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Nucleic acid detection powered by CRISPR technology provides a rapid, sensitive, and deployable approach to molecular diagnostics. While exciting, there remain challenges limiting its practical applications, such as the need for pre-amplification and the lack of quantitative ability. Here, we develop an asymmetric CRISPR assay for cascade signal amplification detection of nucleic acids by leveraging the asymmetric trans -cleavage behavior of competitive crRNA. We discover that the competitive reaction between a full-sized crRNA and split crRNA for CRISPR-Cas12a can induce cascade signal amplification, significantly improving the target detection signal. In addition, we find that CRISPR-Cas12a can recognize fragmented RNA/DNA targets, enabling direct RNA detection by Cas12a. Based on these findings, we apply our asymmetric CRISPR assay to quantitatively detect microRNA without the need for pre-amplification, achieving a detection sensitivity of 856 aM. Moreover, using this method, we analyze and quantify miR-19a biomarker in plasma samples from bladder cancer patients. This asymmetric CRISPR assay has the potential to be widely applied for simple and sensitive nucleic acid detection in various diagnostic settings. New strategies are being developed to simplify CRISPR-based nucleic acid detection. By investigating the competitive reaction between a full-sized crRNA and split crRNA for CRISPR-Cas12a, the authors develop an asymmetric CRISPR assay for amplification-free, cascade signal amplification detection of nucleic acids.

As the COVID-19 pandemic continues, people are becoming infected at an alarming rate, individuals are unknowingly spreading disease, and more lives are lost every day. There is an immediate need for a simple, rapid, early and sensitive point-of-care testing for COVID-19 disease. However, current testing approaches do not meet such need. Recently, clustered regularly interspaced short palindromic repeats (CRISPR)-based detection methods have received substantial attention for nucleic acid-based molecular testing due to their simplicity, high sensitivity and specificity. This review explores the various CRISPR-based COVID-19 detection methods and related diagnostic devices. As with any emerging technology, CRISPR/Cas-based nucleic acid testing methods have several challenges that must be overcome for practical applications in clinics and hospitals. More importantly, these detection methods are not limited to COVID-19 but can be applied to detect any type of pathogen, virus, and fungi that may threaten humans, agriculture, and food industries in resource-limited settings. CRISPR/Cas-based detection methods have the potential to become simpler, more reliable, more affordable, and faster in the near future, which is highly important for achieving point-of-care diagnostics.

The recent outbreak of novel coronavirus (SARS-CoV-2) causing COVID-19 disease spreads rapidly in the world. Rapid and early detection of SARS-CoV-2 facilitates early intervention and prevents the disease spread. Here, we present an All-In-One Dual CRISPR-Cas12a (AIOD-CRISPR) assay for one-pot, ultrasensitive, and visual SARS-CoV-2 detection. By targeting SARS-CoV-2’s nucleoprotein gene, two CRISPR RNAs without protospacer adjacent motif (PAM) site limitation are introduced to develop the AIOD-CRISPR assay and detect the nucleic acids with a sensitivity of few copies. We validate the assay by using COVID-19 clinical swab samples and obtain consistent results with RT-PCR assay. Furthermore, a low-cost hand warmer (~$0.3) is used as an incubator of the AIOD-CRISPR assay to detect clinical samples within 20 min, enabling an instrument-free, visual SARS-CoV-2 detection at the point of care. Thus, our method has the significant potential to provide a rapid, sensitive, one-pot point-of-care assay for SARS-CoV-2. Rapid and early detection of SARS-CoV-2 will aid intervention to stop disease spread. Here the authors present a one-pot CRISPR-based rapid detection system with visual readout.

Despite the growing interest of archiving information in synthetic DNA to confront data explosion, quantitatively querying the data stored in DNA is still a challenge. Herein, we present Search Enabled by Enzymatic Keyword Recognition (SEEKER), which utilizes CRISPR-Cas12a to rapidly generate visible fluorescence when a DNA target corresponding to the keyword of interest is present. SEEKER achieves quantitative text searching since the growth rate of fluorescence intensity is proportional to keyword frequency. Compatible with SEEKER, we develop non-collision grouping coding, which reduces the size of dictionary and enables lossless compression without disrupting the original order of texts. Using four queries, we correctly identify keywords in 40 files with a background of ~8000 irrelevant terms. Parallel searching with SEEKER can be performed on a 3D-printed microfluidic chip. Overall, SEEKER provides a quantitative approach to conducting parallel searching over the complete content stored in DNA with simple implementation and rapid result generation. Targeting the files containing content-of-interest is a challenge in DNA data storage. Here, the authors develop a CRISPR-powered search engine to quantitatively identify the keyword in files stored in DNA.

