SFGAE: a self-feature-based graph autoencoder model for miRNA-disease associations prediction.

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.

Exploring bridge symptoms in HIV-positive people with comorbid depressive and anxiety disorders.

BACKGROUND: The prevalence of comorbid depressive and anxiety disorders in people living with HIV (PLWH) is high. However, it is unclear which symptom is the bridge symptom between depression and anxiety in PLWH. This study aimed to develop symptom networks for depression and anxiety and explore the bridge symptoms and interconnectedness between these disorders in PLWH with comorbid depressive and anxiety disorders. METHODS: A multisite, hospital-based cross-sectional study was conducted from March 2020 to November 2021. Depression and anxiety were measured with the Hospital Anxiety and Depression Scale. We visualized the symptom network using the qgraph package and computed the bridge expected influence of each node. The GLASSO layout was used to generate undirected association networks. RESULTS: A total of 2016 individuals were included in the analysis. In the anxiety cluster, "not feeling relaxed" had the highest bridge expected influence and strength (rbridge expected influence = 0.628, rstrength = 0.903). In the depression cluster, "not feeling cheerful" was identified as having a high bridge expected influence (rbridge expected influence = 0.385). "Not feeling cheerful" and "not feeling relaxed" were the strongest edges across the depression and anxiety clusters (r = 0.30 ± 0.02). CONCLUSIONS: Healthcare professionals should take note when PLWH report severe bridge symptoms. To enhance the levels of perceived cheerfulness and relaxation, positive psychology interventions could be implemented.

Programmable and scalable assembly of a flexible hexagonal DNA origami.

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.

Predictors and Survival of Patients with Osteosarcoma After Limb Salvage versus Amputation: A Population-Based Analysis with Propensity Score Matching.

BACKGROUND: Conflicting findings have been reported concerning the survival of patients treated with limb salvage and amputation for osteosarcoma. This study aimed to identify predictors associated with surgery types and survival difference. METHODS: Patients with osteosarcoma were selected from the Surveillance Epidemiology and End Results database (1975-2016). Multivariable logistic regression analysis was conducted, and a nomogram was further established. Propensity score matching (PSM)-adjusted Kaplan-Meier curves, log-rank tests, Cox proportional hazards regression analysis were performed to compare overall survival (OS) and cancer-specific survival (CSS). RESULTS: Among 3363 patients with osteosarcoma, 2447 and 916 underwent limb salvage and amputation. Predictors associated with amputation in the nomogram included age, gender, primary tumor site, tumor grade, tumor stage, tumor size and radiotherapy. Totally 900 pairs of patients treated with limb salvage and amputation were matched after PSM. Limb salvage was significantly associated with improved OS (HR, 0.773; 95% CI, 0.670-0.892; p < 0.001) and CSS (HR, 0.795; 95% CI, 0.681-0.927; p = 0.003) in PSM-adjusted cohort after adjusting for related variables. The significant treatment effect of limb salvage was consistent within most subgroups. Among patients treated with surgery for osteosarcoma, age between 41 and 60, age ≥ 61, pelvis as the primary site, high tumor grade (III/IV), regional and distant tumor stage, tumor size ≥ 92 mm and radiotherapy were independent prognostic factors in PSM cohort. CONCLUSIONS: Limb salvage exhibits significant benefit on OS and CSS compared with amputation for osteosarcoma. Predicators and survival differences should be given full consideration for the treatment of osteosarcoma.

Size-controllable DNA nanoribbons assembled from three types of reusable brick single-strand DNA tiles.

Precise control of nanostructure is a significant goal shared by supramolecular chemistry, nanotechnology and materials science. In DNA nanotechnology, methods of constructing desired DNA nanostructures using programmable DNA strands have been studied extensively and have become a promising branch of research, but developing universal and low-cost (in the sense of using fewer types of DNA strands) methods remains a challenge. In this work, we propose a novel approach to assemble size-controllable DNA nanoribbons with three types of reusable brick SSTs (single-stranded DNA tiles), where the control of ribbon size is achieved by regulating the concentration ratio between manipulative strands and packed single-stranded DNA tiles. In our method, three types of brick SSTs are sufficient in assembling DNA nanoribbons of different sizes, which is much less than the number of types of unique tile-programmable assembling strategy, thus achieving a universal and low-cost method. The assembled DNA nanoribbons are observed and analyzed by atomic force microscopy (AFM). Experimental observations strongly suggest the feasibility and reliability of our method.

