Genome-Based Classification of 'Haloarcula aidinensis' and Description of Three Novel Halophilic Archaea Isolated from an Inland Saline Lake, Coastal Saline Soil, and a Marine Solar Saltern.

'Haloarcula aidinensis' was described by a pioneer Chinese scientist focused on halophilic archaea thirty years ago, and the type strain of 'Haloarcula aidinensis' was recently classified based on phylogenetic, phylogenomic, and comparative genomic analyses. Other three novel halophilic archaeal strains, CK38T, DT43T, and SYNS111T, isolated from diverse saline environments in China, were simultaneously subjected to polyphasic classification. 'Haloarcula aidinensis' A5 was found to be related to Haloarcula amylolytica, while strains CK38T, DT43T, and SYNS111T represented three novel species of Haloarcula based on phylogenetic, phylogenomic, and comparative genomic analyses. These strains can be distinguished from other species within the genus Haloarcula based on multiple phenotypic characteristics. The major phospholipids, phosphatidylglycerol, phosphatidylglycerol phosphate methyl ester, and phosphatidylglycerol sulfate can be detected in these strains, while the glycolipid profiles of these strains are diverse. Strains A5 and DT43T contained glucosyl mannosyl glucosyl diether and a diglycosyl diether, while other strains CK38T and SYNS111T had sulfated mannosyl glucosyl diether and mannosyl glucosyl diether. Thus, strain A5 should be a reference strain of Har. amylolytica and three novel species of Haloarcula, Haloarcula sediminis sp. nov., Haloarcula brevis sp. nov., and Haloarcula regularis sp. nov. are proposed to accommodate strains CK38T (= CGMCC 1.62732T = JCM 36675T), DT43T (= CGMCC 1.18924T = JCM 36146T), and SYNS111T (= CGMCC 1.62601T = JCM 36149T).

Predicting suicidal behavior in individuals with depression over 50 years of age: Evidence from the UK biobank.

Objective: To construct applicable models suitable for predicting the risk of suicidal behavior among individuals with depression, particularly on the progression from no history of suicidal behavior to suicide attempts, as well as from suicidal ideation to suicide attempts. Methods: Based on a prospective cohort from the UK Biobank, a total of 55,139 individuals aged 50 and above with depression were enrolled in the study, among whom 29,528 exhibited suicidal behavior. Specifically, they were divided into control (25,611), suicidal ideation (24,361), and suicide attempt (5167) groups. Least absolute shrinkage and selection operator (LASSO) regression was used to identify a subset of important features for distinguishing suicidal ideation and suicide attempts. We used the Gradient Boosting Decision Tree (GBDT) algorithm with stratified 10-fold cross-validation and grid-search to construct the prediction models for suicidal ideation or suicide attempts. To address the dataset imbalance in classifying suicide attempts, we used random under-sampling. The SHapley Additive exPlanations (SHAP) were used to estimate the important variables in the GBDT model. Results: Significant differences in sociodemographic, economic, lifestyle, and psychological factors were observed across the three groups. Each classifier optimally utilized 8-11 features. Overall, the algorithms predicting suicide attempts demonstrated slightly higher performance than those predicting suicidal ideation. The GBDT classifier achieved the highest accuracy, with AUROC scores of 0.914 for suicide attempts and 0.803 for suicidal ideation. Distinctive predictive factors were identified for each group: while depression's inherent characteristics crucially distinguished the suicidal ideation group from controls, some key predictors, including the age of depression onset and childhood trauma events, were identified for suicide attempts. Conclusions: We established applicable machine learning-based models for predicting suicidal behavior, particularly suicide attempts, in individuals with depression, and clarified the differences in predictors between suicidal ideation and suicide attempts.

Exploring Protein Conformational Changes Using a Large-Scale Biophysical Sampling Augmented Deep Learning Strategy.

Inspired by the success of deep learning in predicting static protein structures, researchers are now actively exploring other deep learning algorithms aimed at predicting the conformational changes of proteins. Currently, a major challenge in the development of such models lies in the limited training data characterizing different conformational transitions. To address this issue, molecular dynamics simulations is combined with enhanced sampling methods to create a large-scale database. To this end, the study simulates the conformational changes of 2635 proteins featuring two known stable states, and collects the structural information along each transition pathway. Utilizing this database, a general deep learning model capable of predicting the transition pathway for a given protein is developed. The model exhibits general robustness across proteins with varying sequence lengths (ranging from 44 to 704 amino acids) and accommodates different types of conformational changes. Great agreement is shown between predictions and experimental data in several systems and successfully apply this model to identify a novel allosteric regulation in an important biological system, the human ß-cardiac myosin. These results demonstrate the effectiveness of the model in revealing the nature of protein conformational changes.

