Exosome-related lncRNA score: A value-based individual treatment strategy for predicting the response to immunotherapy in clear cell renal cell carcinoma.

BACKGROUND: Exosomes play a crucial role in intercellular communication in clear cell renal cell carcinoma (ccRCC), while the long non-coding RNAs (lncRNAs) are implicated in tumorigenesis and progression. AIMS: The purpose of this study is to construction a exosomes-related lncRNA score and a ceRNA network to predict the response to immunotherapy and potential targeted drug in ccRCC. METHODS: Data of ccRCC patients were obtained from the TCGA database. Pearson correlation analysis was used to identify eExosomes-related lncRNAs (ERLRs) from Top10 exosomes-related genes that have been screened. The entire cohort was randomly divided into a training cohort and a validation cohort in equal scale. LASSO regression and multivariate cox regression was used to construct the ERLRs-based score. Differences in clinicopathological characteristics, immune microenvironment, immune checkpoints, and drug susceptibility between the high- and low-risk groups were also investigated. Finally, the relevant ceRNA network was constructed by machine learning to analyze their potential targets in immunotherapy and drug use of ccRCC patients. RESULTS: A score consisting of 4ERLRs was identified, and patients with higher ERLRs-based score tended to have a worse prognosis than those with lower ERLRs-based score. ROC curves and multivariate Cox regression analysis demonstrated that the score could be considered as a risk factor for prognosis in both training and validation cohorts. Moreover, patients with high scores are predisposed to experience poor overall survival, a larger prevalence of advanced stage (III-IV), a greater tumor mutational burden, a higher infiltration of immunosuppressive cells, and a greater likelihood of responding favorably to immunotherapy. The importance of EMX2OS was determined by mechanical learning, and the ceRNA network was constructed, and EMX2OS may be a potential therapeutic target, possibly exerting its function through the EMX2OS/hsa-miR-31-5p/TLN2 axis. CONCLUSIONS: Based on machine learning, a novel ERLRs-based score was constructed for predicting the survival of ccRCC patients. The ERLRs-based score is a promising potential independent prognostic factor that is closely correlated with the immune microenvironment and clinicopathological characteristics. Meanwhile, we screened out key lncRNAEMX2OS and identified the EMX2OS/hsa-miR-31-5p/TLN2 axis, which may provide new clues for the targeted therapy of ccRCC.

Relationship between affiliate stigma and family quality of life among parents of children with autism spectrum disorders: The mediating role of parenting self-efficacy.

BACKGROUND: The parents of children with autism spectrum disorder (ASD) are under great pressure and experience discrimination in their daily lives, which affects their family quality of life (FQOL). OBJECTIVE: METHODS: A total of 237 parents of children with ASD were recruited in a university-affiliated hospital in Guangzhou, China, from October 2020 to April 2021 by convenience sampling. The Affiliate Stigma Scale, Parenting Sense of Competence Scale and Beach Center Family Quality of Life Scale were employed for data collection. RESULTS: The results showed that affiliate stigma negatively predicts total FQOL and the dimensions of FQOL through both a direct effect and an indirect effect through parenting self-efficacy. CONCLUSIONS: The findings suggest that affiliate stigma is an important predictor of FQOL, and interventions to reduce affiliate stigma and strengthen parenting self-efficacy might be effective in improving FQOL in the parents of children with ASD.

SCPLPA: An miRNA-disease association prediction model based on spatial consistency projection and label propagation algorithm.

Identifying the association between miRNA and diseases is helpful for disease prevention, diagnosis and treatment. It is of great significance to use computational methods to predict potential human miRNA disease associations. Considering the shortcomings of existing computational methods, such as low prediction accuracy and weak generalization, we propose a new method called SCPLPA to predict miRNA-disease associations. First, a heterogeneous disease similarity network was constructed using the disease semantic similarity network and the disease Gaussian interaction spectrum kernel similarity network, while a heterogeneous miRNA similarity network was constructed using the miRNA functional similarity network and the miRNA Gaussian interaction spectrum kernel similarity network. Then, the estimated miRNA-disease association scores were evaluated by integrating the outcomes obtained by implementing label propagation algorithms in the heterogeneous disease similarity network and the heterogeneous miRNA similarity network. Finally, the spatial consistency projection algorithm of the network was used to extract miRNA disease association features to predict unverified associations between miRNA and diseases. SCPLPA was compared with four classical methods (MDHGI, NSEMDA, RFMDA and SNMFMDA), and the results of multiple evaluation metrics showed that SCPLPA exhibited the most outstanding predictive performance. Case studies have shown that SCPLPA can effectively identify miRNAs associated with colon neoplasms and kidney neoplasms. In summary, our proposed SCPLPA algorithm is easy to implement and can effectively predict miRNA disease associations, making it a reliable auxiliary tool for biomedical research.