DNA Origami Nanostructure Detection and Yield Estimation Using Deep Learning.

DNA origami is a milestone in DNA nanotechnology. It is robust and efficient in constructing arbitrary two- and three-dimensional nanostructures. The shape and size of origami structures vary. To characterize them, an atomic force microscope, a transmission electron microscope, and other microscopes are utilized. However, the identification of various origami nanostructures heavily depends on the experience of researchers. In this study, we used the deep learning method (improved Yolox) to detect multiple DNA origami structures and estimate their yield. We designed a feature enhancement fusion network with the attention mechanism, and related parameters were researched. Experiments conducted to verify the proposed method showed that the detection accuracy was higher than that of other methods. This method can detect and estimate the DNA origami yield in complex environments, and the detection speed is in the millisecond range.

DNA strand displacement based computational systems and their applications.

DNA computing has become the focus of computing research due to its excellent parallel processing capability, data storage capacity, and low energy consumption characteristics. DNA computational units can be precisely programmed through the sequence specificity and base pair principle. Then, computational units can be cascaded and integrated to form large DNA computing systems. Among them, DNA strand displacement (DSD) is the simplest but most efficient method for constructing DNA computing systems. The inputs and outputs of DSD are signal strands that can be transferred to the next unit. DSD has been used to construct logic gates, integrated circuits, artificial neural networks, etc. This review introduced the recent development of DSD-based computational systems and their applications. Some DSD-related tools and issues are also discussed.

Rapid microRNA detection method based on DNA strand displacement for ovarian cancer cells.

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.

Towards long double-stranded chains and robust DNA-based data storage using the random code system.

DNA has become a popular choice for next-generation storage media due to its high storage density and stability. As the storage medium of life's information, DNA has significant storage capacity and low-cost, low-power replication and transcription capabilities. However, utilizing long double-stranded DNA for storage can introduce unstable factors that make it difficult to meet the constraints of biological systems. To address this challenge, we have designed a highly robust coding scheme called the "random code system," inspired by the idea of fountain codes. The random code system includes the establishment of a random matrix, Gaussian preprocessing, and random equilibrium. Compared to Luby transform codes (LT codes), random code (RC) has better robustness and recovery ability of lost information. In biological experiments, we successfully stored 29,390 bits of data in 25,700 bp chains, achieving a storage density of 1.78 bits per nucleotide. These results demonstrate the potential for using long double-stranded DNA and the random code system for robust DNA-based data storage.

DNA computing for gastric cancer analysis and functional classification.

Early identification of key biomarkers of malignant cancer is vital for patients' prognosis and therapies. There is research demonstrating that microRNAs are important biomarkers for cancer analysis. In this article, we used the DNA strand displacement mechanism (DSD) to construct the DNA computing system for cancer analysis. First, gene chips were obtained through bioinformatical training. These microRNA data and clinical traits were obtained from the Cancer Genome Atlas (TCGA) dataset. Second, we analyzed the expression data by using a weighted gene co-expression network (WGCNA) and found four biomarkers for two clinic features, respectively. Last, we constructed a DSD-based DNA computing system for cancer analysis. The inputs of the system are these identified biomarkers; the outputs are the fluorescent signals that represent their corresponding traits. The experiment and simulation results demonstrated the reliability of the DNA computing system. This DSD simulation system is lab-free but clinically meaningful. We expect this innovative method to be useful for rapid and accurate cancer diagnosis.

Graph Computation Using Algorithmic Self-Assembly of DNA Molecules.

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.

An Operational DNA Strand Displacement Encryption Approach.

DeoxyriboNucleic Acid (DNA) encryption is a new encryption method that appeared along with the research of DNA nanotechnology in recent years. Due to the complexity of biology in DNA nanotechnology, DNA encryption brings in an additional difficulty in deciphering and, thus, can enhance information security. As a new approach in DNA nanotechnology, DNA strand displacement has particular advantages such as being enzyme free and self-assembly. However, the existing research on DNA-strand-displacement-based encryption has mostly stayed at a theoretical or simulation stage. To this end, this paper proposes a new DNA-strand-displacement-based encryption framework. This encryption framework involves three main strategies. The first strategy was a tri-phase conversion from plaintext to DNA sequences according to a Huffman-coding-based transformation rule, which enhances the concealment of the information. The second strategy was the development of DNA strand displacement molecular modules, which produce the initial key for information encryption. The third strategy was a cyclic-shift-based operation to extend the initial key long enough, and thus increase the deciphering difficulty. The results of simulation and biological experiments demonstrated the feasibility of our scheme for encryption. The approach was further validated in terms of the key sensitivity, key space, and statistic characteristic. Our encryption framework provides a potential way to realize DNA-strand-displacement-based encryption via biological experiments and promotes the research on DNA-strand-displacement-based encryption.

