Nanchang University

In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. For example, a key point that needs to be emphasized is thatthere is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure flux through the autophagy pathway (i.e., the completeprocess including the amount and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accumulation must be differentiated from stimuli that increase autophagic activity, defined as increasedautophagy induction coupled with increased delivery to, and degradation within, lysosomes (inmost higher eukaryotes and some protists such as Dictyostelium) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the field understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in manycases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. It is worth emphasizing here that lysosomal digestion is a stage of autophagy and evaluating its competence is a crucial part of the evaluation of autophagic flux, or complete autophagy. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as forreviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multipleassays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation, it is imperative to target by gene knockout or RNA interference more than one autophagyrelated protein. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways implying that not all Atg proteins can be used as a specific marker for an autophagic process. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular assays, we hope to encourage technical innovation in the field.

BACKGROUND/AIMS: To investigate the regulation of LaCl3 on lipopolysaccharides (LPS)-induced pro-inflammatory cytokines and adhesion molecules in human umbilical vein endothelial cells (HUVECs). METHODS: Primary cultured HUVECs were pretreated with 2.5 µM LaCl3 for 30 min followed by 1 µg/ml LPS for 2 h. Pro-inflammatory cytokine and adhesion molecule expressions were determined by real-time RT-PCR and ELISA. NF-κB/p65 nuclear translocation was examined by immunofluorescence and immuno-blot, and its DNA-binding activity was measured by chemiluminescence. Recruitment of NF-κB/p65, Jmjd3, and H3K27me3 to gene promoter regions was determined by ChIP-qPCR. RESULTS: LaCl3 exhibited no cytotoxic effects to primary HUVECs at concentrations ≤ 50 µM. LPS-mediated TNF-α, IL-1β, IL-6, MMP-9, and ICAM-1 production, nuclear translocation, and DNA-binding activity of NF-κB/p65, as well as Jmjd3 expression, were all reduced significantly by LaCl3. Furthermore, LaCl3 treatment significantly impaired LPS-induced enrichment of NF-κB/p65 to the promoter regions of TNF-α, MMP-9, IL-1β, ICAM-1, and IL-6; and of Jmjd3 to the promoter regions of TNF-α, MMP-9, IL-1β, and IL-6. H3K27me3 abundance in the promoter regions of TNF-α and ICAM-1 increased significantly in following LaCl3 treatment. CONCLUSION: LaCl3 inhibits pro-inflammatory cytokine and adhesion molecule expressions induced by LPS in HUVECs. NF-κB and histone demethylase Jmjd3 are involved in this effect.

In this retrospective case series, chest CT scans of 21 symptomatic patients from China infected with the 2019 novel coronavirus (2019-nCoV) were reviewed, with emphasis on identifying and characterizing the most common findings. Typical CT findings included bilateral pulmonary parenchymal ground-glass and consolidative pulmonary opacities, sometimes with a rounded morphology and a peripheral lung distribution. Notably, lung cavitation, discrete pulmonary nodules, pleural effusions, and lymphadenopathy were absent. Follow-up imaging in a subset of patients during the study time window often demonstrated mild or moderate progression of disease, as manifested by increasing extent and density of lung opacities.

The outbreak of the novel coronavirus disease (COVID-19) quickly spread all over China and to more than 20 other countries. Although the virus (severe acute respiratory syndrome coronavirus [SARS-Cov-2]) nucleic acid real-time polymerase chain reaction (PCR) test has become the standard method for diagnosis of SARS-CoV-2 infection, these real-time PCR test kits have many limitations. In addition, high false-negative rates were reported. There is an urgent need for an accurate and rapid test method to quickly identify a large number of infected patients and asymptomatic carriers to prevent virus transmission and assure timely treatment of patients. We have developed a rapid and simple point-of-care lateral flow immunoassay that can detect immunoglobulin M (IgM) and IgG antibodies simultaneously against SARS-CoV-2 virus in human blood within 15 minutes which can detect patients at different infection stages. With this test kit, we carried out clinical studies to validate its clinical efficacy uses. The clinical detection sensitivity and specificity of this test were measured using blood samples collected from 397 PCR confirmed COVID-19 patients and 128 negative patients at eight different clinical sites. The overall testing sensitivity was 88.66% and specificity was 90.63%. In addition, we evaluated clinical diagnosis results obtained from different types of venous and fingerstick blood samples. The results indicated great detection consistency among samples from fingerstick blood, serum and plasma of venous blood. The IgM-IgG combined assay has better utility and sensitivity compared with a single IgM or IgG test. It can be used for the rapid screening of SARS-CoV-2 carriers, symptomatic or asymptomatic, in hospitals, clinics, and test laboratories.