Selective Cleavage of α-Olefins to Produce Acetylene and Hydrogen.

Acetylene production from mixed α-olefins emerges as a potentially green and energy-efficient approach with significant scientific value in the selective cleavage of C-C bonds. On the Pd(100) surface, it is experimentally revealed that C2 to C4 α-olefins undergo selective thermal cleavage to form surface acetylene and hydrogen. The high selectivity toward acetylene is attributed to the 4-fold hollow sites which are adept at severing the terminal double bonds in α-olefins to produce acetylene. A challenge arises, however, because acetylene tends to stay at the Pd(100) surface. By using the surface alloying methodology with alien Au, the surface Pd d-band center has been successfully shifted away from the Fermi level to release surface-generated acetylene from α-olefins as a gaseous product. Our study actually provides a technological strategy to economically produce acetylene and hydrogen from α-olefins.

Laccase immobilization and its degradation of emerging pollutants: A comprehensive review.

The chronic lack of effective disposal of pollutants has resulted in the detection of a wide variety of EPs in the environment, with concentrations high enough to affect ecological health. Laccase, as a versatile oxidase capable of catalyzing a wide range of substrates and without producing toxic by-products, is a potential candidate for the biodegradation of pollutants. Immobilization can provide favorable protection for free laccase, improve the stability of laccase in complex environments, and greatly enhance the reusability of laccase, which is significant in reducing the cost of industrial applications. This study introduces the properties of laccase and subsequently elaborate on the different support materials for laccase immobilization. The research advances in the degradation of EDs, PPCPs, and PAHs by immobilized laccase are then reviewed. This review provides a comprehensive understanding of laccase immobilization, as well as the advantages of various support materials, facilitating the development of more economical and efficient immobilization systems that can be put into practice to achieve the green degradation of EPs.

Second-Shell N Dopants Regulate Acidic O2 Reduction Pathways on Isolated Pt Sites.

Pt is a well-known benchmark catalyst in the acidic oxygen reduction reaction (ORR) that drives electrochemical O2-to-H2O conversion with maximum chemical energy-to-electricity efficiency. Once dispersing bulk Pt into isolated single atoms, however, the preferential ORR pathway remains a long-standing controversy due to their complex local coordination environment and diverse site density over substrates. Herein, using a set of carbon nanotube supported Pt-N-C single-atom catalysts, we demonstrate how the neighboring N dopants regulate the electronic structure of the Pt central atom and thus steer the ORR selectivity; that is, the O2-to-H2O2 conversion selectivity can be tailored from 10% to 85% at 0.3 V versus reversible hydrogen electrode. Moreover, via a comprehensive X-ray-radiated spectroscopy and shell-isolated nanoparticle-enhanced Raman spectroscopy analysis coupled with theoretical modeling, we reveal that a dominant pyridinic- and pyrrolic-N coordination within the first shell of Pt-N-C motifs favors the 4e- ORR, whereas the introduction of a second-shell graphitic-N dopant weakens *OOH binding on neighboring Pt sites and gives rise to a dominant 2e- ORR. These findings underscore the importance of the chemical environment effect for steering the electrochemical performance of single-atom catalysts.

Targeting Mitochondrial Complex I Deficiency in MPP+/MPTP-induced Parkinson's Disease Cell Culture and Mouse Models by Transducing Yeast NDI1 Gene.

