Multiview representation learning for identification of novel cancer genes and their causative biological mechanisms.

Tumorigenesis arises from the dysfunction of cancer genes, leading to uncontrolled cell proliferation through various mechanisms. Establishing a complete cancer gene catalogue will make precision oncology possible. Although existing methods based on graph neural networks (GNN) are effective in identifying cancer genes, they fall short in effectively integrating data from multiple views and interpreting predictive outcomes. To address these shortcomings, an interpretable representation learning framework IMVRL-GCN is proposed to capture both shared and specific representations from multiview data, offering significant insights into the identification of cancer genes. Experimental results demonstrate that IMVRL-GCN outperforms state-of-the-art cancer gene identification methods and several baselines. Furthermore, IMVRL-GCN is employed to identify a total of 74 high-confidence novel cancer genes, and multiview data analysis highlights the pivotal roles of shared, mutation-specific, and structure-specific representations in discriminating distinctive cancer genes. Exploration of the mechanisms behind their discriminative capabilities suggests that shared representations are strongly associated with gene functions, while mutation-specific and structure-specific representations are linked to mutagenic propensity and functional synergy, respectively. Finally, our in-depth analyses of these candidates suggest potential insights for individualized treatments: afatinib could counteract many mutation-driven risks, and targeting interactions with cancer gene SRC is a reasonable strategy to mitigate interaction-induced risks for NR3C1, RXRA, HNF4A, and SP1.

Label-Free Raman Spectroscopy for Assessing Purity and Maturation of hiPSC-Derived Cardiac Tissue.

I. BACKGROUND: Human induced pluripotent stem cell (hiPSC) derived cardiomyocytes (CMs) have been utilized in drug toxicity evaluation, drug discovery, and treating heart failure patients, showing substantial effects. Ensuring the quality, purity, and maturation of hiPSC-CMs during large-scale production is crucial. There is a growing demand for a novel method to characterize cell molecular profiles without labels and without causing damage. II. METHODS: In this study, we employed label-free Raman microscopy to evaluate hiPSC-derived CMs. The study involved the characterization of cell molecular profiles without labels and without causing damage. The correlation between Raman spectroscopy of specific components, such as cytochrome c and myoglobin, and CM purity and maturation following hiPSC differentiation was investigated. Additionally, the validation of this correlation was performed by assessing mixtures of commercially available CMs (iCell cardiomyocytes2) and fibroblasts at various ratios as well as hiPSC-derived CMs with different efficiencies. Furthermore, CMs were matured using rapid pacing of traveling waves, and the Raman profiles of matured CMs were compared to those of immature ones. III. RESULTS: Raman spectroscopy indicated that the cytochrome c and myoglobin showed correlation with the purity and maturation of CMs following differentiation of hiPSCs. This correlation was validated through experiments involving different CM-fibroblast mixtures and hiPSC-derived CMs with varying efficiencies. Moreover, matured CMs exhibited markedly different Raman profiles compared to immature ones, indicating the potential of Raman imaging as a tool for assessing CM maturation. IV. CONCLUSIONS: We discovered that Raman spectroscopy of certain components, such as cytochrome c and myoglobin, correlates with the CM purity and maturation following hiPSC differentiation. The findings of this study highlight the potential of label-free Raman microscopy as a nondestructive, high-content, and time-efficient method for quality control of hiPSC-derived CMs. This approach could significantly contribute to ensuring the quality and maturity of hiPSC-CMs for various applications in drug discovery and regenerative medicine.

PHIAF: prediction of phage-host interactions with GAN-based data augmentation and sequence-based feature fusion.