Abstract Objective To assess the prevalence of diabetes and its risk factors. Design Population based, cross sectional study. Setting 31 provinces in mainland China with nationally representative cross sectional data from 2015 to 2017. Participants 75 880 participants aged 18 and older—a nationally representative sample of the mainland Chinese population. Main outcome measures Prevalence of diabetes among adults living in China, and the prevalence by sex, regions, and ethnic groups, estimated by the 2018 American Diabetes Association (ADA) and the World Health Organization diagnostic criteria. Demographic characteristics, lifestyle, and history of disease were recorded by participants on a questionnaire. Anthropometric and clinical assessments were made of serum concentrations of fasting plasma glucose (one measurement), two hour plasma glucose, and glycated haemoglobin (HbA 1c ). Results The weighted prevalence of total diabetes (n=9772), self-reported diabetes (n=4464), newly diagnosed diabetes (n=5308), and prediabetes (n=27 230) diagnosed by the ADA criteria were 12.8% (95% confidence interval 12.0% to 13.6%), 6.0% (5.4% to 6.7%), 6.8% (6.1% to 7.4%), and 35.2% (33.5% to 37.0%), respectively, among adults living in China. The weighted prevalence of total diabetes was higher among adults aged 50 and older and among men. The prevalence of total diabetes in 31 provinces ranged from 6.2% in Guizhou to 19.9% in Inner Mongolia. Han ethnicity had the highest prevalence of diabetes (12.8%) and Hui ethnicity had the lowest (6.3%) among five investigated ethnicities. The weighted prevalence of total diabetes (n=8385) using the WHO criteria was 11.2% (95% confidence interval 10.5% to 11.9%). Conclusion The prevalence of diabetes has increased slightly from 2007 to 2017 among adults living in China. The findings indicate that diabetes is an important public health problem in China.

Development of efficient and robust electrocatalysts is critical for practical fuel cells. We report one-dimensional bunched platinum-nickel (Pt-Ni) alloy nanocages with a Pt-skin structure for the oxygen reduction reaction that display high mass activity (3.52 amperes per milligram platinum) and specific activity (5.16 milliamperes per square centimeter platinum), or nearly 17 and 14 times higher as compared with a commercial platinum on carbon (Pt/C) catalyst. The catalyst exhibits high stability with negligible activity decay after 50,000 cycles. Both the experimental results and theoretical calculations reveal the existence of fewer strongly bonded platinum-oxygen (Pt-O) sites induced by the strain and ligand effects. Moreover, the fuel cell assembled by this catalyst delivers a current density of 1.5 amperes per square centimeter at 0.6 volts and can operate steadily for at least 180 hours.

