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Artificial intelligence (AI) has been extensively researched in medicine, but its practical application remains limited. Meanwhile, there are various disparities in existing AI-enabled clinical studies, which pose a challenge to global health equity. In this study, we conducted an in-depth analysis of the geo-economic distribution of 159 AI-enabled clinical studies, as well as the gender disparities among these studies. We aim to reveal these disparities from a global literature perspective, thus highlighting the need for equitable access to medical AI technologies.

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Hypoxia can aggravate tumor occurrence, development, invasion, and metastasis, and greatly inhibit the photodynamic therapy (PDT) effect. Herein, carbon nitride (CNs)-based DNA and photosensitizer co-delivery systems (BPSCNs) with oxygen-producing functions are developed to address this problem. Selenide glucose (Seglu) is used as the dopant to prepare red/NIR-active CNs (SegluCNs). The tumor-targeting unit Bio-PEG2000 is utilized to construct BPSCNs nanoparticles through esterification reactions. Furthermore, DNA hydrophobization is realized via mixing P53 gene with a positively charged mitochondrial-targeted near-infrared (NIR) emitting photosensitizer (MTTPY), which is encapsulated in non-cationic BPSCNs for synergistic delivery. Ester bonds in BPSCNs@MTTPY-P53 complexes can be disrupted by lipase in the liver to facilitate P53 release, upregulated P53 expression, and promoted HIF-1α degradation in mitochondria. In addition, the oxygen produced by the complexes improved the hypoxic microenvironment of hepatocellular carcinoma (HCC), synergistically downregulated HIF-1α expression in mitochondria, promoted mitochondrial-derived ferroptosis and enhanced the PDT effect of the MTTPY unit. Both in vivo and in vitro experiments indicated that the transfected P53-DNA, produced O2 and ROS by these complexes synergistically led to mitochondrial-derived ferroptosis in hepatoma cells through the HIF-1α/SLC7A11 pathway, and completely avoiding PDT resistance caused by hypoxia, exerting a significant therapeutic role in HCC treatment.

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BACKGROUND: Nonadherence to medication among patients with cardiovascular diseases undermines the desired therapeutic outcomes. eHealth interventions emerge as promising strategies to effectively tackle this issue. OBJECTIVE: The aim of this study was to conduct a network meta-analysis (NMA) to compare and rank the efficacy of various eHealth interventions in improving medication adherence among patients with cardiovascular diseases (CVDs). METHODS: A systematic search strategy was conducted in PubMed, Embase, Web of Science, Cochrane, China National Knowledge Infrastructure Library (CNKI), China Science and Technology Journal Database (Weipu), and WanFang databases to search for randomized controlled trials (RCTs) published from their inception on January 15, 2024. We carried out a frequentist NMA to compare the efficacy of various eHealth interventions. The quality of the literature was assessed using the risk of bias tool from the Cochrane Handbook (version 2.0), and extracted data were analyzed using Stata16.0 (StataCorp LLC) and RevMan5.4 software (Cochrane Collaboration). The certainty of evidence was evaluated using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach. RESULTS: A total of 21 RCTs involving 3904 patients were enrolled. The NMA revealed that combined interventions (standardized mean difference [SMD] 0.89, 95% CI 0.22-1.57), telephone support (SMD 0.68, 95% CI 0.02-1.33), telemonitoring interventions (SMD 0.70, 95% CI 0.02-1.39), and mobile phone app interventions (SMD 0.65, 95% CI 0.01-1.30) were statistically superior to usual care. However, SMS compared to usual care showed no statistical difference. Notably, the combined intervention, with a surface under the cumulative ranking curve of 79.3%, appeared to be the most effective option for patients with CVDs. Regarding systolic blood pressure and diastolic blood pressure outcomes, the combined intervention also had the highest probability of being the best intervention. CONCLUSIONS: The research indicates that the combined intervention (SMS text messaging and telephone support) has the greatest likelihood of being the most effective eHealth intervention to improve medication adherence in patients with CVDs, followed by telemonitoring, telephone support, and app interventions. The results of these network meta-analyses can provide crucial evidence-based support for health care providers to enhance patients' medication adherence. Given the differences in the design and implementation of eHealth interventions, further large-scale, well-designed multicenter trials are needed. TRIAL REGISTRATION: INPLASY 2023120063; https://inplasy.com/inplasy-2023-12-0063/.

