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Single-cell clustering is a critical step in biological downstream analysis. The clustering performance could be effectively improved by extracting cell-type-specific genes. The state-of-the-art feature selection methods usually calculate the importance of a single gene without considering the information contained in the gene expression distribution. Moreover, these methods ignore the intrinsic expression patterns of genes and heterogeneity within groups of different mean expression levels. In this work, we present a Feature sElection method based on gene Expression Decomposition (FEED) of scRNA-seq data, which selects informative genes to enhance clustering performance. First, the expression levels of genes are decomposed into multiple Gaussian components. Then, a novel gene correlation calculation method is proposed to measure the relationship between genes from the perspective of distribution. Finally, a permutation-based approach is proposed to determine the threshold of gene importance to obtain marker gene subsets. Compared with state-of-the-art feature selection methods, applying FEED on various scRNA-seq datasets including large datasets followed by different common clustering algorithms results in significant improvements in the accuracy of cell-type identification. The source codes for FEED are freely available at https://github.com/genemine/FEED.
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Perfilación de la Expresión Génica , Análisis de la Célula Individual , Perfilación de la Expresión Génica/métodos , Análisis de Secuencia de ARN/métodos , Análisis de la Célula Individual/métodos , Algoritmos , Análisis por Conglomerados , Expresión GénicaRESUMEN
Alternative splicing (AS) is a key transcriptional regulation pathway. Recent studies have shown that AS events are associated with the occurrence of complex diseases. Various computational approaches have been developed for the detection of disease-associated AS events. In this review, we first describe the metrics used for quantitative characterization of AS events. Second, we review and discuss the three types of methods for detecting disease-associated splicing events, which are differential splicing analysis, aberrant splicing detection and splicing-related network analysis. Third, to further exploit the genetic mechanism of disease-associated AS events, we describe the methods for detecting genetic variants that potentially regulate splicing. For each type of methods, we conducted experimental comparison to illustrate their performance. Finally, we discuss the limitations of these methods and point out potential ways to address them. We anticipate that this review provides a systematic understanding of computational approaches for the analysis of disease-associated splicing.
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Empalme Alternativo , Biología ComputacionalRESUMEN
MOTIVATION: Identifying cancer genes remains a significant challenge in cancer genomics research. Annotated gene sets encode functional associations among multiple genes, and cancer genes have been shown to cluster in hallmark signaling pathways and biological processes. The knowledge of annotated gene sets is critical for discovering cancer genes but remains to be fully exploited. RESULTS: Here, we present the DIsease-Specific Hypergraph neural network (DISHyper), a hypergraph-based computational method that integrates the knowledge from multiple types of annotated gene sets to predict cancer genes. First, our benchmark results demonstrate that DISHyper outperforms the existing state-of-the-art methods and highlight the advantages of employing hypergraphs for representing annotated gene sets. Second, we validate the accuracy of DISHyper-predicted cancer genes using functional validation results and multiple independent functional genomics data. Third, our model predicts 44 novel cancer genes, and subsequent analysis shows their significant associations with multiple types of cancers. Overall, our study provides a new perspective for discovering cancer genes and reveals previously undiscovered cancer genes. AVAILABILITY AND IMPLEMENTATION: DISHyper is freely available for download at https://github.com/genemine/DISHyper.
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Neoplasias , Redes Neurales de la Computación , Humanos , Neoplasias/genética , Biología Computacional/métodos , Genómica/métodos , Genes Relacionados con las Neoplasias , Anotación de Secuencia Molecular/métodos , Bases de Datos GenéticasRESUMEN
Dual-ion batteries (DIBs) are becoming an important technology for energy storage. To overcome the disadvantages of traditional electrodes and electrolytes, here we assemble a dual-carbon DIB with nanodiamond (ND)-modified crimped graphene (DCG) and electrolyte. The DCG anode and cathode realize high capacities of 1121 mA h g-1 and 149 mA h g-1, respectively, at 0.1 A g-1. The corresponding DCG//DCG full cells present a high capacity of 143 mA h g-1 at 1 A g-1 after 3300 cycles, which is superior to most reported results. Achieving these record performances is strongly dependent on the formed DCG electrodes with expanded interlayer spacing and abundant active sites, and NDs dispersed in DCG and electrolytes are very helpful for enhancing the storage of both cations and anions, effectively suppressing the irreversible decomposition of electrolytes. This work breaks through the bottleneck of graphitic-based DIBs, paving the way for realizing high-performance DIBs applied in industry.