BACKGROUND: MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine), original found in synthetic heroin, causes Parkinson's disease (PD) in human through its metabolite MPP+ by inhibiting complex I of mitochondrial respiratory chain in dopaminergic neurons. This study explored whether yeast internal NADH-quinone oxidoreductase (NDI1) has therapeutic effects in MPTP- induced PD models by functionally compensating for the impaired complex I. MPP+-treated SH-SY5Y cells and MPTP-treated mice were used as the PD cell culture and mouse models respectively. The recombinant NDI1 lentivirus was transduced into SH-SY5Y cells, or the recombinant NDI1 adeno-associated virus (rAAV5-NDI1) was injected into substantia nigra pars compacta (SNpc) of mice. RESULTS: The study in vitro showed NDI1 prevented MPP+-induced change in cell morphology and decreased cell viability, mitochondrial coupling efficiency, complex I-dependent oxygen consumption, and mitochondria-derived ATP. The study in vivo revealed that rAAV-NDI1 injection significantly improved the motor ability and exploration behavior of MPTP-induced PD mice. Accordingly, NDI1 notably improved dopaminergic neuron survival, reduced the inflammatory response, and significantly increased the dopamine content in striatum and complex I activity in substantia nigra. CONCLUSIONS: NDI1 compensates for the defective complex I in MPP+/MPTP-induced models, and vastly alleviates MPTP-induced toxic effect on dopaminergic neurons. Our study may provide a basis for gene therapy of sporadic PD with defective complex I caused by MPTP-like substance.

Modulating Nanoparticle Structure by Metal-Metal Oxide Interfacial Interaction in a CeO2-Supported Bimetallic System: The Ni-Cu Case.

Structure-optimized bimetallic and multicomponent catalysts often outperform single-component catalysts, inspiring a detailed investigation of metal-metal and metal-support interactions in the system. We investigated the geometric and electronic structures of ceria-supported Ni-Cu particles prepared using different metal deposition sequences employing a combination of X-ray photoelectron spectroscopy, resonant photoemission spectroscopy, and infrared reflection absorption spectroscopy. The bimetallic model catalyst structure was altered by a distinct surface evolution process determined by the metal deposition sequence. The postdeposited Cu stays on the surface of Ni predeposited CeO2 and forms only a limited Ni-Cu alloy in the Cu-contacted Ni region. However, when Ni is deposited on the Cu predeposited CeO2 surface, Ni can migrate through the Cu layer to the Cu-ceria interface and form an extended Ni-Cu alloy to the whole deposited metal layer on the ceria surface. The dynamic metal diffusion in the CeO2-supported Ni-Cu system indicates that metal-support interactions can be used to achieve the rational design of a bimetallic composition distribution during catalyst preparation.

Correlation between Interfacial Structures and Device Performance: The Double-Edged Sword Effect of Lead Iodide in Perovskite Solar Cells.

The interfacial electronic structure of perovskite layers and transport layers is critical for the performance and stability of perovskite solar cells (PSCs). The device performance of PSCs can generally be improved by adding a slight excess of lead iodide (PbI2 ) to the precursor solution. However, its underlying working mechanism is controversial. Here, we performed a comprehensive study of the electronic structures at the interface between CH3 NH3 PbI3 and C60 with and without the modification of PbI2 using in situ photoemission spectroscopy measurements. The correlation between the interfacial structures and the device performance was explored based on performance and stability tests. We found that there is an interfacial dipole reversal, and the downward band bending is larger at the CH3 NH3 PbI3 /C60 interface with the modification of PbI2 as compared to that without PbI2 . Therefore, PSCs with PbI2 modification exhibit faster charge carrier transport and slower carrier recombination. Nevertheless, the modification of PbI2 undermines the device stability due to aggravated iodide migration. Our findings provide a fundamental understanding of the CH3 NH3 PbI3 /C60 interfacial structure from the perspective of the atomic layer and insight into the double-edged sword effect of PbI2 as an additive.

KATZNCP: a miRNA-disease association prediction model integrating KATZ algorithm and network consistency projection.

