Navigating crisis: The effect of COVID-19 on sports entrepreneurs and service excellence in non-profit organizations.

Sports organizations have deemed sports entrepreneurship crucial in helping them overcome difficult times. The purpose of this research is to find out how the COVID-19 issue affected sports entrepreneurs and whether there are any variations in how non-profit sporting organizations anticipate entrepreneurs' effect on service excellence. This goal was achieved by comparing 145 sports organizations before and after the viral epidemic. The factors evaluated before (Time 1) and after (Time 2) the COVID-19 epidemic were compared using paired sample-t tests. Associations and multilevel linear declines were utilized to examine the link between the factors discussed in the two phases. After the emergence of COVID-19, the findings suggest that risk-taking and creativity are much more significant, although initiative has mostly stayed the same. Lastly, a favourable and statistically substantial correlation exists between sports entrepreneurial and service excellence during pre- and post-crisis periods.

Selective oxygen reduction reaction: mechanism understanding, catalyst design and practical application.

The oxygen reduction reaction (ORR) is a key component for many clean energy technologies and other industrial processes. However, the low selectivity and the sluggish reaction kinetics of ORR catalysts have hampered the energy conversion efficiency and real application of these new technologies mentioned before. Recently, tremendous efforts have been made in mechanism understanding, electrocatalyst development and system design. Here, a comprehensive and critical review is provided to present the recent advances in the field of the electrocatalytic ORR. The two-electron and four-electron transfer catalytic mechanisms and key evaluation parameters of the ORR are discussed first. Then, the up-to-date synthetic strategies and in situ characterization techniques for ORR electrocatalysts are systematically summarized. Lastly, a brief overview of various renewable energy conversion devices and systems involving the ORR, including fuel cells, metal-air batteries, production of hydrogen peroxide and other chemical synthesis processes, along with some challenges and opportunities, is presented.

Mechanism of N2O formation in catalytic after-treatment systems of ammonia/hydrogen-fuelled engines.

The combustion processes and catalytic after-treatment of ammonia/hydrogen-fueled engines, including NOx storage and reduction (NSR) and noble-metal selective catalytic reduction (SCR), can produce the byproduct N2O, a potent greenhouse gas that weakens the zero-carbon attribute of these fuels. Currently, the mechanism of N2O formation on DeNOx catalysts remains unclear due to limited research on catalytic after-treatment for such engines and the complexity of surface catalytic reactions. To elucidate the formation of N2O on the DeNOx catalysts of ammonia/hydrogen fuel engines, the impact factors on N2O formation on platinum catalysts (typical catalysts in NSR and noble-metal SCR) were investigated using first-principles molecular dynamics (FPMD). By employing the blue-moon ensemble enhanced sampling method and the slow-growth approach for free energy surface exploration, together with density functional theory (DFT) for electronic structure analysis, a linear relationship between the spin splitting of the d states of Pt clusters and N2O formation energy barriers was revealed, along with the increased structural sensitivity of Pt clusters with fewer atoms. It is highlighted that the energy barrier for N2O formation is determined by the matching degree of energy levels between molecules and surfaces. These findings provide atomic-scale insights into N2O formation on DeNOx catalysts for ammonia/hydrogen-fueled engines, facilitating N2O emission control for carbon-free engines.

Association of four metalloids in the serum and urine of individuals with major depressive disorders: a case-control study.

Background: Major depressive disorder (MDD) pathogenesis may involve metalloids in a significant way. The aim of our study was to identify potential links between MDD and metalloid elements [boron (B), germanium (Ge), arsenic (As), antimony (Sb)]. Methods: A total of 72 MDD cases and 75 healthy controls (HCs) were recruited from Zhumadian Second People's Hospital in Henan Province, China. The levels of four metallic elements (B, Ge, As, and Sb) in the serum and urine were measured using inductively coupled plasma mass spectrometry (ICP-MS). Results: In comparison to the HCs, the B, As, and Sb levels were considerably lower in the MDD group (p < 0.05) in the serum; the MDD group had significantly higher (p < 0.05) and significantly lower (p < 0.001) B and Sb levels in the urine. After adjusting for potential confounders, serum B (OR = 0.120; 95% CI, 0.048, 0.300; p < 0.001) and Sb (OR = 0.133; 95% CI, 0.055, 0.322; p < 0.001) showed a negative correlation with MDD. Urine B had a negative correlation (OR = 0.393; 95% CI, 0.193, 0.801; p = 0.01) with MDD, while urine Sb had a positive correlation (OR = 3.335; 95% CI, 1.654, 6.726; p = 0.001) with MDD. Conclusion: Our current research offers insightful hints for future investigation into the function of metalloids in connection to MDD processes.