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BACKGROUND: The performance of GPT-4 in nursing examinations within the Chinese context has not yet been thoroughly evaluated. OBJECTIVE: To assess the performance of GPT-4 on multiple-choice and open-ended questions derived from nursing examinations in the Chinese context. METHODS: The data sets of the Chinese National Nursing Licensure Examination spanning 2021 to 2023 were used to evaluate the accuracy of GPT-4 in multiple-choice questions. The performance of GPT-4 on open-ended questions was examined using 18 case-based questions. RESULTS: For multiple-choice questions, GPT-4 achieved an accuracy of 71.0% (511/720). For open-ended questions, the responses were evaluated for cosine similarity, logical consistency, and information quality, all of which were found to be at a moderate level. CONCLUSION: GPT-4 performed well at addressing queries on basic knowledge. However, it has notable limitations in answering open-ended questions. Nursing educators should weigh the benefits and challenges of GPT-4 for integration into nursing education.

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Fat and its hydrolysis products, fatty acids, are indispensable nutritional components; however, prolonged excessive fat consumption, particularly in western diets, contributes to the onset of obesity and multiple metabolic disorders. In this study, we propose a daily-ingestible hydrogel (denoted as ßC-MA hydrogel) composed of natural ß-glucan and sodium carboxymethylcellulose crosslinked by malic acid at 120 °C. This hydrogel exhibits rapid swelling performance, up to 24-fold within 1 min and 176-fold after 1 h in deionized water. It also lengthens gastric retention and increases endogenous satiety signal levels, potentially controlling appetite and reducing food intake. Furthermore, ßC-MA hydrogels that enter the small intestine can effectively inhibit fat hydrolysis and decrease triglyceride synthesis and transport. Specifically, the hydrogels inhibit the release of free fatty acids (FFAs) by approximately 50 % during digestion, influence the translocation of triglycerides and FFAs across the intestinal epithelium, and reduce the serum triglyceride levels by 22.2 %. These findings suggest that ßC-MA hydrogels could serve as a noninvasive gastrointestinal device for weight control, with the advantage of reducing food intake and restoring lipid metabolism homeostasis.

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Hypoxia can lead to liver fibrosis and severely limits the efficacy of photodynamic therapy (PDT). Herein, carbon nitride (CN)-based hybrid nanoparticles (NPs) VPSGCNs@TSI for light-driven water splitting were utilized to solve this problem. CNs were doped with selenide glucose (Se-glu) to enhance their red/NIR region absorption. Then, vitamin A-poly(ethylene glycol) (VA-PEG) fragments and aggregation-induced emission (AIE) photosensitizers TSI were introduced into Se-glu-doped CN NPs (VPSGCNs) to construct VPSGCNs@TSI NPs. The introduction of VA-PEG fragments enhanced the targeting of the NPs to activated hepatic stellate cells (HSCs) and reduced their toxicity to ordinary liver cells. VPSGCN units could trigger water splitting to generate O2 under 660 nm laser irradiation, improve the hypoxic environment of the fibrosis site, downregulate HIF-1α expression, and activate HSC ferroptosis via the HIF-1α/SLC7A11 pathway. In addition, generated O2 could also increase the reactive oxygen species (ROS) production of TSI units in a hypoxic environment, thereby completely reversing hypoxia-triggered PDT resistance to enhance the PDT effect. The combination of water-splitting materials and photodynamic materials showed a 1 + 1 > 2 effect in increasing oxygen levels in liver fibrosis, promoting ferroptosis of activated HSCs and reversing PDT resistance caused by hypoxia.

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At present, biochar has a large application potential in soil amelioration, pollution remediation, carbon sequestration and emission reduction, and research on the effect of biochar on soil ecology and environment has made positive progress. However, under natural and anthropogenic perturbations, biochar may undergo a series of environmental behaviors such as migratory transformation, mineralization and decomposition, and synergistic transport, thus posing certain potential risks. This paper outlines the multi-interfacial migration pathway of biochar in "air-soil-plant-animal-water", and analyzes the migration process and mechanism at different interfaces during the preparation, transportation and application of biochar. The two stages of the biochar mineralization process (mineralization of easily degradable aliphatic carbon components in the early stage and mineralization of relatively stable aromatic carbon components in the later stage) were described, the self-influencing factors and external environmental factors of biochar mineralization were analyzed, and the mineral stabilization mechanism and positive/negative excitation effects of biochar into the soil were elucidated. The proximity between field natural and artificially simulated aging of biochar were analyzed, and the change of its properties showed a trend of biological aging > chemical aging > physical aging > natural aging, and in order to improve the simulation and prediction, the artificially simulated aging party needs to be changed from a qualitative method to a quantitative method. The technical advantages, application scope and potential drawbacks of different biochar modification methods were compared, and biological modification can create new materials with enhanced environmental application. The stability performance of modified biochar was compared, indicating that raw materials, pyrolysis temperature and modification method were the key factors affecting the stability of biochar. The potential risks to the soil environment from different pollutants carried by biochar were summarized, the levels of pollutants released from biochar in the soil environment were highlighted, and a comprehensive selection of ecological risk assessment methods was suggested in terms of evaluation requirements, data acquisition and operation difficulty. Dynamic tracing of migration decomposition behavior, long-term assessment of pollution remediation effects, and directional design of modified composite biochar materials were proposed as scientific issues worthy of focused attention. The results can provide a certain reference basis for the theoretical research and technological development of biochar.

