Development and evaluation of a disulfidoptosis-related lncRNA index for prognostication in clear cell renal cell carcinoma.

Background: This study introduces a novel prognostic tool, the Disulfidoptosis-Related lncRNA Index (DRLI), integrating the molecular signatures of disulfidoptosis and long non-coding RNAs (lncRNAs) with the cellular heterogeneity of the tumor microenvironment, to predict clinical outcomes in patients with clear cell renal cell carcinoma (ccRCC). Methods: We analyzed 530 tumor and 72 normal samples from The Cancer Genome Atlas (TCGA), employing k-means clustering based on disulfidoptosis-associated gene expression to stratify ccRCC samples into prognostic groups. lncRNAs correlated with disulfidoptosis were identified and used to construct the DRLI, which was validated by Kaplan-Meier and receiver operating characteristic curves. We utilized single-cell deconvolution analysis to estimate the proportion of immune cell types within the tumor microenvironment, while the ESTIMATE and TIDE algorithms were employed to assess immune infiltration and potential response to immunotherapy. Results: The Disulfidoptosis-Related lncRNA Index (DRLI) effectively stratified ccRCC patients into high and low-risk groups, significantly impacting survival outcomes (P < 0.001). High-risk patients, marked by a unique lncRNA profile associated with disulfidoptosis, faced worse prognoses. Single-cell analysis revealed marked tumor microenvironment heterogeneity, especially in immune cell makeup, correlating with patient risk levels. In prognostic predictions, DRLI outperformed traditional clinical indicators, achieving AUC values of 0.779, 0.757, and 0.779 for 1-year, 3-year, and 5-year survival in the training set, and 0.746, 0.734, and 0.750 in the validation set. Notably, while the constructed nomogram showed exceptional predictive capability for short-term prognosis (AUC = 0.877), the DRLI displayed remarkable long-term predictive accuracy, with its AUC value reaching 0.823 for 10-year survival, closely approaching the nomogram's performance. Conclusions: The study introduces the DRLI as a groundbreaking molecular stratification tool for ccRCC, enhancing prognostic precision and potentially guiding personalized treatment strategies. This advancement is particularly significant in the context of long-term survival predictions. Our findings also elucidate the complex interplay between disulfidoptosis, lncRNAs, and the immune microenvironment in ccRCC, offering a comprehensive perspective on its pathogenesis and progression. The DRLI and the nomogram together represent significant strides in ccRCC research, highlighting the importance of molecular-based assessments in predicting patient outcomes.

Characterization, hazard identification, and risk assessment of volatile organic compounds in Poly(butylene adipate-co-terephthalate)-based food contact articles.

The chemical safety of poly (butylene adipate-co-terephthalate) (PBAT) based food contact articles (FCAs) has aroused increasing toxicological concerns in recent years, but the chemical characterization and associated risk assessment still remain inadequate as it fails to elucidate the distribution pattern and discern the potential genotoxic and carcinogenic hazards of the identified substances. Herein, the volatile organic compounds (VOCs) in 50 batches of PBAT-based FCAs of representative categories and 10 batches of PLA and PBAT pellets were characterized, by which 237 VOCs of 10 chemical categories were identified and exhibited characteristic distribution patterns in the chemical spaces derived from their molecular descriptors. Chemical hazards associated with the identified VOCs were discerned by a hazard-driven classification scheme integrating hazard-related knowledge from multiple publicly available sources, and 34 VOCs were found to bear genotoxic or carcinogenic hazards and to feature higher average molecular weight than the other VOCs. Finally, the Risk and hazard quotient (HQ) calculated as the metrics of risk suggested that all identified VOCs posed acceptable risks (Risk<10-4 or HQ < 1), whereas oxolane, butyrolactone, N,N-dimethylacetamide, 2-butoxyethanol, benzyl alcohol, and 1,2,3-trichloropropane posed non-negligible (Risk>10-6) genotoxic or carcinogenic risk and thus should be of prioritized concern to promote the chemical safety of PBAT-based FCAs.

Characterization and elimination efficiency of volatile organic compounds in mechanically recycled polyethylene terephthalate at various recycling stages.