Phage therapy has become one of the most promising alternatives to antibiotics in the treatment of bacterial diseases, and identifying phage-host interactions (PHIs) helps to understand the possible mechanism through which a phage infects bacteria to guide the development of phage therapy. Compared with wet experiments, computational methods of identifying PHIs can reduce costs and save time and are more effective and economic. In this paper, we propose a PHI prediction method with a generative adversarial network (GAN)-based data augmentation and sequence-based feature fusion (PHIAF). First, PHIAF applies a GAN-based data augmentation module, which generates pseudo PHIs to alleviate the data scarcity. Second, PHIAF fuses the features originated from DNA and protein sequences for better performance. Third, PHIAF utilizes an attention mechanism to consider different contributions of DNA/protein sequence-derived features, which also provides interpretability of the prediction model. In computational experiments, PHIAF outperforms other state-of-the-art PHI prediction methods when evaluated via 5-fold cross-validation (AUC and AUPR are 0.88 and 0.86, respectively). An ablation study shows that data augmentation, feature fusion and an attention mechanism are all beneficial to improve the prediction performance of PHIAF. Additionally, four new PHIs with the highest PHIAF score in the case study were verified by recent literature. In conclusion, PHIAF is a promising tool to accelerate the exploration of phage therapy.

SGNNMD: signed graph neural network for predicting deregulation types of miRNA-disease associations.

MiRNAs are a class of small non-coding RNA molecules that play an important role in many biological processes, and determining miRNA-disease associations can benefit drug development and clinical diagnosis. Although great efforts have been made to develop miRNA-disease association prediction methods, few attention has been paid to in-depth classification of miRNA-disease associations, e.g. up/down-regulation of miRNAs in diseases. In this paper, we regard known miRNA-disease associations as a signed bipartite network, which has miRNA nodes, disease nodes and two types of edges representing up/down-regulation of miRNAs in diseases, and propose a signed graph neural network method (SGNNMD) for predicting deregulation types of miRNA-disease associations. SGNNMD extracts subgraphs around miRNA-disease pairs from the signed bipartite network and learns structural features of subgraphs via a labeling algorithm and a neural network, and then combines them with biological features (i.e. miRNA-miRNA functional similarity and disease-disease semantic similarity) to build the prediction model. In the computational experiments, SGNNMD achieves highly competitive performance when compared with several baselines, including the signed graph link prediction methods, multi-relation prediction methods and one existing deregulation type prediction method. Moreover, SGNNMD has good inductive capability and can generalize to miRNAs/diseases unseen during the training.

DSEATM: drug set enrichment analysis uncovering disease mechanisms by biomedical text mining.

Disease pathogenesis is always a major topic in biomedical research. With the exponential growth of biomedical information, drug effect analysis for specific phenotypes has shown great promise in uncovering disease-associated pathways. However, this method has only been applied to a limited number of drugs. Here, we extracted the data of 4634 diseases, 3671 drugs, 112 809 disease-drug associations and 81 527 drug-gene associations by text mining of 29 168 919 publications. On this basis, we proposed a 'Drug Set Enrichment Analysis by Text Mining (DSEATM)' pipeline and applied it to 3250 diseases, which outperformed the state-of-the-art method. Furthermore, diseases pathways enriched by DSEATM were similar to those obtained using the TCGA cancer RNA-seq differentially expressed genes. In addition, the drug number, which showed a remarkable positive correlation of 0.73 with the AUC, plays a determining role in the performance of DSEATM. Taken together, DSEATM is an auspicious and accurate disease research tool that offers fresh insights.

Innovative biomarkers TCN2 and LY6E can significantly inhibit respiratory syncytial virus infection.