The ignition and combustion process of lubricating oil leaking from an automotive engine onto a hot surface is a major cause of vehicle fires, and the geometric characteristics of the flame directly affect the spread and severity of the fire. Therefore, studying the ignition characteristics of lubricating oil on hot surfaces and quantifying flame behavior is of great significance for vehicle fire safety protection. This study utilizes a self-developed automotive hot surface ignition oil simulation platform, employing the SOBEL threshold segmentation algorithm combined with box-counting fractal dimension theory. It investigates the factors affecting the ignition delay time of automotive engine lubricating oil, the ignition risk and probability on engine hot surfaces, and analyzes the temporal evolution characteristics of the flame fractal dimension of engine lubricating oil. This research provides theoretical support for vehicle fire risk assessment and prevention. The main findings of this study are as follows: (1) As the temperature of the hot surface increases, the ignition delay time generally shows a decreasing trend, with 450°C being a critical turning point; (2) There is an overlap between ignition and non-ignition cases within a specific range, forming a possible ignition zone, and the R ² values of the fitting equations for the upper and lower boundaries are both above 95%, indicating a good fit. (3) The fractal dimension can effectively quantify the geometric complexity of the flame’s outer contour, thereby characterizing the stability of the flame’s combustion. The evolution of the fractal dimension of the lubricating oil droplet flame shows a trend of first increasing and then slowly decreasing. The interval from 0 to 1 second is the stable combustion phase, from 2 to 3 seconds is the unstable combustion phase, and from 3 to 5 seconds is the secondary stable combustion phase. During this period, the fractal dimension gradually decreases from the peak to around 1, and the flame’s outer contour transforms from complex to simple. (4) The volume of the droplet ( V ) affects both the peak value of the fractal dimension ( D max ) of the flame and the time at which it occurs ( t max ). The larger the volume, the earlier D max occurs. For a 0.1 ml droplet, D max occurs earliest ( t max = 1.98 s), while for a 0.5 ml droplet, D max appears the latest ( t max = 3.22 s). There is a significant correlation between t max and droplet volume V ( R = 0.995, P = 0.001). The spray hole size has a greater impact on D max compared to t max . With spray hole diameters ranging from 0.4 mm to 0.7 mm, the fractal dimensions of all droplet flames appear at around 2.6 seconds, but the values of D max vary significantly. As the spray hole diameter ( S ) decreases, D max approaches 2. When the spray hole diameter is 0.4 mm, D max is the highest, reaching 1.605, indicating the most drastic change in the geometric complexity of the flame’s outer contour and the least stable combustion process overall.

Entropy measures that assess signals' complexity have drawn increasing attention recently in biomedical field, as they have shown the ability of capturing unique features that are intrinsic and physiologically meaningful. In this study, we applied entropy analysis to electroencephalogram (EEG) data to examine its performance in epilepsy detection based on short-term EEG, aiming at establishing a short-term analysis protocol with optimal seizure detection performance. Two classification problems were considered, i.e., 1) classifying interictal and ictal EEGs (epileptic group) from normal EEGs; and 2) classifying ictal from interictal EEGs. For each problem, we explored two protocols to analyze the entropy of EEG: i) using a single analytical window with different window lengths, and ii) using an average of multiple windows for each window length. Two entropy methods-fuzzy entropy (FuzzyEn) and distribution entropy (DistEn)-were used that have valid outputs for any given data lengths. We performed feature selection and trained classifiers based on a cross-validation process. The results show that performance of FuzzyEn and DistEn may complement each other and the best performance can be achieved by combining: 1) FuzzyEn of one 5-s window and the averaged DistEn of five 1-s windows for classifying normal from epileptic group (accuracy: 0.93, sensitivity: 0.91, specificity: 0.96); and 2) the averaged FuzzyEn of five 1-s windows and DistEn of one 5-s window for classifying ictal from interictal EEGs (accuracy: 0.91, sensitivity: 0.93, specificity: 0.90). Further studies are warranted to examine whether this proposed short-term analysis procedure can help track the epileptic activities in real time and provide prompt feedback for clinical practices.