BACKGROUND: Clinical studies have shown that miRNAs are closely related to human health. The study of potential associations between miRNAs and diseases will contribute to a profound understanding of the mechanism of disease development, as well as human disease prevention and treatment. MiRNA-disease associations predicted by computational methods are the best complement to biological experiments. RESULTS: In this research, a federated computational model KATZNCP was proposed on the basis of the KATZ algorithm and network consistency projection to infer the potential miRNA-disease associations. In KATZNCP, a heterogeneous network was initially constructed by integrating the known miRNA-disease association, integrated miRNA similarities, and integrated disease similarities; then, the KATZ algorithm was implemented in the heterogeneous network to obtain the estimated miRNA-disease prediction scores. Finally, the precise scores were obtained by the network consistency projection method as the final prediction results. KATZNCP achieved the reliable predictive performance in leave-one-out cross-validation (LOOCV) with an AUC value of 0.9325, which was better than the state-of-the-art comparable algorithms. Furthermore, case studies of lung neoplasms and esophageal neoplasms demonstrated the excellent predictive performance of KATZNCP. CONCLUSION: A new computational model KATZNCP was proposed for predicting potential miRNA-drug associations based on KATZ and network consistency projections, which can effectively predict the potential miRNA-disease interactions. Therefore, KATZNCP can be used to provide guidance for future experiments.

Surfactants Used in Colloidal Synthesis Modulate Ni Nanoparticle Surface Evolution for Selective CO2 Hydrogenation.

Colloidal chemistry holds promise to prepare uniform and size-controllable pre-catalysts; however, it remains a challenge to unveil the atomic-level transition from pre-catalysts to active catalytic surfaces under the reaction conditions to enable the mechanistic design of catalysts. Here, we report an ambient-pressure X-ray photoelectron spectroscopy study, coupled with in situ environmental transmission electron microscopy, infrared spectroscopy, and theoretical calculations, to elucidate the surface catalytic sites of colloidal Ni nanoparticles for CO2 hydrogenation. We show that Ni nanoparticles with phosphine ligands exhibit a distinct surface evolution compared with amine-capped ones, owing to the diffusion of P under oxidative (air) or reductive (CO2 + H2) gaseous environments at elevated temperatures. The resulting NiPx surface leads to a substantially improved selectivity for CO production, in contrast to the metallic Ni, which favors CH4. The further elimination of surface metallic Ni sites by designing multi-step P incorporation achieves unit selectivity of CO in high-rate CO2 hydrogenation.

Whole genome sequencing and annotation of Daedaleopsis sinensis, a wood-decaying fungus significantly degrading lignocellulose.

Daedaleopsis sinensis is a fungus that grows on wood and secretes a series of enzymes to degrade cellulose, hemicellulose, and lignin and cause wood rot decay. Wood-decaying fungi have ecological, economic, edible, and medicinal functions. Furthermore, the use of microorganisms to biodegrade lignocellulose has high application value. Genome sequencing has allowed microorganisms to be analyzed from the aspects of genome characteristics, genome function annotation, metabolic pathways, and comparative genomics. Subsequently, the relevant information regarding lignocellulosic degradation has been mined by bioinformatics. Here, we sequenced and analyzed the genome of D. sinensis for the first time. A 51.67-Mb genome sequence was assembled to 24 contigs, which led to the prediction of 12,153 protein-coding genes. Kyoto Encyclopedia of Genes and Genomes database analysis of the D. sinensis data revealed that 3,831 genes are involved in almost 120 metabolic pathways. According to the Carbohydrate-Active Enzyme database, 481 enzymes are found in D. sinensis, of which glycoside hydrolases are the most abundant. The genome sequence of D. sinensis provides insights into its lignocellulosic degradation and subsequent applications.

First genome assembly and annotation of Sanghuangporus weigelae uncovers its medicinal functions, metabolic pathways, and evolution.