As the most commonly occurring cancer in women worldwide, breast cancer poses a formidable public health challenge on a global scale. Breast cancer consists of a group of biologically and molecularly heterogeneous diseases originated from the breast. While the risk factors associated with this cancer varies with respect to other cancers, genetic predisposition, most notably mutations in BRCA1 or BRCA2 gene, is an important causative factor for this malignancy. Breast cancers can begin in different areas of the breast, such as the ducts, the lobules, or the tissue in between. Within the large group of diverse breast carcinomas, there are various denoted types of breast cancer based on their invasiveness relative to the primary tumor sites. It is important to distinguish between the various subtypes because they have different prognoses and treatment implications. As there are remarkable parallels between normal development and breast cancer progression at the molecular level, it has been postulated that breast cancer may be derived from mammary cancer stem cells. Normal breast development and mammary stem cells are regulated by several signaling pathways, such as estrogen receptors (ERs), HER2, and Wnt/β-catenin signaling pathways, which control stem cell proliferation, cell death, cell differentiation, and cell motility. Furthermore, emerging evidence indicates that epigenetic regulations and noncoding RNAs may play important roles in breast cancer development and may contribute to the heterogeneity and metastatic aspects of breast cancer, especially for triple-negative breast cancer. This review provides a comprehensive survey of the molecular, cellular and genetic aspects of breast cancer.

OBJECTIVE: The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) has caused more than 26 million cases of Corona virus disease (COVID-19) in the world so far. To control the spread of the disease, screening large numbers of suspected cases for appropriate quarantine and treatment are a priority. Pathogenic laboratory testing is typically the gold standard, but it bears the burden of significant false negativity, adding to the urgent need of alternative diagnostic methods to combat the disease. Based on COVID-19 radiographic changes in CT images, this study hypothesized that artificial intelligence methods might be able to extract specific graphical features of COVID-19 and provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time for disease control. METHODS: We collected 1065 CT images of pathogen-confirmed COVID-19 cases along with those previously diagnosed with typical viral pneumonia. We modified the inception transfer-learning model to establish the algorithm, followed by internal and external validation. RESULTS: The internal validation achieved a total accuracy of 89.5% with a specificity of 0.88 and sensitivity of 0.87. The external testing dataset showed a total accuracy of 79.3% with a specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images, the first two nucleic acid test results were negative, and 46 were predicted as COVID-19 positive by the algorithm, with an accuracy of 85.2%. CONCLUSION: These results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate COVID-19 diagnosis. KEY POINTS: • The study evaluated the diagnostic performance of a deep learning algorithm using CT images to screen for COVID-19 during the influenza season. • As a screening method, our model achieved a relatively high sensitivity on internal and external CT image datasets. • The model was used to distinguish between COVID-19 and other typical viral pneumonia, both of which have quite similar radiologic characteristics.

Sensing strain of soft materials in small scale has attracted increasing attention. In this work, graphene woven fabrics (GWFs) are explored for highly sensitive sensing. A flexible and wearable strain sensor is assembled by adhering the GWFs on polymer and medical tape composite film. The sensor exhibits the following features: ultra‐light, relatively good sensitivity, high reversibility, superior physical robustness, easy fabrication, ease to follow human skin deformation, and so on. Some weak human motions are chosen to test the notable resistance change, including hand clenching, phonation, expression change, blink, breath, and pulse. Because of the distinctive features of high sensitivity and reversible extensibility, the GWFs based piezoresistive sensors have wide potential applications in fields of the displays, robotics, fatigue detection, body monitoring, and so forth.

Atomically dispersed transition metal active sites have emerged as one of the most important fields of study because they display promising performance in catalysis and have the potential to serve as ideal models for fundamental understanding. However, both the preparation and determination of such active sites remain a challenge. The structural engineering of carbon- and nitrogen-coordinated metal sites (M-N-C, M = Fe, Co, Ni, Mn, Cu, etc.) via employing new heteroatoms, e.g., P and S, remains challenging. In this study, carbon nanosheets embedded with nitrogen and phosphorus dual-coordinated iron active sites (denoted as Fe-N/P-C) were developed and determined using cutting edge techniques. Both experimental and theoretical results suggested that the N and P dual-coordinated iron sites were favorable for oxygen intermediate adsorption/desorption, resulting in accelerated reaction kinetics and promising catalytic oxygen reduction activity. This work not only provides efficient way to prepare well-defined single-atom active sites to boost catalytic performance but also paves the way to identify the dual-coordinated single metal atom sites.