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PURPOSE: Serum microRNA (miRNA) holds great potential as a non-invasive biomarker for diagnosing breast cancer (BrC). However, most diagnostic models rely on the absolute expression levels of miRNAs, which are susceptible to batch effects and challenging for clinical transformation. Furthermore, current studies on liquid biopsy diagnostic biomarkers for BrC mainly focus on distinguishing BrC patients from healthy controls, needing more specificity assessment. METHODS: We collected a large number of miRNA expression data involving 8465 samples from GEO, including 13 different cancer types and non-cancer controls. Based on the relative expression orderings (REOs) of miRNAs within each sample, we applied the greedy, LASSO multiple linear regression, and random forest algorithms to identify a qualitative biomarker specific to BrC by comparing BrC samples to samples of other cancers as controls. RESULTS: We developed a BrC-specific biomarker called 7-miRPairs, consisting of seven miRNA pairs. It demonstrated comparable classification performance in our analyzed machine learning algorithms while requiring fewer miRNA pairs, accurately distinguishing BrC from 12 other cancer types. The diagnostic performance of 7-miRPairs was favorable in the training set (accuracy = 98.47%, specificity = 98.14%, sensitivity = 99.25%), and similar results were obtained in the test set (accuracy = 97.22%, specificity = 96.87%, sensitivity = 98.02%). KEGG pathway enrichment analysis of the 11 miRNAs within the 7-miRPairs revealed significant enrichment of target mRNAs in pathways associated with BrC. CONCLUSION: Our study provides evidence that utilizing serum miRNA pairs can offer significant advantages for BrC-specific diagnosis in clinical practice by directly comparing serum samples with BrC to other cancer types.
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Neoplasias de la Mama , MicroARNs , Humanos , Femenino , MicroARNs/genética , Neoplasias de la Mama/diagnóstico , Neoplasias de la Mama/genética , Neoplasias de la Mama/patología , Perfilación de la Expresión Génica , Biomarcadores de Tumor/genética , Biopsia LíquidaRESUMEN
Practical applications of the hydrogen evolution reaction (HER) rely on the development of highly efficient, stable, and low-cost catalysts. Tuning the electronic structure, morphology, and architecture of catalysts is an important way to realize efficient and stable HER electrocatalysts. Herein, Co-doped Cu3P-based sugar-gourd structures (CoâCu3P/CF) are prepared on copper foam as active electrocatalysts for hydrogen evolution. This hierarchical structure facilitates fast mass transport during electrocatalysis. Notably, the introduction of Co not only induces a charge redistribution but also leads to lattice-mismatch on the atomic scale, which creates defects and performs as additional active sites. Therefore, CoâCu3P/CF requires an overpotential of only 81, 111, 185, and 230 mV to reach currents of 50, 100, 500, and 1000 mA cm-2 in alkaline media and remains stable after 10 000 CV cycles in a row and up to 110 h i-t stability tests. In addition, it also shows excellent HER performance in water/seawater electrolytes of different pH values. Experimental and DFT show that the introduction of Co modulates the electronic and energy level structures of the catalyst, optimizes the adsorption and desorption behavior of the intermediate, reduces the water dissociation energy barrier during the reaction, accelerates the Volmer step reaction, and thus improves the HER performance.
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High entropy materials offer almost unlimited catalytic possibilities due to their variable composition, unique structure, and excellent electrocatalytic performance. However, due to the strong tendency of nanoparticles to coarsen and agglomerate, it is still a challenge to synthesize nanoparticles using simple methods to precisely control the morphology and size of the nanoparticles in large quantities, and their large-scale application is limited by high costs and low yields. Herein, a series of high-entropy oxides (HEOs) nanoparticles with high-density and ultrasmall size (<5 nm) loaded on carbon nanosheets with large quantities are prepared by Joule-heating treatment of gel precursors in a short period of time (≈60 s). Among them, the prepared (FeCoNiRuMn)3O4-x catalyst shows the best electrocatalytic activity for oxygen evolution reaction, with low overpotentials (230 mV @10 mA cm-2, 270 mV @100 mA cm-2), small Tafel slope (39.4 mV dec-1), and excellent stability without significant decay at 100 mA cm-2 after 100 h. The excellent performance of (FeCoNiRuMn)3O4-x can be attributed to the synergistic effect of multiple elements and the inherent structural stability of high entropy systems. This study provides a more comprehensive design idea for the preparation of efficient and stable high entropy catalysts.