Complexity of heartbeat interval series is typically measured by entropy. Recent studies have found that sample entropy (SampEn) or fuzzy entropy (FuzzyEn) quantifies essentially the randomness, which may not be uniformly identical to complexity. Additionally, these entropy measures are heavily dependent on the predetermined parameters and confined to data length. Aiming at improving the robustness of complexity assessment for short-term RR interval series, this study developed a novel measure—distribution entropy (DistEn). The DistEn took full advantage of the inherent information underlying the vector-to-vector distances in the state space by probability density estimation. Performances of DistEn were examined by theoretical data and experimental short-term RR interval series. Results showed that DistEn correctly ranked the complexity of simulated chaotic series and Gaussian noise series. The DistEn had relatively lower sensitivity to the predetermined parameters and showed stability even for quantifying the complexity of extremely short series. Analysis further showed that the DistEn indicated the loss of complexity in both healthy aging and heart failure patients (both p  < 0.01), whereas neither the SampEn nor the FuzzyEn achieved comparable results (all p  ≥ 0.05). This study suggested that the DistEn would be a promising measure for prompt clinical examination of cardiovascular function.

Coronary artery disease (CAD) is an irreversible and fatal disease. It necessitates timely and precise diagnosis to slow CAD progression. Electrocardiogram (ECG) and phonocardiogram (PCG), conveying abundant disease-related information, are prevalent clinical techniques for early CAD diagnosis. Nevertheless, most previous methods have relied on single-modal data, restricting their diagnosis precision due to suffering from information shortages. To address this issue and capture adequate information, the development of a multi-modal method becomes imperative. In this study, a novel multi-modal learning method is proposed to integrate both ECG and PCG for CAD detection. Along with deconvolution operation, a novel ECG-PCG coupling signal is evaluated initially to enrich the diagnosis information. After constructing a modified recurrence plot, we build a parallel CNN network to encode multi-modal information, involving ECG, PCG and ECG-PCG coupling deep-coding features. To remove irrelevant information while preserving discriminative features, we add an autoencoder network to compress feature dimension. Final CAD classification is conducted by combining support vector machine and optimal multi-modal features. The experiment is validated on 199 simultaneously recorded ECG and PCG signals from non-CAD and CAD subjects, and achieves high performance with accuracy, sensitivity, specificity and f1-score of 98.49%, 98.57%,98.57% and 98.89%, respectively. The result demonstrates the superiority of the proposed multi-modal method in overcoming information shortages of single-modal signals and outperforming existing models in CAD detection. This study highlights the potential of multi-modal deep-coding information, and offers a wider insight to enhance CAD diagnosis.

Complex diseases often undergo abrupt transitions from pre-disease to disease states, with the pre-disease state is typically unstable but potentially reversible through timely intervention. Detecting these critical transitions is crucial. We propose a model-free method, Local Network Wasserstein Distance (LNWD), for identifying critical transitions/pre-disease states in complex diseases using single sample analysis. LNWD measures statistical perturbations in normal samples caused by diseased samples using the Wasserstein distance, and identifies critical states by observing LNWD score changes. Applied to KIRP, KIRC, LUAD, ESCA (TCGA datasets) and GSE2565, GSE13268 (GEO datasets), the method successfully identified critical states in six disease datasets. This single-sample, local network-based approach provides early warning signals for medical diagnosis and holds great potential for personalized disease diagnosis.

Patch-based medical image registration has been well explored in recent decades. However, the patch fusion process can generate grid-like artifacts along the edge of patches for the following two reasons: firstly, in order to ensure the same size of input and output, zero-padding is used, which causes uncertainty in the edges of the output feature map during the feature extraction process; secondly, the sliding window extraction patch with different strides will result in different degrees of grid-like artifacts. In this paper, we propose an exponential-distance-weighted (EDW) method to remove grid-like artifacts. To consider the uncertainty of predictions near patch edges, we used an exponential function to convert the distance from the point in the overlapping regions to the center point of the patch into a weighting coefficient. This gave lower weights to areas near the patch edges, to decrease the uncertainty predictions. Finally, the dense displacement field was obtained by this EDW weighting method. We used the OASIS-3 dataset to evaluate the performance of our method. The experimental results show that the proposed EDW patch fusion method removed grid-like artifacts and improved the dice similarity coefficient superior to those of several state-of-the-art methods. The proposed fusion method can be used together with any patch-based registration model.