Massively Parallel DNA Computing Based on Domino DNA Strand Displacement Logic Gates.

DNA computing has gained considerable attention due to the characteristics of high-density information storage and high parallel computing for solving computational problems. Building addressable logic gates with biomolecules is the basis for establishing biological computers. In the current calculation model, the multiinput AND operation often needs to be realized through a multilevel cascade between logic gates. Through experiments, it was found that the multilevel cascade causes signal leakage and affects the stability of the system. Using DNA strand displacement technology, we constructed a domino-like multiinput AND gate computing system instead of a cascade of operations, realizing multiinput AND computing on one logic gate and abandoning the traditional multilevel cascade of operations. Fluorescence experiments demonstrated that our methods significantly reduce system construction costs and improve the stability and robustness of the system. Finally, we proved stability and robustness of the domino AND gate by simulating the tic-tac-toe process with a massively parallel computing strategy.

DNA origami frame filled with two types of single-stranded tiles.

DNA origami and DNA single-stranded tiles (SSTs) are two basic motifs that are widely used in fabricating DNA nanostructures. Typically, DNA origami is self-folded via a long single phage strand (scaffold strand) and this process is aided by a myriad of short oligonucleotides (staple strand). Unlike DNA origami, SSTs construct nanostructures using many unique strands connected with each other to obtain specific shapes. These motifs are material- and labour-consuming, and require multiple different synthetic oligonucleotides, and DNA SSTs tend to remain kinetically trapped in the form of tubes. In this study, we present a new strategy that combines DNA origami with DNA SSTs to construct a DNA nanostructure with a predesigned shape. A rectangular DNA origami frame with ten dozen helper strands was filled with two types of SSTs assembled repeatedly, which avoided the kinetic trap and used fewer synthetic oligonucleotides. The assembly results were identified using atomic force microscopy. The experimental analysis demonstrated the stability and feasibility of the strategy.

Optical and Topological Characterization of Hexagonal DNA Origami Nanotags.

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 for topological identification. In this study, 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 the consistent nanoscale distance between morphological and fluorescent labels. Therefore, this fluorophore-embedded hexagonal origami platform can be used as a dual nanoruler for optical and topological calibration.

Multiform DNA origami arrays using minimal logic control.

Self-assembled DNA nanostructures significantly contribute to DNA nanotechnology. Algorithmic guiding of the assembly of DNA arrays remains a challenge in nanoarchitecture. Usually, the more sophisticated a DNA nanoarchitecture, the more DNA connections with specific sequences are required. This study aimed to investigate the feasibility of using the minimum pairs of DNA connection strands to implement algorithm-based self-assembly with finite DNA origamis. We found that the DNA origami linking complexity was markedly reduced. By rotating and turning the origami tile in different linking directions, we obtained 2 × 2 arrays of DNA origamis using a pair of DNA connections, 2 × 4 arrays using two pairs of DNA connections, and 4 × 4 arrays using three pairs of connection strands. We further analysed the effects of distortion on array formation. Overall, this study presents a hierarchical assembly strategy with minimal connections to generate multi-scale DNA arrays.

Adjusting Linking Strands to Form Size-Controllable DNA Origami Rings.

DNA origami is a powerful tool in nanotechnology that can be used to construct arbitrary structures for several nanoengineering applications. Generally, the more complex and sophisticated the construction, the greater is the number of origamis and connection strands that will be needed. Therefore, developing an effective and low-cost method for multiform DNA architecture is important in nanoengineering. Here, we adopted an oblique linking strategy to connect cross-shaped DNA origami with a controlled curing angle. The size of the DNA rings ranged from four blocks of approximately 200 nm to eleven blocks of c.a. 600 nm. We observed that the minimum size of the DNA ring structure was limited by the width of a single block. The largest rings were negatively affected by thermodynamic randomness, and thus, DNA rings consisting of more than eleven blocks were not observed. This strategy facilitates the generation of various DNA origami rings, whose size can be controlled by adjusting the length of the connection strands.