The recycling of polyethylene terephthalate (PET) stands as an effective strategy for mitigating plastic pollution and reducing resource waste. The study aimed to investigate the characterization and elimination efficiency of volatile organic compounds (VOCs) present in rPET at various recycling stages using comprehensive two-dimensional gas chromatography-quadrupole-time-of-flight-mass spectrometry coupled with chemometrics. The results revealed that 52, 135, 95, 44, and 33 VOCs, mostly classified into three chemical groups, were tentatively identified in virgin - PET (v-PET), cold water washed - rPET (C-rPET), decontaminated - rPET (D-rPET), melt-extruded - rPET (M-rPET), and solid-state polycondensation - rPET (S-rPET), respectively. Regarding the VOCs with high and median detection frequencies, fatty acyls showed the highest elimination efficiency (100 % and 92 %), followed by organooxygen compounds (81 % and 99 %), others (97 % and 95 %), and benzene and substituted derivatives (82 % and 95 %) in term of HS-SPME. Following the recycling process, there was a general decrease in the concentration of almost all VOCs, as evidenced by the substantial reduction of o-Xylene, hexanoic acid, octanal, and D-limonene from 18.11, 22.43, 30.74, and 7.41 mg/kg to 0, 0, 3.97, and 0 mg/kg, respectively. However, it was noteworthy that the VOCs identified in the samples were not completely extracted, owing to the limitations of HS-SPME. Furthermore, chemometrics analysis indicated significant discrimination among VOCs from vPET, C-rPET, D-rPET, and M-rPET, while indistinct differences were observed between M-rPET and S-rPET. This study contributes to the enhancement of the recycling process and emphasizes the importance of safeguarding consumer health in terms of elimination of VOCs.

Potential safety concerns of volatile constituents released from coffee-ground-blended single-use biodegradable drinking straws: A chemical space perspective.

Incorporating spent coffee grounds into single-use drinking straws for enhanced biodegradability also raises safety concerns due to increased chemical complexity. Here, volatile organic compounds (VOCs) present in coffee ground straws (CGS), polylactic acid straws (PLAS), and polypropylene straws (PPS) were characterized using headspace - solid-phase microextraction and migration assays, by which 430 and 153 VOCs of 10 chemical categories were identified by gas chromatography - mass spectrometry, respectively. Further, the VOCs were assessed for potential genetic toxicity by quantitative structure-activity relationship profiling and estimated daily intake (EDI) calculation, revealing that the VOCs identified in the CGS generally triggered the most structural alerts of genetic toxicity, and the EDIs of 37.9% of which exceeded the threshold of 0.15 µg person-1 d-1, also outnumbering that of the PLAS and PPS. Finally, 14 VOCs were prioritized due to their definite hazards, and generally higher EDIs or detection frequencies in the CGS. Meanwhile, the probability of producing safer CGS was also illustrated. Moreover, it was uncovered by chemical space that the VOCs with higher risk potentials tended to gather in the region defined by the molecular descriptor related to electronegativity or octanol/water partition coefficient. Our results provided valuable references to improve the chemical safety of the CGS, to promote consumer health, and to advance the sustainable development of food contact materials.

Indirect estimation of pediatric reference interval via density graph deep embedded clustering.

Establishing reference intervals (RIs) for pediatric patients is crucial in clinical decision-making, and there is a critical gap of pediatric RIs in China. However, the direct sampling technique for establishing RIs is resource-intensive and ethically challenging. Indirect estimation methods, such as unsupervised clustering algorithms, have emerged as potential alternatives for predicting reference intervals. This study introduces deep graph clustering methods into indirect estimation of pediatric reference intervals. Specifically, we propose a Density Graph Deep Embedded Clustering (DGDEC) algorithm, which incorporates a density feature extractor to enhance sample representation and provides additional perspectives for distinguishing different levels of health status among populations. Additionally, we construct an adjacency matrix by computing the similarity between samples after feature enhancement. The DGDEC algorithm leverages the adjacency matrix to capture the interrelationships between patients and divides patients into different groups, thereby estimating reference intervals for the potential healthy population. The experimental results demonstrate that when compared to other indirect estimation techniques, our method ensures the predicted pediatric reference intervals in different age and gender groups are closer to the true values while maintaining good generalization performance. Additionally, through ablation experiments, our study confirms that the similarity between patients and the multi-scale density features of samples can effectively describe the potential health status of patients.

Chemometrics-based Discrimination of Virgin and Recycled Acrylonitrile-Butadiene-Styrene Plastics Toys via Non-targeted Screening of Volatile Substances.