Anomalous Catalytic Performance on Non-Active-Site Carbon Substrate.

Carbon-based nanomaterials are gaining attention in electrocatalysis. This study investigates the inherent nitrate reduction activity (NO3RR) of commercial carbon paper as a substrate. Results showed that carbon paper, without additional catalysts, achieved approximately 80.42% NH3 Faradaic efficiency (FE) at -2.1 V vs. Hg/HgO in alkaline conditions, 83.51% NH3 FE at -1.9 V vs. Ag/AgCl in neutral conditions, and 14.53% NH3 FE at -1.9 V vs. MSE in acidic conditions. Density Functional Theory (DFT) calculations revealed energy barriers of 2.66 eV, 0.95 eV, and 1.37 eV, respectively. Molecular physisorption on the carbon paper surface generates an induced electric field, promoting charge transfer between the carbon paper and the adsorbed molecules, thus enhancing the activity of the carbon paper. These findings highlight the importance of considering the intrinsic catalytic properties of carbon substrates in catalyst design and evaluation, as overlooking these properties can lead to inaccurate performance assessments. This study emphasizes the need for a comprehensive approach to optimize catalytic systems.

Corrigendum: Effectiveness, safety, and treatment pattern of sodium zirconium cyclosilicate in Chinese patients with hyperkalemia: interim analysis from a multicenter, prospective, real-world study (Actualize Study).

[This corrects the article DOI: 10.3389/fphar.2024.1398953.].

A case report of severe left ventricular outflow tract obstruction in a middle-aged adult with chest pain and shock.

Severe left ventricular outflow tract obstruction (LVOTO) of hypertrophic cardiomyopathy is an acutely life-threatening, must-not miss, cardiology emergency that infrequently presents to the emergency department (ED). Patients with this condition usually manifest chest pain, syncope, cardiogenic shock, and severe ischemia. LVOTO is easy misdiagnosed as acute coronary syndrome. In our patient, the ECG showed a significant ST-segment depression and a 0/0 mmHg blood pressure when the peak left ventricular outflow tract gradient was abruptly increased by provocable activities. However, the patient had normal coronaries on cardiac catheterization, and, upon being immediately treated with intravenous esmolol, his symptoms were relieved and blood pressure was normal after 30 minutes. This case highlights, not only that early and exact diagnosis of LVOTO is crucial, but also the importance of the therapeutic strategies used.

Development and Validation of Radiomics-Based Models for Predicting the Parametrial Invasion in Stage IB1 to IIA2 Cervical Cancer.

Objective: To develop an early warning system that enables accurate parametrial invasion (PMI) risk prediction in cervical cancer patients with early-stage. Methods: We retrospectively collected 218 early-stage cervical cancer patients who were treated in Jingzhou Central Hospital from January 31, 2015, to January 31, 2023, and diagnosed with early stage cervical cancer by pathology. The prediction model training is achieved by randomly dividing 70% of the training queue population, with the remaining 30% used as the testing queue. Then, a prediction model based on machine learning algorithms (including random forest, generalized linear regression, decision tree, support vector machine, and artificial neural network) is constructed to predict the risk of PMI occurrence. Ultimately, the analysis of receiver operating characteristic curve (ROC) and decision curve analysis (DCA) is used to evaluate the predictive ability of various prediction models. Results: We finally included radiomics-based candidate variables that can be used for PMI model. Multivariate logistic regression analysis showed that energy, correlation, sum entropy (SUE), entropy, mean sum (MES), variance of differences (DIV), and inverse difference (IND) were independent risk factors for PMI occurrence. The predictive performance AUC of five types of machine learning ranges from 0.747 to 0.895 in the training set and can also reach a high accuracy of 0.905 in the testing set, indicating that the predictive model has ideal robustness. Conclusion: Our ML-based model incorporating GLCM parameters can predict PMI in cervical cancer patients with stage IB1 to IIA2, particularly the RFM, which could contribute to distinguishing PMI before surgery, especially in assisting decision-making on surgical scope.

