DNA or gene signal detection is of great significance in many fields including medical examination, intracellular molecular monitoring, and gene disease signal diagnosis, but detection of DNA or gene signals in a low concentration with instant visual results remains a challenge. In this work, a universal fast and visual colorimetric detection method for DNA signals is proposed. Specifically, a DNA signal amplification “circuit” based on DNA strand displacement is firstly designed to amplify the target DNA signals, and then thiol modified hairpin DNA strands and gold nanoparticles are used to make signal detection results visualized in a colorimetric manner. If the target DNA signal exists, the gold nanoparticles aggregate and settle down with color changing from dark red to grey quickly; otherwise, the gold nanoparticles’ colloids remain stable in dark red. The proposed method provides a novel way to detect quickly DNA or gene signals in low concentrations with instant visual results. When applied in real‐life, it may provide a universal colorimetric method for gene disease signal diagnosis.
Abstract Background: DNA origami can be applied as a “ruler” for nanoscale calibration or super-resolution fluorescence microscopy with an ideal structure for defining fluorophore arrangement, allowing the distance between fluorophores to be precisely controlled at the nanometer scale. DNA origami can also be used as a nanotag with arbitrary programmable shapes. Result: We formed a hexagonal origami structure embedded with three different fluorescent dyes on the surface. The distance between each fluorescent block was ~120 nm, which is below the diffraction limit of light, allowing for its application as a nano-ruler for super-resolution fluorescence microscopy. The outside edge of the hexagonal structure was redesigned to form three different substructures as topological labels. Atomic and scanning force microscopy demonstrated consistency of the nanoscale distance between morphological and fluorescent labels. Conclusion: We assembled the hexagonal origami platform and confirmed the fluorescent and topological lables, this fluorophore-embedded hexagonal origami platform can be used as a dual nano-ruler for both optical and topological calibration.
Since the discovery of the DNA Strand Displacement mechanism, researchers have implemented a lot of applications, such as DNA computing, DNA Circuits, Logic gates, and Chemical Reaction network.To achieve those functions, a well-designed system is essential, among which the toehold domains and migration domains play a vital role.In this paper, we designed three basic logic gates based on the toehold mediates DNA strand displacement mechanism, and utilized them to struct a three-layer multiplexer DNA logic circuit.However, the traditional INHIBIT gate annihilated all the inputs strands which obscure the multiplexer in further applications.Therefore, we improved the INHIBIT gate, so the desired input strand can be selected, and the corresponding output strand can be identified.Lastly, we adjusted the multiplexer and realized a cyclic DNA circuit.The simulation results verified the efficiency and reliability of our multiplexer DNA logic circuits.Our method has the ability in architecting complex DNA integrated circuits.
Learning noisy labels is a huge challenge. Noise inevitably occurs, and it takes considerable time and money to improve annotation accuracy. Due to the high capacity of the deep network, it will fit the noise labels sooner or later during the training process. Owing to the phenomenon of memory retention, the deep network will initiate its learning process by assimilating simpler patterns and progressively adapt to the complete dataset. Based on this property, we propose a novel approach called Collaborative Extreme Noise Classifier (CENC) to combat noise labels. CENC trains multiple independent deep convolutional neural networks simultaneously. It selects small-loss samples from each mini-batch and passes them on to other networks while updating its own parameters with samples that the remaining networks consider clean. Our approach proposes a new consensus function that ensures collaboration and mutual constraints among different networks, thereby making the number of transferable samples more reasonable. Our model is able to capture comprehensive information about the characteristics of clean samples, significantly improving the accuracy of the model in selecting clean samples. CENC is found to be an effective approach for training deep models with improved robustness based on noise experiments conducted on MNIST, CIFAR10 and CIFAR100.
In view of recent advancements in HIV prevention and care for men who have sex with men (MSM) during the COVID-19 outbreak, the study conducted a literature review to comprehend how restriction-related interventions to minimize Coronavirus illness effect sexual behavior change among MSM, access to HIV services, and mental health and clinical health outcomes among MSM. The findings indicate that MSM populations altered their sexual behavior throughout the pandemic, including a drop in the number of sexual partners and sexual activities, but an unmet requirement for condom use. HIV services were impacted during COVID-19, in addition to behavioral and structural interventions. Overburdened public health systems are forcing resources to be diverted to pandemic treatment, with plans to suspend related HIV services to reduce population infections. Additionally, the MSM's need for self-protection and obstacles to engaging in sexual relations are considerations. Poor psychological and clinical outcomes among MSM, particularly those with HIV, are dramatically worsened. The results also imply that telemedicine interventions should be implemented for MSM populations during this disease to address the burden of the services for HIV and continuity of care. Further investigation is needed to determine how sexual behavior, HIV treatment, and service interruptions affect MSM's psychological and physical health.