Abstract Background The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) has caused more than 2.5 million cases of Corona Virus Disease (COVID-19) in the world so far, with that number continuing to grow. To control the spread of the disease, screening large numbers of suspected cases for appropriate quarantine and treatment is a priority. Pathogenic laboratory testing is the gold standard but is time-consuming with significant false negative results. Therefore, alternative diagnostic methods are urgently needed to combat the disease. Based on COVID-19 radiographical changes in CT images, we hypothesized that Artificial Intelligence’s deep learning methods might be able to extract COVID-19’s specific graphical features and provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time for disease control. Methods and Findings We collected 1,065 CT images of pathogen-confirmed COVID-19 cases (325 images) along with those previously diagnosed with typical viral pneumonia (740 images). We modified the Inception transfer-learning model to establish the algorithm, followed by internal and external validation. The internal validation achieved a total accuracy of 89.5% with specificity of 0.88 and sensitivity of 0.87. The external testing dataset showed a total accuracy of 79.3% with specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images that first two nucleic acid test results were negative, 46 were predicted as COVID-19 positive by the algorithm, with the accuracy of 85.2%. Conclusion These results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate COVID-19 diagnosis. Author summary To control the spread of the COVID-19, screening large numbers of suspected cases for appropriate quarantine and treatment measures is a priority. Pathogenic laboratory testing is the gold standard but is time-consuming with significant false negative results. Therefore, alternative diagnostic methods are urgently needed to combat the disease. We hypothesized that Artificial Intelligence’s deep learning methods might be able to extract COVID-19’s specific graphical features and provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time. We collected 1,065 CT images of pathogen-confirmed COVID-19 cases along with those previously diagnosed with typical viral pneumonia. We modified the Inception transfer-learning model to establish the algorithm. The internal validation achieved a total accuracy of 89.5% with specificity of 0.88 and sensitivity of 0.87. The external testing dataset showed a total accuracy of 79.3% with specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images that first two nucleic acid test results were negative, 46 were predicted as COVID-19 positive by the algorithm, with the accuracy of 85.2%. Our study represents the first study to apply artificial intelligence to CT images for effectively screening for COVID-19.

The gut microbiota, the largest symbiotic ecosystem with the host, has been shown to play important roles in maintaining intestinal homeostasis. Dysbiosis of the gut microbiome is caused by the imbalance between the commensal and pathogenic microbiomes. The commensal microbiome regulates the maturation of the mucosal immune system, while the pathogenic microbiome causes immunity dysfunction, resulting in disease development. The gut mucosal immune system, which consists of lymph nodes, lamina propria and epithelial cells, constitutes a protective barrier for the integrity of the intestinal tract. The composition of the gut microbiota is under the surveillance of the normal mucosal immune system. Inflammation, which is caused by abnormal immune responses, influences the balance of the gut microbiome, resulting in intestinal diseases. In this review, we briefly outlined the interaction between the gut microbiota and the immune system and provided a reference for future studies.

Mobile-edge computing (MEC) and wireless power transfer are two promising techniques to enhance the computation capability and to prolong the operational time of low-power wireless devices that are ubiquitous in Internet of Things. However, the computation performance and the harvested energy are significantly impacted by the severe propagation loss. In order to address this issue, an unmanned aerial vehicle (UAV)-enabled MEC wireless-powered system is studied in this paper. The computation rate maximization problems in a UAV-enabled MEC wireless powered system are investigated under both partial and binary computation offloading modes, subject to the energy-harvesting causal constraint and the UAV's speed constraint. These problems are non-convex and challenging to solve. A two-stage algorithm and a three-stage alternative algorithm are, respectively, proposed for solving the formulated problems. The closed-form expressions for the optimal central processing unit frequencies, user offloading time, and user transmit power are derived. The optimal selection scheme on whether users choose to locally compute or offload computation tasks is proposed for the binary computation offloading mode. Simulation results show that our proposed resource allocation schemes outperform other benchmark schemes. The results also demonstrate that the proposed schemes converge fast and have low computational complexity.