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Superhydrophobic surfaces are of great interest because of their remarkable properties. Due to its maximal hardness and chemical inertness, diamond film has great potential in fabricating robust superhydrophobic surfaces. In the present study, an oxygen-terminated polycrystalline boron-doped diamond (O-PBDD) superhydrophobic surface with micro/nano-hierarchical porous structures is developed. The preparation method is very simple, requiring only sputtering and dewetting procedures. The former involves sputtering gold and copper particles onto the hydrogen-terminated polycrystalline boron-doped diamond (H-PBDD) to form gold/copper films, whereas the latter involves placing the samples in an atmospheric tube furnace to form hierarchical pores. By controlling the etching parameters, the wettability of the O-PBDD surface can be adjusted from hydrophilic to superhydrophobic, which is significantly different to the normal hydrophilicity feature of O-termination diamonds. The water contact angle of the obtained O-PBDD surface can reach 165 ± 5°, which is higher than the superhydrophobic diamond surfaces that are reported in the literature. In addition, the O-PBDD surface exhibits excellent durability; it can maintain satisfactory superhydrophobicity even after high-pressure, high-temperature, and sandpaper friction tests. This work provides a new research direction for fabricating robust superhydrophobic materials with diamond film.
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The developed strategies for synthesizing metal phosphides are usually cumbersome and pollute the environment. In this work, an ultrafast (30 s) quasi-solid-state microwave approach is developed to construct cobalt-molybdenum phosphide decorated with Ru (Ru/CoxP-MoP) featured porous morphology with interconnected channels. The specific nanostructure favors mass transport, such as electrolyte bubbles transfer and exposing rich active sites. Moreover, the coupling effects between metallic elements, especially the decorated Ru, also act as a pivotal role on enhancing the electrocatalytic performance. Benefiting from the effects of composition and specific nanostructure, the prepared Ru/CoxP-MoP exhibits efficient HER performance with a current density of 10 mA cm-2 achieved in 1 m KOH, 0.5 m H2SO4, seawater containing 1 m KOH and 1 m PBS, with overpotentials of 52, 59, 55, 90 mV, and coupling with good stability. This work opens a novel and fast avenue to design metal-phosphide-based nanomaterials in energy-conversion and storage fields.
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Alzheimer's disease (AD) has a strong genetic predisposition. However, its risk genes remain incompletely identified. We developed an Alzheimer's brain gene network-based approach to predict AD-associated genes by leveraging the functional pattern of known AD-associated genes. Our constructed network outperformed existing networks in predicting AD genes. We then systematically validated the predictions using independent genetic, transcriptomic, proteomic data, neuropathological and clinical data. First, top-ranked genes were enriched in AD-associated pathways. Second, using external gene expression data from the Mount Sinai Brain Bank study, we found that the top-ranked genes were significantly associated with neuropathological and clinical traits, including the Consortium to Establish a Registry for Alzheimer's Disease score, Braak stage score and clinical dementia rating. The analysis of Alzheimer's brain single-cell RNA-seq data revealed cell-type-specific association of predicted genes with early pathology of AD. Third, by interrogating proteomic data in the Religious Orders Study and Memory and Aging Project and Baltimore Longitudinal Study of Aging studies, we observed a significant association of protein expression level with cognitive function and AD clinical severity. The network, method and predictions could become a valuable resource to advance the identification of risk genes for AD.
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Enfermedad de Alzheimer/genética , Enfermedad de Alzheimer/metabolismo , Encéfalo/metabolismo , Redes Reguladoras de Genes , Predisposición Genética a la Enfermedad , Envejecimiento/genética , Perfilación de la Expresión Génica , Humanos , Estudios Longitudinales , Memoria , Proteómica , RNA-Seq , TranscriptomaRESUMEN
MOTIVATION: A single gene may yield several isoforms with different functions through alternative splicing. Continuous efforts are devoted to developing machine-learning methods to predict isoform functions. However, existing methods do not consider the relevance of each feature to specific functions and ignore the noise caused by the irrelevant features. In this case, we hypothesize that constructing a feature selection framework to extract the function-relevant features might help improve the model accuracy in isoform function prediction. RESULTS: In this article, we present a feature selection-based approach named IsoFrog to predict isoform functions. First, IsoFrog adopts a reversible jump Markov Chain Monte Carlo (RJMCMC)-based feature selection framework to assess the feature importance to gene functions. Second, a sequential feature selection procedure is applied to select a subset of function-relevant features. This strategy screens the relevant features for the specific function while eliminating irrelevant ones, improving the effectiveness of the input features. Then, the selected features are input into our proposed method modified domain-invariant partial least squares, which prioritizes the most likely positive isoform for each positive MIG and utilizes diPLS for isoform function prediction. Tested on three datasets, our method achieves superior performance over six state-of-the-art methods, and the RJMCMC-based feature selection framework outperforms three classic feature selection methods. We expect this proposed methodology will promote the identification of isoform functions and further inspire the development of new methods. AVAILABILITY AND IMPLEMENTATION: IsoFrog is freely available at https://github.com/genemine/IsoFrog.