Critical review on salt tolerance improvement and salt accumulation inhibition strategies of osmotic membrane bioreactors.

The osmotic membrane bioreactor (OMBR) is a novel wastewater treatment and resource recovery technology combining forward osmosis (FO) and membrane bioreactor. It has attracted attention for its low energy consumption and high contaminant removal performance. However, in the long-term operation, OMBR faces the problem of salt accumulation due to high salt rejection and reverse salt flux, which affects microbial activity and contaminants removal efficiency. This review analyzed the feasibility of screening salt-tolerant microorganisms and determining salinity thresholds to improve the salt tolerance of OMBR. Combined with recent research, the inhibition strategies for salt accumulation were reviewed, including the draw solution, FO membrane, operating conditions and coupling with other systems. It is hoped to provide a theoretical basis and practical guidance for the further development of OMBR. Finally, future research directions were prospected. This review provides new insights for achieving stable operation of OMBR and promotes its wide application.

Research on fertilization decision method for rice tillering stage based on the coupling of UAV hyperspectral remote sensing and WOFOST.

Introduction: The use of chemical fertilizers in rice field management directly affects rice yield. Traditional rice cultivation often relies on the experience of farmers to develop fertilization plans, which cannot be adjusted according to the fertilizer requirements of rice. At present, agricultural drones are widely used for early monitoring of rice, but due to their lack of rationality, they cannot directly guide fertilization. How to accurately apply nitrogen fertilizer during the tillering stage to stabilize rice yield is an urgent problem to be solved in the current large-scale rice production process. Methods: WOFOST is a highly mechanistic crop growth model that can effectively simulate the effects of fertilization on rice growth and development. However, due to its lack of spatial heterogeneity, its ability to simulate crop growth at the field level is weak. This study is based on UAV remote sensing to obtain hyperspectral data of rice canopy and assimilation with the WOFOST crop growth model, to study the decision-making method of nitrogen fertilizer application during the rice tillering stage. Extracting hyperspectral features of rice canopy using Continuous Projection Algorithm and constructing a hyperspectral inversion model for rice biomass based on Extreme Learning Machine. By using two data assimilation methods, Ensemble Kalman Filter and Four-Dimensional Variational, the inverted biomass of the rice biomass hyperspectral inversion model and the localized WOFOST crop growth model were assimilated, and the simulation results of the WOFOST model were corrected. With the average yield as the goal, use the WOFOST model to formulate fertilization decisions and create a fertilization prescription map to achieve precise fertilization during the tillering stage of rice. Results: The research results indicate that the training set R2 and RMSE of the rice biomass hyperspectral inversion model are 0.953 and 0.076, respectively, while the testing set R2 and RMSE are 0.914 and 0.110, respectively. When obtaining the same yield, the fertilization strategy based on the ENKF assimilation method applied less fertilizer, reducing 5.9% compared to the standard fertilization scheme. Discussion: This study enhances the rationality of unmanned aerial vehicle remote sensing machines through data assimilation, providing a new theoretical basis for the decision-making of rice fertilization.

Dispelling the Fake: Social Bot Detection Based on Edge Confidence Evaluation.

Social bot detection is essential for maintaining the safety and integrity of online social networks (OSNs). Graph neural networks (GNNs) have emerged as a promising solution. Mainstream GNN-based social bot detection methods learn rich user representations by recursively performing message passing along user-user interaction edges, where users are treated as nodes and their relationships as edges. However, these methods face challenges when detecting advanced bots interacting with genuine accounts. Interaction with real accounts results in the graph structure containing camouflaged and unreliable edges. These unreliable edges interfere with the differentiation between bot and human representations, and the iterative graph encoding process amplifies this unreliability. In this article, we propose a social Bot detection method based on Edge Confidence Evaluation (BECE). Our model incorporates an edge confidence evaluation module that assesses the reliability of the edges and identifies the unreliable edges. Specifically, we design features for edges based on the representation of user nodes and introduce parameterized Gaussian distributions to map the edge embeddings into a latent semantic space. We optimize these embeddings by minimizing Kullback-Leibler (KL) divergence from the standard distribution and evaluate their confidence based on edge representation. Experimental results on three real-world datasets demonstrate that BECE is effective and superior in social bot detection. Additionally, experimental results on six widely used GNN architectures demonstrate that our proposed edge confidence evaluation module can be used as a plug-in to improve detection performance.