BACKGROUND: Respiratory syncytial virus (RSV) is a prominent etiological agent of lower respiratory tract infections in children, responsible for approximately 80% of cases of pediatric bronchiolitis and 50% of cases of infant pneumonia. Despite notable progress in the diagnosis and management of pediatric RSV infection, the current biomarkers for early-stage detection remain insufficient to meet clinical needs. Therefore, the development of more effective biomarkers for early-stage pediatric respiratory syncytial virus infection (EPR) is imperative. METHODS: The datasets used in this study were derived from the Gene Expression Omnibus (GEO) database. We used GSE188427 dataset as the training set to screen for biomarkers. Biomarkers of EPR were screened by Weighted Gene Co-expression Network Analysis (WGCNA), three machine-learning algorithms (LASSO regression, Random Forest, XGBoost), and other comprehensive bioinformatics analysis techniques. To evaluate the diagnostic value of these biomarkers, multiple external and internal datasets were employed as validation sets. Next, an examination was performed to investigate the relationship between the screened biomarkers and the infiltration of immune cells. Furthermore, an investigation was carried out to identify potential small molecule compounds that interact with selected diagnostic markers. Finally, we confirmed that the expression levels of the selected biomarkers exhibited a significant increase following RSV infection, and they were further identified as having antiviral properties. RESULTS: The study found that lymphocyte antigen 6E (LY6E) and Transcobalamin-2 (TCN2) are two biomarkers with diagnostic significance in EPR. Analysis of immune cell infiltration showed that they were associated with activation of multiple immune cells. Furthermore, our analysis demonstrated that small molecules, 3'-azido-3'-deoxythymine, methotrexate, and theophylline, have the potential to bind to TCN2 and exhibit antiviral properties. These compounds may serve as promising therapeutic agents for the management of pediatric RSV infections. Additionally, our data revealed an upregulation of LY6E and TCN2 expression in PBMCs from patients with RSV infection. ROC analysis indicated that LY6E and TCN2 possessed diagnostic value for RSV infection. Finally, we confirmed that LY6E and TCN2 expression increased after RSV infection and further inhibited RSV infection in A549 and BEAS-2B cell lines. Importantly, based on TCN2, our findings revealed the antiviral properties of a potentially efficacious compound, vitamin B12. CONCLUSION: LY6E and TCN2 are potential peripheral blood diagnostic biomarkers for pediatric RSV infection. LY6E and TCN2 inhibit RSV infection, indicating that LY6E and TCN2 are potential therapeutic target for RSV infection.

Modern software and physical education: can online training enhance gym training?

BACKGROUND: This study discusses the effectiveness of a 12-week intervention aimed at improving squat jump and sprint performance among second-year sports students. METHODS: The students were randomly divided into experimental (n = 89) and control (n = 92) groups. In addition to gym training, students of the experimental group also underwent online PE training. The students' performance in Squat Jumps, 30 m sprint, and Progressive Aerobic Cardiovascular Endurance Run (PACER), as well as their situational motivation, were assessed before and after the intervention. Furthermore, the students assessed their physical activity weekly using self-reports. RESULTS: The implementation of online training has positively impacted intrinsic and identified motivation, as well as external regulation; however, it was less effective in reducing amotivation compared to traditional gym-based training. CONCLUSIONS: The findings of the study contribute to the data synthesis on the expediency of using modern software in physical education.

Light Enables the Cathodic Interface Reaction Reversibility in Solid-State Lithium-Oxygen Batteries.

Limited triple-phase boundaries arising from the accumulation of solid discharge product(s) in solid-state cathodes (SSCs) pose a challenge to high-property solid-state lithium-oxygen batteries (SSLOBs). Light-assisted SSLOBs have been gradually explored as an ingenious system; however, the fundamental mechanisms of the SSCs interface behavior remain unclear. Here, we discovered that light assistance can enhance the fast inner-sphere charge transfer in SSCs and regulate the discharge products with spherical particles generated via the surface growth model. Moreover, the high photoelectron excitation and transportation capabilities of SSCs can retard cathodic catalytic decay by avoiding structural degradation of the cathode with a reduced charge voltage. The light-induced SSLOBs exhibited excellent stability (170 cycles) with a low discharge-charge polarization overpotential (0.27 V). Furthermore, transparent SSLOBs with exceptional flexibility, mechanical stability, and multiform shapes were fabricated for theory-to-practical applications in sunlight-induced batteries. Our study opens new opportunities for the introduction of solar energy into energy storage systems.

Ion-Dipole-Interaction-Induced Encapsulation of Free Residual Solvent for Long-Cycle Solid-State Lithium Metal Batteries.