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Empalme Alternativo , Aprendizaje Automático , Cadenas de Markov , Isoformas de Proteínas , Método de MontecarloRESUMEN
MOTIVATION: Single-cell RNA sequencing (scRNA-seq) offers a powerful tool to dissect the complexity of biological tissues through cell sub-population identification in combination with clustering approaches. Feature selection is a critical step for improving the accuracy and interpretability of single-cell clustering. Existing feature selection methods underutilize the discriminatory potential of genes across distinct cell types. We hypothesize that incorporating such information could further boost the performance of single cell clustering. RESULTS: We develop CellBRF, a feature selection method that considers genes' relevance to cell types for single-cell clustering. The key idea is to identify genes that are most important for discriminating cell types through random forests guided by predicted cell labels. Moreover, it proposes a class balancing strategy to mitigate the impact of unbalanced cell type distributions on feature importance evaluation. We benchmark CellBRF on 33 scRNA-seq datasets representing diverse biological scenarios and demonstrate that it substantially outperforms state-of-the-art feature selection methods in terms of clustering accuracy and cell neighborhood consistency. Furthermore, we demonstrate the outstanding performance of our selected features through three case studies on cell differentiation stage identification, non-malignant cell subtype identification, and rare cell identification. CellBRF provides a new and effective tool to boost single-cell clustering accuracy. AVAILABILITY AND IMPLEMENTATION: All source codes of CellBRF are freely available at https://github.com/xuyp-csu/CellBRF.
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Benchmarking , Bosques Aleatorios , Diferenciación Celular , Análisis por ConglomeradosRESUMEN
OBJECTIVES: Idiopathic inflammatory myopathies (IIMs) are a group of heterogeneous autoimmune diseases. Intron retention (IR) serves as an important post-transcriptional and translational regulatory mechanism. This study aims to identify changes in IR profiles in IIM subtypes, investigating their influence on proteins and their correlations with clinical features. METHODS: RNA sequencing and liquid chromatography-tandem mass spectrometry were performed on muscle tissues obtained from 174 patients with IIM and 19 controls, following QC procedures. GTFtools and iREAD software were used for IR identification. An analysis of differentially expressed IRs (DEIs), exons and proteins was carried out using edgeR or DEP. Functional analysis was performed with clusterProfiler, and SPIRON was used to assess splicing factors. RESULTS: A total of 6783 IRs located in 3111 unique genes were identified in all IIM subtypes compared with controls. IIM subtype-specific DEIs were associated with the pathogenesis of respective IIM subtypes. Splicing factors YBX1 and HSPA2 exhibited the most changes in dermatomyositis and immune-mediated necrotising myopathy. Increased IR was associated with reduced protein expression. Some of the IIM-specific DEIs were correlated with clinical parameters (skin rash, MMT-8 scores and muscle enzymes) and muscle histopathological features (myofiber necrosis, regeneration and inflammation). IRs in IFIH1 and TRIM21 were strongly correlated with anti-MDA5+ antibody, while IRs in SRP14 were associated with anti-SRP+ antibody. CONCLUSION: This study revealed distinct IRs and specific splicing factors associated with IIM subtypes, which might be contributing to the pathogenesis of IIM. We also emphasised the potential impact of IR on protein expression in IIM muscles.