Owing to the growing emphasis on child safety, it is greatly urgent to identify and assess the unknown compounds and discriminate the recycled materials for plastic toys. In this study, gas chromatography mass spectrometry coupled with static headspace has been optimized by response surface methodology for non-targeted screening of unknown volatiles in acrylonitrile-butadiene-styrene (ABS) plastic toys. Optimum conditions for static headspace were 120 °C for extraction temperature and 48 min for extraction time. A total of 83 volatiles in 11 categories were qualitatively identified by matching the NIST database library, retention index and standard materials. Considering high positive rate and potential toxicity, high-risk volatiles in ABS plastic toys were listed and traced for safety pre-warning. Moreover, the differential volatiles between virgin and recycled ABS plastics were screened out by orthogonal partial least-squares discrimination analysis. Principal component analysis, hierarchical cluster analysis and linear discrimination analysis were employed to successfully discriminate recycled ABS plastic toys based on the differential volatiles. The proposed strategy represents an effective and promising analytical method for non-targeted screening and risk assessment of unknown volatiles and discrimination of recycled materials combining with various chemometric techniques for children's plastic products to safeguard children's health.

A nomogram based on the preoperative neutrophil-to-lymphocyte ratio to distinguish sarcomatoid renal cell carcinoma from clear cell renal cell carcinoma.

Objective: Our study aimed to assess the predictive value of the preoperative neutrophil-to-lymphocyte ratio(NLR) in distinguishing sarcomatoid renal cell carcinoma (SRCC) from clear cell renal cell carcinoma(CCRCC) and to developing a nomogram based on the preoperative NLR and other factors to distinguish SRCC from CCRCC. Materials and methods: The database involved 280 patients, including 46 SRCC and 234 CCRCC. logistic analysis was conducted to select the variables associated with identifying SRCC preoperatively, and subgroup analysis was used to further validate the ability of NLR with preoperative identification of SRCC.In addition, The data were randomly separated into a training cohort(n=195) and a validation cohort(n=85). And an NLR-based nomogram was plotted based on the logistic analysis results. The nomogram was evaluated according to its discrimination, consistency, and clinical benefits. Results: Multivariate analysis indicated that NLR, flank pain, tumor size, and total cholesterol(TC) were independent risk factors for identifying SRCC. The results of subgroup analysis showed that higher NLR was associated with a higher probability of SRCC in most subgroups. The area under the curve(AUC) of the training and validation cohorts were 0.801 and 0.738, respectively. The results of the calibration curve show high consistency between predicted and actual results. Decision Curve Analysis(DCA) showed clinical intervention based on the model was beneficial over most of the threshold risk range. Conclusion: NLR is a potential indicator for preoperative differentiation of SRCC and CCRCC, and the predictive model constructed based on NLR has a good predictive ability. The new model could provide suggestions for the early identification of SRCC.

Detection of trace chlorine pollutants in recycled pulp using gas chromatography-tandem mass spectrometry with response surface-optimized carbon structure online catalysis.

RATIONALE: Chlorinated aromatics and alkanes are widely used for their flame retardancy, but they need to be monitored when used in recycled pulp. This paper reports the use of palladium acetate/activated carbon (Pa/Ac) activated by nitric acid as an online catalyst to determine chlorinated aromatics and chlorinated alkanes in recycled paper products using gas chromatography-tandem mass spectrometry (GC-MS/MS), which significantly improves the sensitivity of the method and remarkably lowers the detection limits. METHODS: The Pa/Ac catalyst was prepared using a self-made catalytic device and used as key to the online catalytic conversion of target chlorinated aromatic hydrocarbons and chlorinated alkanes for GC-MS/MS analysis. The response surface model was used to optimize catalytic conditions. Then GC-MS/MS in the multireaction monitoring mode with online catalysis was applied for the analysis of polychlorinated biphenyls, polychlorinated terphenyls, polychlorinated naphthalene, and chlorinated paraffins (CP) in recycled paper products. RESULTS: Compared with traditional methods, the Pa/Ac catalyst can transform chlorinated aromatic hydrocarbons into aromatic hydrocarbons through dechlorination hydrogenation, thus lowering the detection limit of the GC-MS/MS method significantly. It can transform paraffin chloride into the corresponding alkane to better distinguish short-chain, medium-chain, or long-chain CPs. Online catalytic conversion significantly improved the sensitivity and reproducibility (88.7%-113.1%) of the method. Tissue samples with various concentration levels of chlorinated aromatics and chlorinated alkanes were tested. The linearity range of the reduced target compounds in the reduction product solution was 0.02-1.00 µg/ml (R2  > 0.995). The quantitative detection limit was 0.03-0.05 µg/kg, and relative standard deviation was less than 6.9%. CONCLUSION: This study was the first to introduce the Pa/Ac catalytic device as an online catalytic unit in the determination of chlorinated aromatics and chlorinated alkanes using the GC-MS/MS method. The target compounds were converted into alkanes and aromatic hydrocarbons with unchanged carbon structures, and the method could achieve a low detection limit with no need for high-end methods such as GC-chemical ionization ion source (CI)-MS or high-resolution mass spectrometry. These methods are suitable for the determination of chlorine pollutants in recycled paper and its raw materials.