An algorithm to identify patients aged 0-3 with rare genetic disorders.

BACKGROUND: With over 7000 Mendelian disorders, identifying children with a specific rare genetic disorder diagnosis through structured electronic medical record data is challenging given incompleteness of records, inaccurate medical diagnosis coding, as well as heterogeneity in clinical symptoms and procedures for specific disorders. We sought to develop a digital phenotyping algorithm (PheIndex) using electronic medical records to identify children aged 0-3 diagnosed with genetic disorders or who present with illness with an increased risk for genetic disorders. RESULTS: Through expert opinion, we established 13 criteria for the algorithm and derived a score and a classification. The performance of each criterion and the classification were validated by chart review. PheIndex identified 1,088 children out of 93,154 live births who may be at an increased risk for genetic disorders. Chart review demonstrated that the algorithm achieved 90% sensitivity, 97% specificity, and 94% accuracy. CONCLUSIONS: The PheIndex algorithm can help identify when a rare genetic disorder may be present, alerting providers to consider ordering a diagnostic genetic test and/or referring a patient to a medical geneticist.

Research on the Application and Mechanisms of Electroactive Microorganisms in Toxicants Monitoring: A Review.

A biological treatment is the core process for removing organic pollutants from industrial wastewater. However, industrial wastewater often contains large amounts of toxic and harmful pollutants, which can inhibit the activity of microorganisms in a treatment system, precipitate the deterioration of effluent quality, and threaten water ecological security from time to time. In most of the existing anaerobic biological treatment processes, toxic effects on microorganisms are determined according to the amounts of end-products of the biochemical reactions, and the evaluation results are relatively lacking. When microorganisms contact toxic substances, changes in biological metabolic activity precede the accumulation of reaction products. As sensitive units, electroactive microorganisms can generate electrical signals, a change in which can directly reflect the toxicity level. The applications of electroactive microorganisms for the toxicity monitoring of wastewater are very promising. Further attention needs to be paid to considering the appropriate evaluation index, the influence of the environment on test results, mechanisms, and other aspects. Therefore, we reviewed the literature regarding the above aspects in order to provide a research foundation for the practical application of electroactive microorganisms in toxicant monitoring.

Surfactant Micelle-Driven High-Efficiency and High-Resolution Length Separation of Carbon Nanotubes for Electronic Applications.

High-efficiency extraction of long single-wall carbon nanotubes (SWCNTs) with excellent optoelectronic properties from SWCNT solution is critical for enabling their application in high-performance optoelectronic devices. Here, a straightforward and high-efficiency method is reported for length separation of SWCNTs by modulating the concentrations of binary surfactants. The results demonstrate that long SWCNTs can spontaneously precipitate for binary-surfactant but not for single-surfactant systems. This effect is attributed to the formation of compound micelles by binary surfactants that squeeze the free space of long SWCNTs due to their large excluded volumes. With this technique, it can readily separate near-pure long (≥500 nm in length, 99% in content) and short (≤500 nm in length, 98% in content) SWCNTs with separation efficiencies of 26% and 64%, respectively, exhibiting markedly greater length resolution and separation efficiency than those of previously reported methods. Thin-film transistors fabricated from extracted semiconducting SWCNTs with lengths >500 nm exhibit significantly improved electrical properties, including a 10.5-fold on-state current and 14.7-fold mobility, compared with those with lengths <500 nm. The present length separation technique is perfectly compatible with various surfactant-based methods for structure separations of SWCNTs and is significant for fabrication of high-performance electronic and optoelectronic devices.

Simultaneous Wastewater Treatment and Resources Recovery by Forward Osmosis Coupled with Microbial Fuel Cell: A Review.