Soybean has undergone several genetic bottlenecks. These include domestication in Asia to produce numerous Asian landraces, introduction of relatively few landraces to North America, and then selective breeding over the past 75 years. It is presumed that these three human-mediated events have reduced genetic diversity. We sequenced 111 fragments from 102 genes in four soybean populations representing the populations before and after genetic bottlenecks. We show that soybean has lost many rare sequence variants and has undergone numerous allele frequency changes throughout its history. Although soybean genetic diversity has been eroded by human selection after domestication, it is notable that modern cultivars have retained 72% of the sequence diversity present in the Asian landraces but lost 79% of rare alleles (frequency </=0.10) found in the Asian landraces. Simulations indicated that the diversity lost through the genetic bottlenecks of introduction and plant breeding was mostly due to the small number of Asian introductions and not the artificial selection subsequently imposed by selective breeding. The bottleneck with the most impact was domestication; when the low sequence diversity present in the wild species was halved, 81% of the rare alleles were lost, and 60% of the genes exhibited evidence of significant allele frequency changes.

Acetylcholine, a neurotransmitter secreted by cholinergic neurons, is involved in signal transduction related to memory and learning ability. Alzheimer's disease (AD), a progressive and commonly diagnosed neurodegenerative disease, is characterized by memory and cognitive decline and behavioral disorders. The pathogenesis of AD is complex and remains unclear, being affected by various factors. The cholinergic hypothesis is the earliest theory about the pathogenesis of AD. Cholinergic atrophy and cognitive decline are accelerated in age-related neurodegenerative diseases such as AD. In addition, abnormal central cholinergic changes can also induce abnormal phosphorylation of ttau protein, nerve cell inflammation, cell apoptosis, and other pathological phenomena, but the exact mechanism of action is still unclear. Due to the complex and unclear pathogenesis, effective methods to prevent and treat AD are unavailable, and research to explore novel therapeutic drugs is various and active in the world. This review summaries the role of cholinergic signaling and the correlation between the cholinergic signaling pathway with other risk factors in AD and provides the latest research about the efficient therapeutic drugs and treatment of AD.

Exploring and understanding biological and pathological changes are of great significance for early diagnosis and therapy of diseases. Optical sensing and imaging approaches have experienced major progress in this field. Particularly, an emergence of various functional optical nanoprobes has provided enhanced sensitivity, specificity, targeting ability, as well as multiplexing and multimodal capabilities due to improvements in their intrinsic physicochemical and optical properties. However, one of the biggest challenges of conventional optical nanoprobes is their absolute intensity-dependent signal readout, which causes inaccurate sensing and imaging results due to the presence of various analyte-independent factors that can cause fluctuations in their absolute signal intensity. Ratiometric measurements provide built-in self-calibration for signal correction, enabling more sensitive and reliable detection. Optimizing nanoprobe designs with ratiometric strategies can surmount many of the limitations encountered by traditional optical nanoprobes. This review first elaborates upon existing optical nanoprobes that exploit ratiometric measurements for improved sensing and imaging, including fluorescence, surface enhanced Raman scattering (SERS), and photoacoustic nanoprobes. Next, a thorough discussion is provided on design strategies for these nanoprobes, and their potential biomedical applications for targeting specific biomolecule populations (e.g. cancer biomarkers and small molecules with physiological relevance), for imaging the tumor microenvironment (e.g. pH, reactive oxygen species, hypoxia, enzyme and metal ions), as well as for intraoperative image guidance of tumor-resection procedures.