Sanghuangporus, also known as "Sanghuang" in China, is a well-known genus of traditional Chinese medicinal macrofungi. To make more effective use of Sanghuangporus resources, we completed the first genome assembly and annotation of a monokaryon strain of S. weigelae in the present study. A 33.96-Mb genome sequence was assembled as 13 contigs, leading to prediction of 9377 protein-coding genes. Phylogenetic and average nucleotide identity analyses indicated that the S. weigelae genome is closely related to those of other Sanghuangporus species in evolutionary tree, which clustered in one clade. Collinearity analysis revealed a high level of collinearity of S. weigelae with S. baumii, S. vaninii, and S. sanghuang. Biosynthesis pathways potentially involved in medicinal properties, including terpenoid and polysaccharide synthesis, were identified in S. weigelae, while polysaccharides were identified as the main medicinal metabolites in S. weigelae, with flavonoids more important in Sanghuangporus than other medicinal mushroom groups. Genes encoding 332 carbohydrate-active enzymes were identified in the S. weigelae genome, including major glycoside hydrolases and glycosyltransferases predicted, revealing the robust lignocellulose degradation capacity of S. weigelae. Further, 130 genes, clustered in seven classes were annotated to encode cytochromes P450 in the S. weigelae genome. Overall, our results reveal the remarkably medicinal capacity of S. weigelae and provide new insights that will inform the study of evolution and medicinal application of S. weigelae. The data are a reference resource for the formulation of scientific and rational ecological protection policies for Sanghuangporus species.

Paired electrocatalysis in 5-hydroxymethylfurfural valorization.

5-Hydroxymethylfurfural (HMF) has aroused considerable interest over the past years as an important biomass-derived platform molecule, yielding various value-added products. The conventional HMF conversion requires noble metal catalysts and harsh operating conditions. On the other hand, the electrocatalytic conversion of HMF has been considered as an environmentally benign alternative. However, its practical application is limited by low overall energy efficiency and incomplete conversion. Paired electrolysis and highly efficient electrocatalysts are two viable strategies to address these limitations. Herein, an overview of coupled electrocatalytic HMF hydrogenation or hydrogen evolution reaction (HER) with HMF oxidation as well as the associated electrocatalysts are reviewed and discussed. In this mini-review, a brief introduction of electrocatalytic HMF upgrading is given, followed by the recent advances and challenges of paired electrolysis with an emphasis on the integration HMF electrohydrogenation with HMF electrooxidation. Finally, a perspective for a future sustainable biomass upgrading community based on electrocatalysis is proposed.

Promoting nickel oxidation state transitions in single-layer NiFeB hydroxide nanosheets for efficient oxygen evolution.

Promoting the formation of high-oxidation-state transition metal species in a hydroxide catalyst may improve its catalytic activity in the oxygen evolution reaction, which remains difficult to achieve with current synthetic strategies. Herein, we present a synthesis of single-layer NiFeB hydroxide nanosheets and demonstrate the efficacy of electron-deficient boron in promoting the formation of high-oxidation-state Ni for improved oxygen evolution activity. Raman spectroscopy, X-ray absorption spectroscopy, and electrochemical analyses show that incorporation of B into a NiFe hydroxide causes a cathodic shift of the Ni2+(OH)2 → Ni3+δOOH transition potential. Density functional theory calculations suggest an elevated oxidation state for Ni and decreased energy barriers for the reaction with the NiFeB hydroxide catalyst. Consequently, a current density of 100 mA cm-2 was achieved in 1 M KOH at an overpotential of 252 mV, placing it among the best Ni-based catalysts for this reaction. This work opens new opportunities in electronic engineering of metal hydroxides (or oxides) for efficient oxygen evolution in water-splitting applications.

Machine Learning Predicts the X-ray Photoelectron Spectroscopy of the Solid Electrolyte Interface of Lithium Metal Battery.

X-ray photoelectron spectroscopy (XPS) is a powerful surface analysis technique widely applied in characterizing the solid electrolyte interphase (SEI) of lithium metal batteries. However, experiment XPS measurements alone fail to provide atomic structures from a deeply buried SEI, leaving vital details missing. By combining hybrid ab initio and reactive molecular dynamics (HAIR) and machine learning (ML) models, we present an artificial intelligence ab initio (AI-ai) framework to predict the XPS of a SEI. A localized high-concentration electrolyte with a Li metal anode is simulated with a HAIR scheme for ∼3 ns. Taking the local many-body tensor representation as a descriptor, four ML models are utilized to predict the core level shifts. Overall, extreme gradient boosting exhibits the highest accuracy and lowest variance (with errors ≤ 0.05 eV). Such an AI-ai model enables the XPS predictions of ten thousand frames with marginal cost.