Osmotic microbial fuel cells (OsMFCs) with the abilities to simultaneously treat wastewater, produce clean water, and electricity provided a novel approach for the application of microbial fuel cell (MFC) and forward osmosis (FO). This synergistic merging of functions significantly improved the performances of OsMFCs. Nonetheless, despite their promising potential, OsMFCs currently receive inadequate attention in wastewater treatment, water reclamation, and energy recovery. In this review, we delved into the cooperation mechanisms between the MFC and the FO. MFC facilitates the FO process by promoting water flux, reducing reverse solute flux (RSF), and degrading contaminants in the feed solution (FS). Moreover, the water flux based on the FO principle contributed to MFC's electricity generation capability. Furthermore, we summarized the potential roles of OsMFCs in resource recovery, including nutrient, energy, and water recovery, and identified the key factors, such as configurations, FO membranes, and draw solutions (DS). We prospected the practical applications of OsMFCs in the future, including their capabilities to remove emerging pollutants. Finally, we also highlighted the existing challenges in membrane fouling, system expansion, and RSF. We hope this review serves as a useful guide for the practical implementation of OsMFCs.

A Light Multi-View Stereo Method with Patch-Uncertainty Awareness.

Multi-view stereo methods utilize image sequences from different views to generate a 3D point cloud model of the scene. However, existing approaches often overlook coarse-stage features, impacting the final reconstruction accuracy. Moreover, using a fixed range for all the pixels during inverse depth sampling can adversely affect depth estimation. To address these challenges, we present a novel learning-based multi-view stereo method incorporating attention mechanisms and an adaptive depth sampling strategy. Firstly, we propose a lightweight, coarse-feature-enhanced feature pyramid network in the feature extraction stage, augmented by a coarse-feature-enhanced module. This module integrates features with channel and spatial attention, enriching the contextual features that are crucial for the initial depth estimation. Secondly, we introduce a novel patch-uncertainty-based depth sampling strategy for depth refinement, dynamically configuring depth sampling ranges within the GRU-based optimization process. Furthermore, we incorporate an edge detection operator to extract edge features from the reference image's feature map. These edge features are additionally integrated into the iterative cost volume construction, enhancing the reconstruction accuracy. Lastly, our method is rigorously evaluated on the DTU and Tanks and Temples benchmark datasets, revealing its low GPU memory consumption and competitive reconstruction quality compared to other learning-based MVS methods.

Validation of biomechanical assessment of coronary plaque vulnerability based on intravascular optical coherence tomography and digital subtraction angiography.

Background: It has been suggested that biomechanical factors may influence plaque development. However, key determinants for assessing plaque vulnerability remain speculative. Methods: In this study, a two-dimensional (2D) structural mechanical analysis and a three-dimensional (3D) fluid-structure interaction (FSI) analysis were conducted based on intravascular optical coherence tomography (IV-OCT) and digital subtraction angiography (DSA) data sets. In the 2D study, 103 IV-OCT slices were analyzed. An in-depth morpho-mechanic analysis and a weighted least absolute shrinkage and selection operator (LASSO) regression analysis were conducted to identify the crucial features related to plaque vulnerability via the tuning parameter (λ). In the 3D study, the coronary model was reconstructed by fusing the IV-OCT and DSA data, and a FSI analysis was subsequently performed. The relationship between vulnerable plaque and wall shear stress (WSS) was investigated. Results: The influential factors were selected using the minimum criteria (λ-min) and one-standard error criteria (λ-1se). In addition to the common vulnerable factor of the minimum fibrous cap thickness (FCTmin), four biomechanical factors were selected by λ-min, including the average/maximal displacements and average/maximal stress, and two biomechanical factors were selected by λ-1se, including the average/maximal displacements. Additionally, the positions of the vulnerable plaques were consistent with the sites of high WSS. Conclusions: Functional indices are crucial for plaque status assessment. An evaluation based on biomechanical simulations might provide insights into risk identification and guide therapeutic decisions.

Differences in greenhouse gas emissions and microbial communities between underground and conventionally constructed wastewater treatment plants.

Large quantities of greenhouse gases (GHGs) are emitted into the atmosphere during wastewater treatment. In this study, GHG and microbial samples were collected from four wastewater treatment plants (WWTPs), and their differences and relationships were assessed. The study showed that, compared with conventionally constructed WWTPs, well-established gas collection systems in underground WWTPs facilitate comprehensive collection and accurate accounting of GHGs. In aboveground WWTPs, capped anoxic ponds promote methane production releasing it at 2-8 times the rate of uncapped emissions, in contrast to nitrous oxide emissions. Moreover, a stable subsurface environment allows for smaller fluctuations in daily GHG emissions and higher microbial diversity and abundance. This study highlights differences in GHG emission fluxes and microbial communities in differently constructed WWTPs, which are useful for control and accurate accounting of GHG emissions.

Low SAA4 gene expression is associated with advanced HCC stage and a poor prognosis.