Topology Optimization Method of a Cavity Receiver and Built-In Net-Based Flow Channels for a Solar Parabolic Dish Collector.

The design of a thermal cavity receiver and the arrangement of the fluid flow layout within it are critical in the construction of solar parabolic dish collectors, involving the prediction of the thermal-fluid physical field of the receiver and optimization design. However, the thermal-fluid analysis coupled with a heat loss model of the receiver is a non-linear and computationally intensive solving process that incurs high computational costs in the optimization procedure. To address this, we implement a net-based thermal-fluid model that incorporates heat loss analysis to describe the receiver's flow and heat transfer processes, reducing computational costs. The physical field results of the net-based thermal-fluid model are compared with those of the numerical simulation, enabling us to verify the accuracy of the established thermal-fluid model. Additionally, based on the developed thermal-fluid model, a topology optimization method that employs a genetic algorithm (GA) is developed to design the cavity receiver and its built-in net-based flow channels. Using the established optimization method, single-objective and multi-objective optimization experiments are conducted under inhomogeneous heat flux conditions, with objectives including maximizing temperature uniformity and thermal efficiency, as well as minimizing the pressure drop. The results reveal varying topological characteristics for different optimization objectives. In comparison with the reference design (spiral channel) under the same conditions, the multi-objective optimization results exhibit superior comprehensive performance.

Synaptic Resistor Circuits Based on Al Oxide and Ti Silicide for Concurrent Learning and Signal Processing in Artificial Intelligence Systems.

Neurobiological circuits containing synapses can process signals while learning concurrently in real time. Before an artificial neural network (ANN) can execute a signal-processing program, it must first be programmed by humans or trained with respect to a large and defined data set during learning processes, resulting in significant latency, high power consumption, and poor adaptability to unpredictable changing environments. In this work, a crossbar circuit of synaptic resistors (synstors) is reported, each synstor integrating a Si channel with an Al oxide memory layer and Ti silicide Schottky contacts. Individual synstors are characterized and analyzed to understand their concurrent signal-processing and learning abilities. Without any prior training, synstor circuits concurrently execute signal processing and learning in real time to fly drones toward a target position in an aerodynamically changing environment faster than human controllers, and with learning speed, performance, power consumption, and adaptability to the environment significantly superior to an ANN running on computers. The synstor circuit provides a path to establish power-efficient intelligent systems with real-time learning and adaptability in the capriciously mutable real world.

Analysis of microplastics released from plastic take-out food containers based on thermal properties and morphology study.

Plastic take-out food containers may release microplastics (MPs) into food and pose a potential risk to food safety and human health. Here, after being subjected to hot water treatment, MPs released from three types of plastic food containers (polypropylene, PP; polyethylene, PE; expanded polystyrene, EPS) were identified by micro-Raman spectroscopy. The results showed that the size of released MPs ranged from 0.8-38 µm and over 96% MPs were smaller than 10 µm. Various MPs concentrations were found from the three types of containers, that is, 1.90 × 104, 1.01 × 105, and 2.82 × 106 particles/L on average from PP, PE, and EPS, respectively. Moreover, based on thermal and morphology analysis, we discovered that both relaxations of the polymer chains in the rubbery state and defects caused by processing techniques might contribute to the release of MPs. Thus, such release can be reduced by increasing the thermal stability of the materials and mitigating the defects generated during production.

Occurrence of volatile contaminants in recycled poly(ethylene terephthalate) by HS-SPME-GC×GC-QTOF-MS combined with chemometrics for authenticity assessment of geographical recycling regions.