Trace Key Mechanistic Features of the Arsenite Sequestration Reaction with Nanoscale Zerovalent Iron.

Nanoscale zerovalent iron (nZVI) is considered as a highly efficient material for sequestrating arsenite, but the origin of its high efficacy as well as the chemical transformations of arsenite during reaction is not well understood. Here, we report an in situ X-ray absorption spectroscopy (XAS) study to investigate the complex mechanism of nZVI reaction with arsenite under anaerobic conditions at the time scale from seconds to days. The time-resolved XAS analysis revealed a gradual oxidation of AsIII to AsV in the course of minutes to hours in both the solid and liquid phase for the high (above 0.5 g/L) nZVI dose system. When the reaction time increased up to 60 days, AsV became the dominant species. The quick-scanning extended X-ray absorption fine structure (QEAXFS) was introduced to discover the transient intermediate at the highly reactive stage, and a small red-shift in As K-edge absorption edge was observed. The QEAXFS combined with density functional theory (DFT) calculation suggested that the red-shift is likely due to the electron donation in a Fe-O-As complex and possible active sites of As sequestrations include Fe(OH)4 and 4-Fe cluster. This is the first time that the transient reaction intermediate was identified in the As-nZVI sequestration system at the fast-reacting early stage. This study also demonstrated usefulness of in situ monitoring techniques in environmental water research.

Involvement of RpoN in Regulating Motility, Biofilm, Resistance, and Spoilage Potential of Pseudomonas fluorescens.

Pseudomonas fluorescens is a typical spoiler of proteinaceous foods, and it is characterized by high spoilage activity. The sigma factor RpoN is a well-known regulator controlling nitrogen assimilation and virulence in many pathogens. However, its exact role in regulating the spoilage caused by P. fluorescens is unknown. Here, an in-frame deletion mutation of rpoN was constructed to investigate its global regulatory function through phenotypic and RNA-seq analysis. The results of phenotypic assays showed that the rpoN mutant was deficient in swimming motility, biofilm formation, and resistance to heat and nine antibiotics, while the mutant increased the resistance to H2O2. Moreover, the rpoN mutant markedly reduced extracellular protease and total volatile basic nitrogen (TVB-N) production in sterilized fish juice at 4°C; meanwhile, the juice with the rpoN mutant showed significantly higher sensory scores than that with the wild-type strain. To identify RpoN-controlled genes, RNA-seq-dependent transcriptomics analysis of the wild-type strain and the rpoN mutant was performed. A total of 1224 genes were significantly downregulated, and 474 genes were significantly upregulated by at least two folds at the RNA level in the rpoN mutant compared with the wild-type strain, revealing the involvement of RpoN in several cellular processes, mainly flagellar mobility, adhesion, polysaccharide metabolism, resistance, and amino acid transport and metabolism; this may contribute to the swimming motility, biofilm formation, stress and antibiotic resistance, and spoilage activities of P. fluorescens. Our results provide insights into the regulatory role of RpoN of P. fluorescens in food spoilage, which can be valuable to ensure food quality and safety.

Predictions of Chemical Shifts for Reactive Intermediates in CO2 Reduction under Operando Conditions.

The electroreduction of CO2 into value-added products is a significant step toward closing the global carbon loop, but its performance remains far from meeting the requirement of any practical application. The insufficient understanding of the reaction mechanism is one of the major causes that impede future development. Although several possible reaction pathways have been proposed, significant debates exist due to the lack of experimental support. In this work, we provide opportunities for experiments to validate the reaction mechanism by providing predictions of the core-level shifts (CLS) of reactive intermediates, which can be verified by the X-ray photoelectron spectroscopy (XPS) data in the experiment. We first validated our methods from benchmark calculations of cases with reliable experiments, from which we reach consistent predictions with experimental results. Then, we conduct theoretical calculations under conditions close to the operando experimental ones and predict the C 1s CLS of 20 reactive intermediates in the CO2 reduction reaction (CO2RR) to CH4 and C2H4 on a Cu(100) catalyst by carefully including solvation effects and applied voltage (U). The results presented in this work should be guidelines for future experiments to verify and interpret the reaction mechanism of CO2RR.