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Intrones , Músculo Esquelético , Miositis , Humanos , Miositis/genética , Miositis/inmunología , Miositis/patología , Masculino , Femenino , Músculo Esquelético/patología , Músculo Esquelético/metabolismo , Persona de Mediana Edad , Intrones/genética , Adulto , Dermatomiositis/genética , Dermatomiositis/patología , Dermatomiositis/metabolismo , Dermatomiositis/inmunología , Estudios de Casos y Controles , Anciano , Análisis de Secuencia de ARNRESUMEN
The toxic gases emitted from industrial production have caused significant damage to the environment and human health, necessitating efficient gas sensors for their detection and removal. In this work, first-principles calculations are employed to investigate the potential application of diamanes for high-performance toxic gas sensors. The results show that nine gas molecules (CO, CO2, NO, NO2, NH3, SO2, N2, O2, and H2O) are physisorbed on pristine diamane by weak van der Waals interactions. After introducing H/F defects, diamane can effectively capture specific toxic gases (CO, NO, NO2, and SO2) in the presence of interfering gases (N2, O2, and H2O), suggesting excellent selectivity and anti-interference ability. Orbital hybridization and significant charge redistribution between gas molecules and defective diamane dominate the enhanced adsorbate-substrate interactions. More importantly, the high sensitivity and good reversibility of defective diamane for detecting CO, NO, and SO2 molecules enable its reuse as a superior resistance-type gas sensor. Our calculations provide valuable insights into the potential of defective diamane for detecting toxic gases and shed light on the practical application of novel carbon-based materials in the gas-sensing field.
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Transition-metal phosphides (TMPs) have attracted extensive attention in energy-related fields, especially for electrocatalytic hydrogen evolution reaction (HER). However, it is imperative to develop a facile and time-consuming approach to prepare metal phosphides with satisfactory catalytic performance. Herein, nitrogen-doped CoP-Co2P decorated with Ru (Ru/N-CoP-Co2P) is synthesized (Ru/N-CoP-Co2P) through a hydrothermal route and following an ultrafast and simple microwave avenue within 20 s. The achieved Ru/N-CoP-Co2P possesses an interconnected porous morphology to expose abundant active sites and accelerate the mass transport. Moreover, N doping and Ru-supported decorated Ru/N-CoP-Co2P also play a key role in promoting the electrocatalytic activity. Therefore, the as-designed Ru/N-CoP-Co2P presents good catalytic performance for the HER in a wide pH range. Ru/N-CoP-Co2P merely needs overpotentials of 63, 100, and 65 mV to obtain 10 mA cm-2 in acidic, alkaline, and seawater electrolytes. This research provides a novel and efficient strategy for the synthesis of TMPs with highly efficient catalytic activity.
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Heteroatom doping and heterostructure construction are the key methods to improve the performance of electrocatalysts. However, developing such catalysts remains a challenging task. Herein, we designed two comparable polymers, phytic acid/thiourea polymer (PATP) and phytic acid/urea polymer (PAUP), as precursors, which contain C, N, S/O, and P by microwave heating. To pinpoint how the introduction of sulfur would affect the electronic structure and catalytic activity, these two polymers were physically blended with CoCo-Prussian blue analogue (CoCo-PBA) and further calcination, respectively. The highly dispersed CoP/Co2P-rich interfacial catalysts anchored on the N,S-codoped or N-doped carbon support were successfully prepared (CoP/Co2P@CNS and CoP/Co2P@CN). The prepared CoP/Co2P@CNS catalyst showed good ORR properties (E1/2 = 0.856 V vs RHE) and OER properties (Ej10 = 1.54 V vs RHE), which were superior to the commercial Pt/C and RuO2 catalysts. The reversible oxygen electrode index (ΔE = Ej10 - E1/2) can reach â¼0.684 V. Meanwhile, the rechargeable zinc-air battery assembled with a CoP/Co2P@CNS catalyst as the air cathode also showed excellent performance, with a charge-discharge cycle stability of up to 900 h. DFT calculations further confirm that the introduction of S atoms can affect the electronic structure and enhance the catalytic activity of C and N atoms on carbon support.
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The electronic and magnetic properties of the Au-doped diamond surface are investigated by first-principles calculation. After Au-doping, diamond shows surface p-type conductivity with an areal electron density of 6.34 × 1013 cm-2. Unlike the non-magnetic feature of intrinsic diamond, magnetism is induced for diamond (100), (110) and (111) surfaces as well as at different terminations (H, F, N and O). The magnetism originates from the s-p hybridization between the Au-6s state and the C-2p state, and the spin charge density and magnetic moments of Au-doped diamond originate mainly from the Au atoms and their surrounding C atoms. Further studies show that the magnetic properties still maintain under different doping concentrations (0.125-0.5 monolayer). Therefore, this study would provide great potential applications of diamond in novel magnetic semiconductors and transistors.