DNA molecules have been used as novel computing tools, by which Synthetic DNA was designed to execute computing processes with a programmable sequence. Here, we proposed a parallel computing method using DNA origamis as agents to solve the three-color problem, an example of the graph problem. Each agent was fabricated with a DNA origami of ∼50 nm diameter and contained DNA probes with programmable sticky ends that execute preset computing processes. With the interaction of different nanoagents, DNA molecules self-assemble into spatial nanostructures, which embody the computation results of the three-color problem with polynomial numbers of computing nanoagents in a one-pot annealing step. The computing results were confirmed by atomic force microscopy. Our method is completely different from existing DNA computing methods in its computing algorithm, and it has an advantage in terms of computational complexity and results detection for solving graph problems.
The current cancer detection methods are heavily dependent on the component analysis of corresponding cancer antigens. There is a lack of effective and simple clinical methods of ovarian cancer screening, which hinders the early identification for ovarian cancer and its treatment. To develop a simple and rapid method for quantitative analysis of ovarian cancer, we developed a DNA strand displacement-based method and finished the rapid detection of miR-21 in ovarian cancer cells within 5 min by a one-step isothermal reaction. The fluorescence intensity trajectory had a good linear relationship with miR-21 concentrations in the range of 100 fM-100 nM, with a lower limit of 6.05 pM. This detection method is simple, faster, and accurate. Besides, it can be applied to detect the miRNA biomarkers of other cancers by changing the preset sequences of toehold.
Nanoscale structures demonstrate considerable potential utility in the construction of nanorobots, nanomachines, and many other devices. In this study, a hexagonal DNA origami ring was assembled and visualized via atomic force microscopy. The DNA origami shape could be programmed into either a hexagonal or linear shape with an open or folded pattern. The flexible origami was robust and switchable for dynamic pattern recognition. Its edges were folded by six bundles of DNA helices, which could be opened or folded in a honeycomb shape. Additionally, the edges were programmed into a concave-convex pattern, which enabled linkage between the origami and dipolymers. Furthermore, biotin-streptavidin labels were embedded at each edge for nanoscale calibration. The atomic force microscopy results demonstrated the stability and high-yield of the flexible DNA origami ring. The polymorphous nanostructure is useful for dynamic nano-construction and calibration of structural probes or sensors.
Increasing evidence has suggested that microRNAs (miRNAs) are important biomarkers of various diseases. Numerous graph neural network (GNN) models have been proposed for predicting miRNA-disease associations. However, the existing GNN-based methods have over-smoothing issue-the learned feature embeddings of miRNA nodes and disease nodes are indistinguishable when stacking multiple GNN layers. This issue makes the performance of the methods sensitive to the number of layers, and significantly hurts the performance when more layers are employed. In this study, we resolve this issue by a novel self-feature-based graph autoencoder model, shortened as SFGAE. The key novelty of SFGAE is to construct miRNA-self embeddings and disease-self embeddings, and let them be independent of graph interactions between two types of nodes. The novel self-feature embeddings enrich the information of typical aggregated feature embeddings, which aggregate the information from direct neighbors and hence heavily rely on graph interactions. SFGAE adopts a graph encoder with attention mechanism to concatenate aggregated feature embeddings and self-feature embeddings, and adopts a bilinear decoder to predict links. Our experiments show that SFGAE achieves state-of-the-art performance. In particular, SFGAE improves the average AUC upon recent GAEMDA [1] on the benchmark datasets HMDD v2.0 and HMDD v3.2, and consistently performs better when less (e.g. 10%) training samples are used. Furthermore, SFGAE effectively overcomes the over-smoothing issue and performs stably well on deeper models (e.g. eight layers). Finally, we carry out case studies on three human diseases, colon neoplasms, esophageal neoplasms and kidney neoplasms, and perform a survival analysis using kidney neoplasm as an example. The results suggest that SFGAE is a reliable tool for predicting potential miRNA-disease associations.
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