Popliteal Artery Entrapment Syndrome: Updates for Evaluation, Diagnosis, and Treatment.

ABSTRACT: Popliteal artery entrapment syndrome remains difficult to diagnose. Meanwhile, our limited knowledge and understanding make treatment decisions complex. The list of differential diagnoses for exertional leg pain is broad. Oftentimes, patients exhibit confounding and coexisting diagnoses. However, accurate and rapid diagnosis of popliteal artery entrapment syndrome is essential to reduce potential lasting damage to the popliteal artery. A combination of clinical history, physical examination, ankle-brachial index, along with dynamic and static imaging such as duplex ultrasound, computed tomography angiogram, and magnetic resonance angiography, aids diagnosis. Surgical treatment may be definitive depending on the type of popliteal artery entrapment syndrome, but there have been recent advances in diagnostics with intravascular ultrasound and nonsurgical treatment with botulinum toxin type A. Further research is needed to standardize diagnostic criteria, uncover innovative diagnostic methods, and validate promising nonoperative treatment options.

Mechanistic insights into C-C coupling in electrocatalytic CO2 reduction reaction.

The utilization of CO2 has become an emerging area of research in response to climate change and global warming. The electrochemical CO2 reduction reaction (CO2RR) holds significant promise as a technology to address this issue by converting CO2 molecules into various commercially valuable chemicals. While CO2RR to C1 hydrocarbons has achieved high activity and selectivity, the C-C coupling to produce higher hydrocarbons remains challenging due to low energy efficiency and the prevalent hydrogen evolution reaction (HER) on the same catalyst, leading to high hydrogenation rates. In this review, we aim to elucidate the fundamental challenges of C-C coupling and explore potential strategies to enhance the selectivity for higher hydrocarbon products. We discuss the mechanisms underlying the formation of C2 and C3 products, focusing on molecular catalysts that facilitate C-C coupling by positioning CO2 molecules in close proximity. Additionally, we provide a comprehensive overview of different approaches to improve higher hydrocarbon selectivity, along with future suggestions and recommendations for new researchers in the field. This review serves as a valuable resource for both academic researchers and industrial stakeholders aiming for the commercialization of CO2RR technologies.

Detection of PCBs and OCPs in the Irtysh River Water (GC-MS/MS) and ecological risk assessment.

This study optimized a gas chromatography-tandem triple quadrupole mass spectrometry (GC-MS/MS) method for the determination of 21 persistent organic pollutants (POPs) in Irtysh River water, including 14 organochlorines (OCPs) and 7 polychlorinated biphenyls (PCBs). Factors such as column temperature ramping, selection of qualitative and quantitative ion pairs and collision energy were considered to achieve perfect separation and accurate quantification of all 21 target compounds. The limits of detection (LOD) for PCBs and OCPs ranged from 0.21 to 1.18 ng/L. Applying this method to detect POPs in the Irtysh River revealed concentrations of OCPs ranging from ND to 20.2 ng/L and PCBs from ND to 0.411 ng/L. Source analysis indicated that POPs in the Irtysh River mainly originate from historical industrial and agricultural activities, particularly the deliberate use of pesticides. To ensure ecological safety and human health, expanding the range of target analytes and monitoring periods is necessary. This study provides:•Qualitative and quantitative analysis methods for 7 PCBs and 14 OCPs.•Recoveries achieved ranged between 74.6 to 109 % with RSD less than 15 %.•Analysis of sources, transport pathways, accumulation status, and ecological risks of PCBs and OCPs in the Irtysh River.

Selection of dialysis methods for end-stage kidney disease patients with diabetes.

The increasing prevalence of diabetes has led to a growing population of end-stage kidney disease (ESKD) patients with diabetes. Currently, kidney transplantation is the best treatment option for ESKD patients; however, it is limited by the lack of donors. Therefore, dialysis has become the standard treatment for ESKD patients. However, the optimal dialysis method for diabetic ESKD patients remains controversial. ESKD patients with diabetes often present with complex conditions and numerous complications. Furthermore, these patients face a high risk of infection and technical failure, are more susceptible to malnutrition, have difficulty establishing vascular access, and experience more frequent blood sugar fluctuations than the general population. Therefore, this article reviews nine critical aspects: Survival rate, glucose metabolism disorder, infectious complications, cardiovascular events, residual renal function, quality of life, economic benefits, malnutrition, and volume load. This study aims to assist clinicians in selecting individualized treatment methods by comparing the advantages and disadvantages of hemodialysis and peritoneal dialysis, thereby improving patients' quality of life and survival rates.