A comparison was performed on various methods detecting the volatile contaminants (VCs) in recycled poly(ethylene terephthalate) (rPET) flakes, the results demonstrated that head-space solid phase micro-extraction combined with comprehensive two-dimensional gas chromatograph-tandem quadrupole-time-of-flight mass spectrometry (HS-SPME-GC×GC-QTOF-MS) was a sensitive, effective, accurate method, and successfully applied to analyze 57 rPET flakes collected from different recycling plants in China. A total of 212 VCs were tentatively identified, and the possible source were associated with plastic, food, and cosmetics. 45 VCs are classified as high-priority compounds with toxicity level IV or V and may pose a risk to human health. Combined chemometrics for further analysis revealed that significant differences among these three geographical recycling regions. 6, 7, and 6 volatile markers were chosen based on VIP values and S-plot among plant1 plant 2 and plant 3, respectively. The markers differed significantly between recycled rPET samples in three geographical recycling regions based on chemometrics analysis. The initial classification rate and cross-validation accuracy were 100% on the identified VCs. These significant differences demonstrate that a systematic study is needed to obtain a comprehensive data on the contamination of rPET for food contact applications in China.

Sarcomatoid-associated gene risk index for clear cell renal cell carcinoma.

Sarcomatoid renal cell carcinoma is a de-differentiated form of kidney cancer with an extremely poor prognosis. Genes associated with sarcomatoid differentiation may be closely related to the prognosis of renal cell carcinoma. The prognosis of renal cell carcinoma itself is extremely variable, and a new prognostic model is needed to stratify patients and guide treatment. Data on clear cell renal cell carcinoma with or without sarcomatoid differentiation were obtained from TCGA database, and a sarcomatoid-associated gene risk index (SAGRI) and column line graphs were constructed using sarcomatoid-associated genes. The predictive power of the SAGRI and column line graphs was validated using an internal validation set and an independent validation set (E-MTAB-1980). The SAGRI was constructed using four sarcoma-like differentiation-related genes, COL7A1, LCTL, NPR3, ZFHX4, and had a 1-year AUC value of 0.725 in the training set, 0.712 in the internal validation set, and 0.770 in the independent validation set for TCGA training cohort, with high model reliability. The molecular characteristics among the SAGRI subgroups were analyzed by multiple methods, and results suggested that the SAGRI-HIGH subgroup may benefit more from immunotherapy to improve prognosis. SAGRI satisfactorily predicted the prognosis of patients with clear cell renal cell carcinoma with or without sarcomatoid differentiation.

The characterization and influence factors of semi-volatile compounds from mechanically recycled polyethylene terephthalate (rPET) by combining GC×GC-TOFMS and chemometrics.

A non-targeted method was developed for screening the semi-volatile compounds of different mechanically recycled PET intended for food contact materials. The data was further analyzed by multiple chemometrics methods to obtain the difference level, and the potential influence factors were investigated. The results showed that total dissolution with comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry was more effective than other reported methods. Based on the difference level, 97 compounds were characterized into 4 levels. 1-Methyl-2-pyrrolidinone originating from organic solvent was recognized as level IV and could be determined as the primary difference indicator. The contaminant is mainly attributed to the residuum derived from non-food consumer products. The specific types of contaminants and process parameters of the recycling, such as moisture content, properties of rPET, and temperature, were the potential key factors affecting the presence of semi-volatile compounds of mechanically recycled rPET.

Influence of Organizational Learning and Dynamic Capability on Organizational Performance of Human Resource Service Enterprises: Moderation Effect of Technology Environment and Market Environment.

This study aims to explore the influence of organizational learning and dynamic capability on organizational performance of human resource service enterprises with the moderating role of technology environment and market environment. Data were gathered from 360 human resource service enterprises, and applied the hierarchical linear regression method and structural equation model to test the hypotheses. We found that organizational learning has a significantly positive impact on resource integration capability, as well as has a significantly positive impact on resource reconfiguration capability of human resource service enterprises. Resource integration capability and resource reconfiguration capability have a significantly positive impact on organizational performance. Moreover, results indicated that the resource integration capability and resource reconfiguration capability partially mediate in the relationship between organizational learning and organizational performance. Furthermore, technology environment and market environment have positive moderation effect between resource integration capability and organizational performance of human resource service enterprises, as well as have positive moderation effect between resource reconfiguration capability and organizational performance of human resource service enterprises. The current study contributes to a better understand the impact mechanism of organizational learning on organizational performance from the perspective of organizational learning theory and dynamic capability theory. In addition, this study provides implications for human resource service enterprises and managers to improve organizational performance.

Machine learning directed discrimination of virgin and recycled poly(ethylene terephthalate) based on non-targeted analysis of volatile organic compounds.