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Mapping gene interactions within tissues/cell types plays a crucial role in understanding the genetic basis of human physiology and disease. Tissue functional gene networks (FGNs) are essential models for mapping complex gene interactions. We present TissueNexus, a database of 49 human tissue/cell line FGNs constructed by integrating heterogeneous genomic data. We adopted an advanced machine learning approach for data integration because Bayesian classifiers, which is the main approach used for constructing existing tissue gene networks, cannot capture the interaction and nonlinearity of genomic features well. A total of 1,341 RNA-seq datasets containing 52,087 samples were integrated for all of these networks. Because the tissue label for RNA-seq data may be annotated with different names or be missing, we performed intensive hand-curation to improve quality. We further developed a user-friendly database for network search, visualization, and functional analysis. We illustrate the application of TissueNexus in prioritizing disease genes. The database is publicly available at https://www.diseaselinks.com/TissueNexus/.
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Bases de Datos Genéticas , Redes Reguladoras de Genes/genética , Especificidad de Órganos/genética , RNA-Seq , Curaduría de Datos , Manejo de Datos , Genoma Humano/genética , Humanos , Programas InformáticosRESUMEN
Doxorubicin (DOX) has been an effective antitumor agent for human liver cancer cells; however, an overdose might lead to major side effects appearing in clinical applications. In this work, we present a strategy of combining DOX and blue light (BL) irradiation for the antitumor treatment of HepG2 cells (one typical human liver cancer cell line). It is demonstrated that synergetic DOX and BL can significantly reduce cell proliferation and increase the apoptotic rate of HepG2 cells in comparison to individual DOX treatment. The additional BL irradiation is further helpful for enhancing the inhibition of cell migration and invasion. Analyses of reactive oxygen species (ROS) level and Western blotting reveal that the strategy results in more ROS accumulation, mitochondrial damage, and the upregulation of proapoptotic protein (Bcl-2) and downregulation of antiapoptotic protein (Bax). In addition to the improved therapeutic effect, the non-contact BL irradiation is greatly helpful for reducing the dosage of DOX, and subsequently reduces the side effects caused by the DOX drug. These findings offer a novel perspective for the therapeutic approach toward liver cancer with high efficiency and reduced side effects.
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Apoptosis , Movimiento Celular , Proliferación Celular , Doxorrubicina , Luz , Neoplasias Hepáticas , Especies Reactivas de Oxígeno , Doxorrubicina/farmacología , Humanos , Células Hep G2 , Neoplasias Hepáticas/tratamiento farmacológico , Neoplasias Hepáticas/radioterapia , Neoplasias Hepáticas/patología , Neoplasias Hepáticas/metabolismo , Especies Reactivas de Oxígeno/metabolismo , Apoptosis/efectos de los fármacos , Apoptosis/efectos de la radiación , Proliferación Celular/efectos de los fármacos , Proliferación Celular/efectos de la radiación , Movimiento Celular/efectos de los fármacos , Movimiento Celular/efectos de la radiación , Luz AzulRESUMEN
Environmentally friendly electrocatalytic coupling of CO2 and N2 for urea synthesis is a promising strategy. However, it is still facing problems such as low yield as well as low stability. Here, a new carbon-coated liquid alloy catalyst, Ga79Cu11Mo10@C is designed for efficient electrochemical urea synthesis by activating Ga active sites. During the N2 and CO2 co-reduction process, the yield of urea reaches 28.25â mmol h-1 g-1, which is the highest yield reported so far under the same conditions, the Faraday efficiency (FE) is also as high as 60.6 % at -0.4â V vs. RHE. In addition, the catalyst shows excellent stability under 100â h of testing. Comprehensive analyses showed that sequential exposure of a high density of active sites promoted the adsorption and activation of N2 and CO2 for efficient coupling reactions. This coupling reaction occurs through a thermodynamic spontaneous reaction between *N=N* and CO to form a C-N bond. The deformability of the liquid state facilitates catalyst recovery and enhances stability and resistance to poisoning. Moreover, the introduction of Cu and Mo stimulates the Ga active sites, which successfully synthesises the *NCON* intermediate. The reaction energy barrier of the third proton-coupled electron transfer process rate-determining step (RDS) *NHCONHâ*NHCONH2 was lowered, ensuring the efficient synthesis of urea.