Owing to high ionic conductivity and mechanical strength, poly(vinylidene fluoride) (PVDF) electrolytes have attracted increasing attention for solid-state lithium batteries, but highly reactive residual solvents severely plague cycling stability. Herein, we report a free-solvent-capturing strategy triggered by reinforced ion-dipole interactions between Li+ and residual solvent molecules. Lithium difluoro(oxalato)borate (LiDFOB) salt additive with electron-withdrawing capability serves as a redistributor of the Li+ electropositive state, which offers more binding sites for residual solvents. Benefiting from the modified coordination environment, the kinetically stable anion-derived interphases are preferentially formed, effectively mitigating the interfacial side reactions between the electrodes and electrolytes. As a result, the assembled solid-state battery shows a lifetime of over 2000 cycles with an average Coulombic efficiency of 99.9% and capacity retention of 80%. Our discovery sheds fresh light on the targeted regulation of the reactive residual solvent to extend the cycle life of solid-state batteries.

Multifocal Raman Spectrophotometer for Examining Drug-Induced and Chemical-Induced Cellular Changes in 3D Cell Spheroids.

Cell spheroids offer alternative in vitro cell models to monolayer cultured cells because they express complexities similar to those of in vivo tissues, such as cellular responses to drugs and chemicals. Raman spectroscopy emerged as a powerful analytical tool for detecting chemical changes in living cells because it nondestructively provides vibrational information regarding a target. Although multiple iterations are required in drug screening to determine drugs to treat cell spheroids and assess the inter-spheroid heterogeneity, current Raman applications used in spheroids analysis allow the observation of only a few spheroids owing to the low throughput of Raman spectroscopy. In this study, we developed a multifocal Raman spectrophotometer that enables simultaneous analysis of multiple spheroids in separate wells of a regular 96-well plate. By utilizing 96 focal spots excitation and parallel signal collection, our system can improve the throughput by approximately 2 orders of magnitude compared to a conventional single-focus Raman microscope. The Raman spectra of HeLa cell spheroids treated with anticancer drugs and HepG2 cell spheroids treated with free fatty acids were measured simultaneously, and concentration-dependent cellular responses were observed in both studies. Using the multifocal Raman spectrophotometer, we rapidly observed chemical changes in spheroids, and thus, this system can facilitate the application of Raman spectroscopy in analyzing the cellular responses of spheroids.

Label-Free Evaluation of Maturation and Hepatotoxicity of Human iPSC-Derived Hepatocytes Using Hyperspectral Raman Imaging.

To promote the clinical application of human induced pluripotent stem cell (hiPSC)-derived hepatocytes, a method capable of monitoring regenerative processes and assessing differentiation efficiency without harming or modifying these cells is important. Raman microscopy provides a powerful tool for this as it enables label-free identification of intracellular biomolecules in live samples. Here, we used label-free Raman microscopy to assess hiPSC differentiation into hepatocyte lineage based on the intracellular chemical content. We contrasted these data with similar phenotypes from the HepaRG and from commercially available hiPSC-derived hepatocytes (iCell hepatocytes). We detected hepatic cytochromes, lipids, and glycogen in hiPSC-derived hepatocyte-like cells (HLCs) but not biliary-like cells (BLCs), indicating intrinsic differences in biomolecular content between these phenotypes. The data show significant glycogen and lipid accumulation as early as the definitive endoderm transition. Additionally, we explored the use of Raman imaging as a hepatotoxicity assay for the HepaRG and iCell hepatocytes, with data displaying a dose-dependent reduction of glycogen accumulation in response to acetaminophen. These findings show that the nondestructive and high-content nature of Raman imaging provides a promising tool for both quality control of hiPSC-derived hepatocytes and hepatotoxicity screening.

Multi-way relation-enhanced hypergraph representation learning for anti-cancer drug synergy prediction.