The use of non-decontaminated recycled poly(ethylene terephthalate) (PET) in food packages arouses consumer safety concerns, and thus is a major obstacle hindering PET bottle-to-bottle recycling in many developing regions. Herein, machine learning (ML) algorithms were employed for the discrimination of 127 batches of virgin PET and recycled PET (rPET) samples based on 1247 volatile organic compounds (VOCs) tentatively identified by headspace solid-phase microextraction comprehensive two-dimensional gas chromatography quadrupole-time-of-flight mass spectrometry. 100% prediction accuracy was achieved for PET discrimination using random forest (RF) and support vector machine (SVM) algorithms. The features of VOCs bearing high variable contributions to the RF prediction performance characterized by mean decrease Gini and variable importance were summarized as high occurrence rate, dominant appearance and distinct instrument response. Further, RF and SVM were employed for PET discrimination using the simplified input datasets composed of 62 VOCs with the highest contributions to the RF prediction performance derived by the AUCRF algorithm, by which over 99% prediction accuracy was achieved. Our results demonstrated ML algorithms were reliable and powerful to address PET adulteration and were beneficial to boost food-contact applications of rPET bottles.

Expression of N-Terminal Cysteine Aß42 and Conjugation to Generate Fluorescent and Biotinylated Aß42.

Fluorescent derivatives of the ß-amyloid peptides (Aß) are valuable tools for studying the interactions of Aß with cells. Facile access to labeled expressed Aß offers the promise of Aß with greater sequence and stereochemical integrity, without impurities from amino acid deletion and epimerization. Here, we report methods for the expression of Aß42 with an N-terminal cysteine residue, Aß(C1-42), and its conjugation to generate Aß42 bearing fluorophores or biotin. The methods rely on the hitherto unrecognized observation that expression of the Aß(MC1-42) gene yields the Aß(C1-42) peptide, because the N-terminal methionine is endogenously excised by Escherichia coli. Conjugation of Aß(C1-42) with maleimide-functionalized fluorophores or biotin affords the N-terminally labeled Aß42. The expression affords ∼14 mg of N-terminal cysteine Aß from 1 L of bacterial culture. Subsequent conjugation affords ∼3 mg of labeled Aß from 1 L of bacterial culture with minimal cost for labeling reagents. High-performance liquid chromatography analysis indicates the N-terminal cysteine Aß to be >97% pure and labeled Aß peptides to be 94-97% pure. Biophysical studies show that the labeled Aß peptides behave like unlabeled Aß and suggest that labeling of the N-terminus does not substantially alter the properties of the Aß. We further demonstrate applications of the fluorophore-labeled Aß peptides by using fluorescence microscopy to visualize their interactions with mammalian cells and bacteria. We anticipate that these methods will provide researchers convenient access to useful N-terminally labeled Aß, as well as Aß with an N-terminal cysteine that enables further functionalization.

A Reusable CNT-Supported Single-Atom Iron Catalyst for the Highly Efficient Synthesis of C-N Bonds.

C-N bond formation is regarded as a very useful and fundamental reaction for the synthesis of nitrogen-containing molecules in both organic and pharmaceutical chemistry. Noble-metal and homogeneous catalysts have frequently been used for C-N bond formation, however, these catalysts have a number of disadvantages, such as high cost, toxicity, and low atom economy. In this work, a low-toxic and cheap iron complex (iron ethylene-1,2-diamine) has been loaded onto carbon nanotubes (CNTs) to prepare a heterogeneous single-atom catalyst (SAC) named Fe-Nx /CNTs. We employed this SAC in the synthesis of C-N bonds for the first time. It was found that Fe-Nx /CNTs is an efficient catalyst for the synthesis of C-N bonds starting from aromatic amines and ketones. Its catalytic performance was excellent, giving yields of up to 96 %, six-fold higher than the yields obtained with noble-metal catalysts, such as AuCl3 /CNTs and RhCl3 /CNTs. The catalyst showed efficacy in the reactions of thirteen aromatic amine substrates, without the need for additives, and seventeen enaminones were obtained. High-angle annular dark-field scanning transmission electron microscopy in combination with X-ray absorption spectroscopy revealed that the iron species were well dispersed in the Fe-Nx /CNTs catalyst as single atoms and that Fe-Nx might be the catalytic active species. This Fe-Nx /CNTs catalyst has potential industrial applications as it could be cycled seven times without any significant loss of activity.