Dynamic development of microglia and macrophages after spinal cord injury.

Secondary injury following spinal cord injury is primarily characterized by a complex inflammatory response, with resident microglia and infiltrating macrophages playing pivotal roles. While previous studies have grouped these two cell types together based on similarities in structure and function, an increasing number of studies have demonstrated that microglia and macrophages exhibit differences in structure and function and have different effects on disease processes. In this study, we used single-cell RNA sequencing and spatial transcriptomics to identify the distinct evolutionary paths of microglia and macrophages following spinal cord injury. Our results showed that microglia were activated to a pro-inflammatory phenotype immediately after spinal cord injury, gradually transforming to an anti-inflammatory steady state phenotype as the disease progressed. Regarding macrophages, our findings highlighted abundant communication with other cells, including fibroblasts and neurons. Both pro-inflammatory and neuroprotective effects of macrophages were also identified; the pro-inflammatory effect may be related to integrin ß2 (Itgb2) and the neuroprotective effect may be related to the oncostatin M pathway. These findings were validated by in vivo experiments. This research underscores differences in the cellular dynamics of microglia and macrophages following spinal cord injury, and may offer new perspectives on inflammatory mechanisms and potential therapeutic targets.

Predicting physical functioning status in older adults: insights from wrist accelerometer sensors and derived digital biomarkers of physical activity.

OBJECTIVE: Conventional physical activity (PA) metrics derived from wearable sensors may not capture the cumulative, transitions from sedentary to active, and multidimensional patterns of PA, limiting the ability to predict physical function impairment (PFI) in older adults. This study aims to identify unique temporal patterns and develop novel digital biomarkers from wrist accelerometer data for predicting PFI and its subtypes using explainable artificial intelligence techniques. MATERIALS AND METHODS: Wrist accelerometer streaming data from 747 participants in the National Health and Aging Trends Study (NHATS) were used to calculate 231 PA features through time-series analysis techniques-Tsfresh. Predictive models for PFI and its subtypes (walking, balance, and extremity strength) were developed using 6 machine learning (ML) algorithms with hyperparameter optimization. The SHapley Additive exPlanations method was employed to interpret the ML models and rank the importance of input features. RESULTS: Temporal analysis revealed peak PA differences between PFI and healthy controls from 9:00 to 11:00 am. The best-performing model (Gradient boosting Tree) achieved an area under the curve score of 85.93%, accuracy of 81.52%, sensitivity of 77.03%, and specificity of 87.50% when combining wrist accelerometer streaming data (WAPAS) features with demographic data. DISCUSSION: The novel digital biomarkers, including change quantiles, Fourier transform (FFT) coefficients, and Aggregated (AGG) Linear Trend, outperformed traditional PA metrics in predicting PFI. These findings highlight the importance of capturing the multidimensional nature of PA patterns for PFI. CONCLUSION: This study investigates the potential of wrist accelerometer digital biomarkers in predicting PFI and its subtypes in older adults. Integrated PFI monitoring systems with digital biomarkers would improve the current state of remote PFI surveillance.

Effectiveness, safety, and treatment pattern of sodium zirconium cyclosilicate in Chinese patients with hyperkalemia: interim analysis from a multicenter, prospective, real-world study (Actualize Study).

Introduction: Sodium zirconium cyclosilicate (SZC) is a nonabsorbed cation-exchanger approved in China for the treatment of hyperkalemia [HK; serum potassium (sK+) levels >5.0 mmol/L]. This is the first real-world study aimed to assess the effectiveness, safety, and treatment patterns of SZC in Chinese patients with HK. Here we present the results of the first interim analysis. Methods: This multicenter, prospective, cohort study included patients aged ≥18 years with documented HK within 1-year before study enrollment day. These patients were followed up for 6 months from the enrollment day after initiating SZC treatment. The treatment was categorized into correction phase (FAS-P1) and maintenance phase (FAS-P2 new and ongoing users). Subgroup analysis was performed in patients on hemodialysis (FAS-H). The primary objective was evaluation of safety profile of SZC; secondary objectives included assessment of treatment patterns of SZC and its effectiveness. Results: Of 421 screened patients, 193, 354, and 162 patients were enrolled in the FAS-P1, FAS-P2, and FAS-H groups, respectively. sK+ levels were reduced significantly from 5.9 mmol/L to 5.0 mmol/L after the correction phase. For the maintenance phase, the mean sK+ levels were maintained at 5.2 mmol/L and 5.0 mmol/L in the FAS-P2 new and ongoing user, respectively, and 5.3 mmol/L in the FAS-H subgroup. A considerable proportion of patients showed normokalemia after 48 h of SZC treatment (FAS-P1:51.3%) which was maintained up to 6 months in the maintenance phase (FAS-P2:44%). SZC was well-tolerated. Conclusion: SZC was effective and safe for the treatment of HK in real-world clinical practice in China.