At present, although there are tumor markers for hepatocellular carcinoma (HCC), markers with better predictive efficiency are needed. SAA4 gene expression in liver tumor and paracancerous tissues was analyzed using The Cancer Genome Atlas database. The differentially expressed genes (DEGs) were analyzed and visualized by heatmap and volcano plot. Survival analysis was performed based on SAA4 expression. SAA4 expression was compared in patients grouped based on clinicopathological features, and gene set enrichment analysis (GSEA) was conducted. Immunohistochemical staining was used to verify the SAA4 protein staining intensity from The Human Protein Atlas database and our center's samples. The diagnostic value of SAA4 for HCC was evaluated by receiver operating characteristic curves. SAA4 was expressed at low levels in HCC tissues, and low SAA4 expression was associated with a poor prognosis in HCC. In addition, SAA4 expression decreased with HCC progression. There were 188 upregulated DEGs and 1551 downregulated DEGs between the high and low SAA4 expression groups. Complement and coagulation cascades, fatty acid metabolism, and ECM receptor interaction were significantly enriched in the GSEA. SAA4 had good predictive efficacy for HCC and even early HCC and was superior to AFP. In general, low SAA4 expression was associated with advanced HCC stage and a poor prognosis. In addition, SAA4 may be helpful for the diagnosis of early HCC and may become a novel tumor marker with good predictive power for HCC.

Effect of NO2 on N2 O production and NOx emission reduction in NH3 Selective Catalytic Reduction.

With the introduction of increasingly strict emission regulations, reducing nitrogen oxide (NOx ) emissions and nitrous oxide (N2 O) production from diesel engines have become the focus of research. At high temperature, the reaction of NO2 in the catalyst generates the intermediate product NH4 NO3 , which first crystallizes below 300 °C. These crystals tend to block the pores and inhibit the reaction. Subsequently, N2 O is produced through the decomposition of NH4 NO3 , leading to additional pollution. Therefore, the concentration of NO2 has a direct impact on both the NOx conversion efficiency and the generation of N2 O, requiring consideration of the optimal proportion of NO2 in SCR. Considering these two factors, it is concluded that the optimal amount of NO2 varies with temperature. To improve the NOx conversion rate of the Cu-SSZ-13 catalyst at low temperatures and reduce N2 O generation, the optimal NO2 ratio of the Cu-SSZ-13 catalyst under various operating conditions is studied using numerical simulations. As the temperature rises, the optimal NO2 /NOx ratio first increases and then decreases. Under the optimal NO2 /NOx ratio, the NOx conversion rate significantly increases, while N2 O generation decreases considerably. The optimal NO2 /NOx ratio also provides suggestions for the optimization of the DOC-DPF-DCR system.

Geometric phase-encoded stimuli-responsive cholesteric liquid crystals for visualizing real-time remote monitoring: humidity sensing as a proof of concept.

Liquid crystals are a vital component of modern photonics, and recent studies have demonstrated the exceptional sensing properties of stimuli-responsive cholesteric liquid crystals. However, existing cholesteric liquid crystal-based sensors often rely on the naked eye perceptibility of structural color or the measurement of wavelength changes by spectrometric tools, which limits their practical applications. Therefore, developing a platform that produces recognizable sensing signals is critical. In this study, we present a visual sensing platform based on geometric phase encoding of stimuli-responsive cholesteric liquid crystal polymers that generates real-time visual patterns, rather than frequency changes. To demonstrate this platform's effectiveness, we used a humidity-responsive cholesteric liquid crystal polymer film encoded with a q-plate pattern, which revealed that humidity causes a shape change in the vortex beam reflected from the encoded cholesteric liquid crystal polymers. Moreover, we developed a prototype platform towards remote humidity monitoring benefiting from the high directionality and long-range transmission properties of laser beams carrying orbital angular momentum. Our approach provides a novel sensing platform for cholesteric liquid crystals-based sensors that offers promising practical applications. The ability to generate recognizable sensing signals through visual patterns offers a new level of practicality in the sensing field with stimuli-responsive cholesteric liquid crystals. This platform might have significant implications for a broad readership and will be of interest to researchers working in the field of photonics and sensing technology.

Cross-linked α-Ni(OH)2 nanosheets with a Ni3+-rich structure for accelerating electrochemical oxidation of 5-hydroxymethylfurfural.