Perovskites go organic The perovskite structure accommodates many different combinations of elements, making it attractive for use in a wide variety of applications. Building perovskites out of only organic compounds is appealing because these materials tend to be flexible, fracture-resistant, and potentially easier to synthesize than their inorganic counterparts. Ye et al. describe a previously unknown family of all-organic perovskites, of which they synthesized 23 different family members (see the Perspective by Li and Ji). The compounds are attractive as ferroelectrics, including one compound with properties close to the well-known inorganic ferroelectric BaTiO 3 . Science , this issue p. 151 ; see also p. 132

Entropy weight method (EWM) is a commonly used weighting method that measures value dispersion in decision-making. The greater the degree of dispersion, the greater the degree of differentiation, and more information can be derived. Meanwhile, higher weight should be given to the index, and vice versa. This study shows that the rationality of the EWM in decision-making is questionable. One example is water source site selection, which is generated by Monte Carlo Simulation. First, too many zero values result in the standardization result of the EWM being prone to distortion. Subsequently, this outcome will lead to immense index weight with low actual differentiation degree. Second, in multi-index decision-making involving classification, the classification degree can accurately reflect the information amount of the index. However, the EWM only considers the numerical discrimination degree of the index and ignores rank discrimination. These two shortcomings indicate that the EWM cannot correctly reflect the importance of the index weight, thus resulting in distorted decision-making results.

BACKGROUND: Human induced pluripotent stem cell-derived mesenchymal stem cells (hiPSC-MSCs) have emerged as a promising alternative for stem cell transplantation therapy. Exosomes derived from mesenchymal stem cells (MSC-Exos) can play important roles in repairing injured tissues. However, to date, no reports have demonstrated the use of hiPSC-MSC-Exos in cutaneous wound healing, and little is known regarding their underlying mechanisms in tissue repair. METHODS: hiPSC-MSC-Exos were injected subcutaneously around wound sites in a rat model and the efficacy of hiPSC-MSC-Exos was assessed by measuring wound closure areas, by histological and immunofluorescence examinations. We also evaluated the in vitro effects of hiPSC-MSC-Exos on both the proliferation and migration of human dermal fibroblasts and human umbilical vein endothelial cells (HUVECs) by cell-counting and scratch assays, respectively. The effects of exosomes on fibroblast collagen and elastin secretion were studied in enzyme-linked immunosorbent assays and quantitative reverse-transcriptase-polymerase chain reaction (qRT-PCR). In vitro capillary network formation was determined in tube-formation assays. RESULTS: Transplanting hiPSC-MSC-Exos to wound sites resulted in accelerated re-epithelialization, reduced scar widths, and the promotion of collagen maturity. Moreover, hiPSC-MSC-Exos not only promoted the generation of newly formed vessels, but also accelerated their maturation in wound sites. We found that hiPSC-MSC-Exos stimulated the proliferation and migration of human dermal fibroblasts and HUVECs in a dose-dependent manner in vitro. Similarly, Type I, III collagen and elastin secretion and mRNA expression by fibroblasts and tube formation by HUVECs were also increased with increasing hiPSC-MSC-Exos concentrations. CONCLUSIONS: Our findings suggest that hiPSC-MSC-Exos can facilitate cutaneous wound healing by promoting collagen synthesis and angiogenesis. These data provide the first evidence for the potential of hiPSC-MSC-Exos in treating cutaneous wounds.

Abstract Conversion of naturally abundant nitrogen to ammonia is a key (bio)chemical process to sustain life and represents a major challenge in chemistry and biology. Electrochemical reduction is emerging as a sustainable strategy for artificial nitrogen fixation at ambient conditions by tackling the hydrogen- and energy-intensive operations of the Haber–Bosch process. However, it is severely challenged by nitrogen activation and requires efficient catalysts for the nitrogen reduction reaction. Here we report that a boron carbide nanosheet acts as a metal-free catalyst for high-performance electrochemical nitrogen-to-ammonia fixation at ambient conditions. The catalyst can achieve a high ammonia yield of 26.57 μg h –1 mg –1 cat. and a fairly high Faradaic efficiency of 15.95% at –0.75 V versus reversible hydrogen electrode, placing it among the most active aqueous-based nitrogen reduction reaction electrocatalysts. Notably, it also shows high electrochemical stability and excellent selectivity. The catalytic mechanism is assessed using density functional theory calculations.