Hippocampal subfield morphology from first episodes of bipolar disorder type II and major depressive disorder in a drug naïve Chinese cohort.

Introduction: Symptoms during the onset of major depressive disorder [MDD] and bipolar disorder type II [BD-II] are similar. The difference of hippocampus subregion could be a biological marker to distinguish MDD from BD-II. Methods: We recruited 61 drug-naïve patients with a first-episode MDD and BD-II episode and 30 healthy controls (HC) to participate in a magnetic resonance imaging [MRI] study. We built a general linear model (one-way analysis of covariance) with 22 hippocampal subfields and two total hippocampal volumes as dependent variables, and the diagnosis of MDD, BD-II, and HC as independent variables. We performed pair-wise comparisons of hippocampal subfield volumes between MDD and HC, BD-II and MDD, BD-II and HC with post hoc for primary analysis. Results: We identified three regions that differed significantly in size between patients and controls. The left hippocampal fissure, the hippocampal-amygdaloid transition area (HATA), and the right subiculum body were all significantly larger in patients with MDD compared with the HC. In the onset of first-episode of MDD, the hippocampal volume increased significantly, especially on the left side comparing to HC. However, we found differences between MDD and BD-II were not statistically significant. The volume of the left HATA and right subiculum body in BD-II was larger. Conclusions: The sample size of this study is relatively small, as it is a cross-sectional comparative study. In both MDD and BD-II groups, the volume of more left subregions appeared to increase. The left subregions were severely injured in the development of depressive disorder.

Eggplant NAC domain transcription factor SmNST1 as an activator promotes secondary cell wall thickening.

NAC-domain transcription factors (TFs) are plant-specific transcriptional regulators playing crucial roles in plant secondary cell wall (SCW) biosynthesis. SCW is important for plant growth and development, maintaining plant morphology, providing rigid support, ensuring material transportation and participating in plant stress responses as a protective barrier. However, the molecular mechanisms underlying SCW in eggplant have not been thoroughly explored. In this study, the NAC domain TFs SmNST1 and SmNST2 were cloned from the eggplant line 'Sanyue qie'. SmNST1 and SmNST2 expression levels were the highest in the roots and stems. Subcellular localization analysis showed that they were localized in the cell membrane and nucleus. Their overexpression in transgenic tobacco showed that SmNST1 promotes SCW thickening. The expression of a set of SCW biosynthetic genes for cellulose, xylan and lignin, which regulate SCW formation, was increased in transgenic tobacco. Bimolecular fluorescence and luciferase complementation assays showed that SmNST1 interacted with SmNST2 in vivo. Yeast one-hybrid, electrophoretic mobility shift assay (EMSA) and Dual-luciferase reporter assays showed that SmMYB26 directly bound to the SmNST1 promoter and acted as an activator. SmNST1 and SmNST2 interact with the SmMYB108 promoter and repress SmMYB108 expression. Altogether, we showed that SmNST1 positively regulates SCW formation, improving our understanding of SCW biosynthesis transcriptional regulation.

SKLB023 protects against inflammation and apoptosis in sepsis-associated acute kidney injury via the inhibition of toll-like receptor 4 signaling.