MOTIVATION: Drug combinations have exhibited promise in treating cancers with less toxicity and fewer adverse reactions. However, in vitro screening of synergistic drug combinations is time-consuming and labor-intensive because of the combinatorial explosion. Although a number of computational methods have been developed for predicting synergistic drug combinations, the multi-way relations between drug combinations and cell lines existing in drug synergy data have not been well exploited. RESULTS: We propose a multi-way relation-enhanced hypergraph representation learning method to predict anti-cancer drug synergy, named HypergraphSynergy. HypergraphSynergy formulates synergistic drug combinations over cancer cell lines as a hypergraph, in which drugs and cell lines are represented by nodes and synergistic drug-drug-cell line triplets are represented by hyperedges, and leverages the biochemical features of drugs and cell lines as node attributes. Then, a hypergraph neural network is designed to learn the embeddings of drugs and cell lines from the hypergraph and predict drug synergy. Moreover, the auxiliary task of reconstructing the similarity networks of drugs and cell lines is considered to enhance the generalization ability of the model. In the computational experiments, HypergraphSynergy outperforms other state-of-the-art synergy prediction methods on two benchmark datasets for both classification and regression tasks and is applicable to unseen drug combinations or cell lines. The studies revealed that the hypergraph formulation allows us to capture and explain complex multi-way relations of drug combinations and cell lines, and also provides a flexible framework to make the best use of diverse information. AVAILABILITY AND IMPLEMENTATION: The source data and codes of HypergraphSynergy can be freely downloaded from https://github.com/liuxuan666/HypergraphSynergy. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Ultrasound-guided microwave ablation combined with ethanol injection for the treatment of solitary nodular retrosternal goiter: a prospective study of 72 patients.

OBJECTIVES: We prospectively evaluated the efficacy and safety of microwave ablation (MWA) combined with ethanol injection (EI) in solitary nodular retrosternal goiters (RSGs). METHODS: From November 2018 to November 2020, 72 patients diagnosed with solitary nodular RSG were treated by ultrasound-guided MWA with EI. Patients were followed up at 1, 3, 6, and 12 months and every 6-12 months thereafter by ultrasound and contrast-enhanced ultrasound (CEUS). The nodule volume, volume reduction ratio (VRR), neck circumference, symptom score, and cosmetic grading score were recorded to evaluate the treatment efficacy. RESULTS: All patients successfully underwent treatment. The mean initial nodule volume was 71.25 mL ± 61.61 mL, which decreased significantly to 7.47 mL ± 9.19 mL at a mean follow-up time of 23.89 months ± 7.66 months (range 15-39 months) with a mean VRR of 90.99% ± 7.52%. The neck circumference, symptom score, and cosmetic grading score significantly decreased from 36.94 cm ± 3.04 cm to 35.06 cm ± 2.84 cm, from 3.78 ± 1.19 to 0.36 ± 0.63, and from 3.42 ± 0.76 to 1.13 ± 0.37, at the 12 months after treatment, respectively (all 7 p < 0.001). Of all the nodules, eight (11.1%) received a second ablation. No major complications occurred. CONCLUSION: Ultrasound-guided MWA combined with EI is an effective and safe treatment for solitary nodular RSG and may be a potential alternative to surgery in selected patients, especially for those who are ineligible or unwilling to receive surgical treatment. KEY POINTS: • MWA combined with EI is an effective and safe approach for the treatment of solitary nodular RSG. • This treatment should be conducted by experienced physicians. • It provides a potential alternative to surgery for solitary nodular RSG in patients who are ineligible or unwilling to receive surgical treatment.

The preventive effect of PNF stretching exercise on sports injuries in physical education based on IoT data monitoring.

In physical education, in order to prevent sports injuries of students, it is necessary to study and build a set of physical education Internet of Things data monitoring training system to prevent sports injuries of students. This system is mainly composed of sensors, smart phones and cloud servers. Wearable devices equipped with sensors are used to complete data acquisition and transmission by means of the Internet of Things system, and relevant parameters are sorted and monitored by combining data analysis technology. The system makes a more in-depth, comprehensive and accurate analysis and processing of the collected data, so as to better evaluate the status and quality of students' sports, find out the existing problems in time, and put forward the corresponding solutions. By analyzing students' sports data and health data, the system generates personalized training programs, including training intensity, training time, training frequency and other parameters, so as to meet the needs and actual conditions of different students and avoid sports injuries caused by overtraining. This system can better analyze and process the collected data, provide teachers with more comprehensive and in-depth assessment and monitoring of students' sports status, and provide students with more personalized and scientific training programs, so as to effectively prevent the occurrence of students' sports injuries.