Electrochemical oxidation of biomass-based 5-hydroxymethylfurfural (HMF) is an effective approach for achieving the high-value products of 2,5-furandicarboxylic acid (FDCA). However, the restricted formation of high-valence metal active species for electrocatalysts results in a sluggish kinetic process of HMF oxidation reaction (HMFOR). Herein, we fabricated the Ni3+-rich cross-linked α-Ni(OH)2 nanosheets for accelerating the HMFOR through an anion-mediated strategy. It is identified that the Cl- ions with strong penetrability replace a portion of lattice oxygen atoms in α-Ni(OH)2 to form Ni-Cl bonds, contributing to breaking the inherent lattice order and generating a special Ni3+-rich structure. Owing to the promoted adsorption and accelerated oxidation of hydroxyl and aldehyde groups by the affluent Ni3+ active species, the large oxidation current density of 116.5 mA cm-2 and HMFOR kinetic constant of 0.067 min-1 has been achieved at 1.45 V (vs. RHE). By analyzing the oxidation products, the FDCA yield and Faradic efficiency are both higher than 99.25 % and 99.36 % for five successive determinations. Therefore, this work provides an insightful anion-mediated strategy for designing high-performance electrocatalysts for biomass conversion application.

Two Antimicrobial Peptides Derived from Bacillus and Their Properties.

Growth promotion and disease prevention are important strategies in the modern husbandry industry, and for this reason, antibiotics are widely used as animal feed additives. However, the overuse of antibiotics has led to the serious problem of increasing resistance of pathogenic microorganisms, posing a major threat to the environment and human health. "Limiting antibiotics" and "Banning antibiotics" have become the inevitable trends in the development of the livestock feed industry, so the search for alternative antimicrobial agents has become a top priority. Antimicrobial peptides (AMPs) produced by Bacillus spp. have emerged as a promising alternative to antibiotics, due to their broad-spectrum antimicrobial activity against resistant pathogens. In this study, two strains of Bacillus velezensis 9-1 and B. inaquosorum 76-1 with good antibacterial activity were isolated from commercial feed additives, and the antimicrobial peptides produced by them were purified by ammonium sulfate precipitation, anion exchange chromatography, gel chromatography, and RP-HPLC. Finally, two small molecule peptides, named peptide-I and peptide-II, were obtained from strain 9-1 and 76-1, respectively. The molecular weight and sequences of the peptides were analyzed and identified by LC-MS/MS, which were 988.5706 Da and VFLENVLR, and 1286.6255 Da and FSGSGSGTAFTLR, respectively. The results of an antibacterial activity and stability study showed that the two peptides had good antibacterial activity against Staphylococcus aureus, B. cereus, and Salmonella enterica, and the minimum inhibitory concentrations were 64 µg/mL and 16 µg/mL, 32 µg/mL and 64 µg/mL, and 8 µg/mL and 8 µg/mL, respectively. All of them have good heat, acid, and alkali resistance and protease stability, and can be further developed as feed antibiotic substitutes.

Serum and urinary essential trace elements in association with major depressive disorders: a case-control study.

Introduction: The etiology and pathophysiology of major depressive disorders (MDDs) remain unclear. Increasing evidence has demonstrated that essential trace elements (ETEs), such as iodine (I), zinc (Zn), copper (Cu), selenium (Se), cobalt (Co), and molybdenum (Mo), play vital roles in MDDs. Methods: In total, 72 patients with MDD and 75 healthy controls (HCs) in the Zhumadian Second People's Hospital, Henan Province, China were recruited in our study. The levels of different ETEs were examined in both serum and urine, using an inductively coupled plasma mass spectrometer (ICP-MS), for both the MDD patients and HCs. Results: The serum levels of I, Se, Cu, and Mo were significantly lower in the MDD patients compared to the HCs (p < 0.05), and the urinary levels of I and Zn were significantly higher in the MDD patients compared to the HCs (p < 0.05). The serum concentration of I (Q3: OR = 0.210, Q4: OR = 0.272) was negatively associated with MDD after adjusting for potential confounders, including age, gender, and BMI, and the urinary concentration of I (Q4: OR = 2.952) was positively associated. Conclusions: The higher levels of I, Se, Cu, and Mo in serum might be protective against the development of MDD, and the excess I and Zn in urine may be associated with MDD pathogenesis. Future research needs to gain a deeper understanding of the metabolic pathways of ETEs, especially I, Se, Zn, Cu, and Mo, in MDD, and their role in the pathogenesis of depression.