Sepsis-associated acute kidney injury (SA-AKI) is one of common critical illnesses with high morbidity and mortality. At present, effective therapeutic drugs for SA-AKI are remain lacking. SKLB023 is a synthetic small-molecule compound which exerts potent anti-inflammatory effects in our previous studies. Here, this study aimed to characterize the protective effect of SKLB023 on SA-AKI and explore its underlying mechanism. The SA-AKI experimental models have been established by cecum ligation/puncture (CLP) and lipopolysaccharide (LPS) injection in male C57BL/6J mice. SKLB023 was administered by gavage (50 or 25 mg/kg in CLP model and 50 mg/kg in LPS model) daily 3 days in advance and 30 min earlier on the day of modeling. Our results confirmed SKLB023 treatment could improve the survival of SA-AKI mice and ameliorate renal pathological injury, inflammation, and apoptosis in the two types of septic AKI mice. Mechanically, SKLB023 deceased the expression of TLR4 in LPS-triggered renal tubular epithelial cells, and inhibited the activation of downstream pathways including NF-κB and MAPK pathways. Our study suggested that SKLB023 is expected to be a potential drug for the prevention and treatment of septic AKI.

Attenuated frontotemporal brain activation during cognitive tasks is associated with lower succinate dehydrogenase protein levels in patients with major depressive disorder.

BACKGROUND: Previous studies have shown a lower hemodynamic response in patients with major depressive disorder (MDD) during cognitive tasks. However, the mechanism underlying impaired hemodynamic and neural responses to cognitive tasks in MDD patients remains unclear. Succinate dehydrogenase (SDH) is a key biomarker of mitochondrial energy generation, and it can affect the hemodynamic response via the neurovascular coupling effect. In the current study, cerebral hemodynamic responses were detected during verbal fluency tasks (VFTs) via functional near-infrared spectroscopy (fNIRS) and SDH protein levels were examined in serum from MDD patients to quickly identify whether these hemodynamic alterations were related to mitochondrial energy metabolism. METHODS: Fifty patients with first-episode drug-naïve MDD and 42 healthy controls (HCs) were recruited according to inclusion and exclusion criteria. The 17-item Hamilton Depression Rating Scale (17-HDRS), Hamilton Anxiety Rating Scale (HAMA) and Inventory of Depressive Symptomatology-Self Report (IDS-SR) were used to assess the clinical symptoms of the patients. All participants underwent fNIRS measurements to evaluate cerebral hemodynamic responses in the frontal and temporal cortex during VFTs; moreover, SDH protein levels were measured using an enzyme-linked immunosorbent assay. RESULTS: Activation in the frontal-temporal brain region during the VFTs was lower in patients with MDD than in HCs. The SDH level in the serum of MDD patients was also significantly lower than that in HCs (p = 0.003), which significantly affected right lateral frontal (p = 0.025) and right temporal (p = 0.022) lobe activation. Both attenuated frontal-temporal activation during the VFTs (OR = 1.531) and lower SDH levels (OR = 1.038) were risk factors for MDD. CONCLUSIONS: MDD patients had lower cerebral hemodynamic responses to VFTs; this was associated with mitochondrial dysfunction, as indicated by SDH protein levels. Furthermore, attenuated hemodynamic responses in frontotemporal regions and lower SDH levels increased the risk for MDD. LIMITATIONS: The sample size is relatively small. SDH protein levels in peripheral blood may not necessarily reflect mitochondrial energy generation in the central nervous system.

Entropy-Engineered Middle-In Synthesis of Dual Single-Atom Compounds for Nitrate Reduction Reaction.

Despite the immense potential of Dual Single-Atom Compounds (DSACs), the challenges in their synthesis process, including complexity, stability, purity, and scalability, remain primary concerns in current research. Here, we present a general strategy, termed "Entropy-Engineered Middle-In Synthesis of Dual Single-Atom Compounds" (EEMIS-DSAC), which is meticulously crafted to produce a diverse range of DSACs, effectively addressing the aforementioned issues. Our strategy integrates the advantages of both bottom-up and top-down paradigms, proposing an insight into optimizing the catalyst structure. The as-fabricated DSACs exhibited excellent activity and stability in the nitrate reduction reaction (NO3RR). In a significant advancement, our prototypical CuNi DSACs demonstrated outstanding performance under conditions reminiscent of industrial wastewater. Specifically, under a NO3- concentration of 2000 ppm, it yielded a Faradaic efficiency (FE) for NH3 of 96.97%, coupled with a mass productivity of 131.47 mg h-1 mg-1 and an area productivity of 10.06 mg h-1 cm-2. Impressively, even under a heightened NO3- concentration of 0.5 M, the FE for NH3 peaked at 90.61%, with a mass productivity reaching 1024.50 mg h-1 mg-1 and an area productivity of 78.41 mg h-1 cm-2. This work underpins the potential of the EEMIS-DSAC approach, signaling a frontier for high-performing DSACs.