The Efficacy of REG Proteins as a Diagnostic Biomarker for Pancreatic Cancer: a Meta-Analysis.

BACKGROUND: Regenerating gene (REG) family proteins play a pivotal role in cell proliferation, tissue regeneration, and tumor metastasis. Recent studies have concentrated on the role of REG proteins in pancreatic cancer, but the results remain controversial. In this study, a meta-analysis was performed to evaluate the precise diagnostic value of REG proteins in pancreatic cancer. METHODS: A search was conducted in PubMed, Medline, Embase, Cochrane Library, Chinese National Knowledge Infrastructure (CNKI), Biomedical Literature Database (CBM), and WANFANG Data up to May 5, 2021. The QUADAS-2 tool was used to evaluate the quality of the included studies. The statistical analysis of the diagnostic tests was conducted using RevMan5 and Meta-Disc 1.4. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and their 95% confidence intervals (95% CIs) were calculated from each eligible study. RESULTS: The meta-analysis included 15 articles containing 796 patients and 584 controls. The pooled sensitivity was 0.71 (95% CI: 0.67 - 0.74), the pooled specificity was 0.73 (95% CI: 0.70 - 0.76), and the pooled DOR was 11.35 (95% CI: 5.92 - 21.77), respectively. The overall area under the receiver operating characteristic curve (AUC) was 0.84. Spearman's correlation coefficient was 0.34 (p = 0.221). For the subgroup analysis, the REG4 protein showed higher diagnostic accuracy compared with the other REG proteins. CONCLUSIONS: REG proteins have moderate diagnostic accuracy in pancreatic cancer. Further well-designed studies with larger sample sizes and clinical application are needed to validate the results of this meta-analysis.

Label-Free Monitoring of Drug-Induced Cytotoxicity and Its Molecular Fingerprint by Live-Cell Raman and Autofluorescence Imaging.

Simultaneous observation of drug distribution at the effector site and subsequent cell response are essential in the drug development process. However, few studies have visualized the drug itself and biomolecular interactions in living cells. Here, we used label-free Raman microscopy to investigate drug-induced cytotoxicity and visualize drug uptake and subcellular localization by its specific molecular fingerprint. A redox-sensitive Raman microscope detected the decrease of reduced cytochrome c (cyt c) after Actinomycin D (ActD) treatment in a time-dependent and dose-dependent format. Immunofluorescence staining of cyt c suggested that the release of cyt c was not the major cause. Combining Raman microscopy with conventional biological methods, we reported that the oxidization of cyt c is an early cytotoxicity marker prior to the release of cyt c. Moreover, as the spectral properties of ActD are sensitive to the surrounding environment, subcellular localization of ActD was visualized sensitively by the weak autofluorescence, and the intercalation of ActD into DNA was detected by shifted Raman peaks, allowing for parallel observation of drug uptake and the mechanism of action. In this research, we achieved simultaneous observation of cytotoxicity and cellular drug uptake by Raman microscopy, which could facilitate a precise understanding of pharmacological effects and predict potential drug toxicity in the future.

Comparison and integration of computational methods for deleterious synonymous mutation prediction.

Synonymous mutations do not change the encoded amino acids but may alter the structure or function of an mRNA in ways that impact gene function. Advances in next generation sequencing technologies have detected numerous synonymous mutations in the human genome. Several computational models have been proposed to predict deleterious synonymous mutations, which have greatly facilitated the development of this important field. Consequently, there is an urgent need to assess the state-of-the-art computational methods for deleterious synonymous mutation prediction to further advance the existing methodologies and to improve performance. In this regard, we systematically compared a total of 10 computational methods (including specific method for deleterious synonymous mutation and general method for single nucleotide mutation) in terms of the algorithms used, calculated features, performance evaluation and software usability. In addition, we constructed two carefully curated independent test datasets and accordingly assessed the robustness and scalability of these different computational methods for the identification of deleterious synonymous mutations. In an effort to improve predictive performance, we established an ensemble model, named Prediction of Deleterious Synonymous Mutation (PrDSM), which averages the ratings generated by the three most accurate predictors. Our benchmark tests demonstrated that the ensemble model PrDSM outperformed the reviewed tools for the prediction of deleterious synonymous mutations. Using the ensemble model, we developed an accessible online predictor, PrDSM, available at http://bioinfo.ahu.edu.cn:8080/PrDSM/. We hope that this comprehensive survey and the proposed strategy for building more accurate models can serve as a useful guide for inspiring future developments of computational methods for deleterious synonymous mutation prediction.

