis and confirmatory experiments, we show that our algorithm can avoid the infeasible updating path under several assumptions (i.e., Assumptions 1 and 2), and fit the entire solution path in a finite number of steps. When the regularization path of v-SVC is available, a novel approach proposed by Yang and Ong can be applied to obtain the global optimal solution of common validation functions for v-SVC, and the computation for the whole process is minimal. Numerical experiments show that it is more efficient than various kinds of grid search methods for selecting the optimal regularization parameter v.