CircARID1A regulates mouse skeletal muscle regeneration by functioning as a sponge of miR-6368.

The noncoding RNAs play important role in growth and development of mammalian skeletal muscle. Recent work has shown that circRNAs are abundant in skeletal muscle tissue, with significant changes in their expression patterns during muscle development and aging. We identified a novel circRNA called circARID1A that is highly expressed in mice skeletal muscle compare to its linear transcript. Experiments shown that circARID1A significantly inhibited the process of C2C12 cell proliferation and promoted its differentiation. Interactions between circRNA and miRNA were screened by miRNA gene chip sequencing. The results indicated that circARID1A can sponge miR-6368, which was further verified by miRNA sensor and other experiments. Besides, miR-6368 is a commonly expressed miRNA that regulates the expression of several target genes including Tlr4. A mouse model of skeletal muscle injury was successfully established to explore the role of circARID1A in skeletal muscle development and regeneration in vivo. Moreover, we found the overexpression of circARID1A significantly promoted the regeneration of skeletal muscle. The results of our study suggest that circARID1A may regulate skeletal muscle cell development and regeneration by sponging miR-6368.

First-principles study of point defects in U3Si2: effects on the mechanical and electronic properties.

In recent years, U3Si2 has been proposed as an alternative nuclear fuel material to uranium dioxide (UO2) because of its intrinsically high uranium density and thermal conductivity. However, the operation environment in the nuclear reactor is complex and extreme, such as in-pile neutron irradiation, and thus it is necessary to explore the radiation response behavior of U3Si2 and the physical properties of its damaged states. In the present study, first-principles calculations based on density functional theory were carried out to investigate the mechanical and electronic properties of defective U3Si2. Our results showed that the defect stability in U3Si2, except its interstitial defects, is dependent on its chemical environment. When vacancy, antisite or interstitial defects are introduced into U3Si2, its elastic modulus are decreased and its ductility is enhanced. Although the presence of defects in U3Si2 does not change its metallic nature and the electron distribution in its Fermi level, their effect on the partial chemical bonding interaction is significant. This study suggests that under a radiation environment, the created defects in U3Si2 remarkably affect its mechanical and electronic properties.

Nanomaterials-based electrochemical sensors for the detection of natural antioxidants in food and biological samples: research progress.

Antioxidants are healthy substances that are beneficial to the human body and exist mainly in natural and synthetic forms. Among many kinds of antioxidants, the natural antioxidants have great applications in many fields such as food chemistry, medical care, and clinical application. In recent years, many efforts have been made for the determination of natural antioxidants. Nano-electrochemical sensors combining electrochemistry and nanotechnology have been widely used in the determination of natural antioxidants due to their unique advantages. Therefore, a large number of nanomaterials such as metal oxide, carbon materials, and conducting polymer have attracted much attention in the field of electrochemical sensors due to their good catalytic effect and stable performance. This review mainly introduces the construction of electrochemical sensors based on different nanomaterials, such as metallic nanomaterials, metal oxide nanomaterials, carbon nanomaterials, metal-organic frameworks, polymer nanomaterials, and other nanocomposites, and their application to the detection of natural antioxidants, including ascorbic acid, phenolic acids, flavonoid, tryptophan, citric acid, and other natural antioxidants. In the end, the limitations of the existing nano-sensing technology, the latest development trend, and the application prospect for various natural antioxidant substances are summarized and analyzed. We expect that this review will be helpful to researchers engaged in electrochemical sensors.