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Reflective cholesteric liquid crystal (CLC) Pancharatnam-Berry phase lens (PBL) devices have attracted significant attention in augmented reality (AR) display due to their wide spectral and angular response bandwidths, high diffraction efficiency, and polarization selectivity. However, currently reported CLC reflective PBLs are either limited by monochrome display or suffers from complicated design for colorful display. Herein, we demonstrate a colorful multi-plane AR display system with dynamically tunable reflective PBL. The reflective PBL is fabricated by polymer-stabilized cholesteric liquid crystal (PSCLC) that provides dynamical and continuous tunability of color and focal length by direct current (DC) voltage. A proof-of-concept colorful multi-plane AR device is demonstrated, where over 90% diffraction efficiency at desired wavelength has been obtained. The proposed simple, compact, and light AR display system capable of color-imaging with multi-depth shows great application potential in the vehicle-mounted head-up display (HUD).

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Although the triglyceride form is the natural form of fish oil found in fish, the ethyl ester form of fish oil, which is used during processing to save costs, is also present on the market. In this study, fatty acids and lipids were determined using gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-linear ion trap mass spectrometry (LC-LIT/MS), respectively, according to developed methods. The identification of fatty acids was based on the mass spectral characteristics and equivalent chain lengths. However, the fatty acid contents of both forms of fish oils are quite similar. The application of the LC-LIT/MS method for the structural characterization of triacylglycerols (TAGs) and the mechanism of LIT/MS fragmentation are also discussed. Neutral losses of CH2=CH2 (m/z 28) and CH3CH2OH (m/z 46), which are LIT/MS characteristics of ethyl ester from fish oil, were found for the first time. The triglyceride form of fish oils was easily and accurately identified using fingerprint chromatography. In conclusion, lipid analysis combined with LC-LIT/MS showed an improved capability to distinguish between types of fish oil.

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The fate of dissolved organic matter (DOM) is primarily governed by its sources, degradation, and transformation processes within the environment. However, the influence of metal-DOM complexation on DOM degradation remains ambiguous. In this study, controlled laboratory experiments were conducted using Cu(II) and natural water from the Duliujian River and the Beidagang Wetland to examine the effects of metal-DOM binding on the degradation pathway of DOM. Our results showed that Cu(II)-DOM complexation affected the distribution of DOM molecular weight with elevated Mw after complexed with Cu(II). Nevertheless, the concentration of DOM decreased over the incubation period due to degradation. In the absence of Cu(II) binding, both wetland and river DOM followed similar degradation pathways, transforming from high to low molecular weight with changes predominantly in the 1-10 kDa size-fraction during DOM degradation. In contrast, in the presence of Cu(II) and thus Cu(II)-DOM binding, the degradation of DOM was enhanced, resulting in higher kinetic rate constants for both wetland and river DOM. The results of differential spectra further confirmed the degradation of DOM with a decrease in bulk spectroscopic properties and an increase in the degree of DOM-Cu(II) complexation. These findings imply a mutually reinforcing relationship between metal-DOM complexation and the degradation of DOM in aquatic environments, providing new insights into the biogeochemical behavior and environmental fate of DOM.

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OBJECTIVE: We propose FedScore, a privacy-preserving federated learning framework for scoring system generation across multiple sites to facilitate cross-institutional collaborations. MATERIALS AND METHODS: The FedScore framework includes five modules: federated variable ranking, federated variable transformation, federated score derivation, federated model selection and federated model evaluation. To illustrate usage and assess FedScore's performance, we built a hypothetical global scoring system for mortality prediction within 30 days after a visit to an emergency department using 10 simulated sites divided from a tertiary hospital in Singapore. We employed a pre-existing score generator to construct 10 local scoring systems independently at each site and we also developed a scoring system using centralized data for comparison. RESULTS: We compared the acquired FedScore model's performance with that of other scoring models using the receiver operating characteristic (ROC) analysis. The FedScore model achieved an average area under the curve (AUC) value of 0.763 across all sites, with a standard deviation (SD) of 0.020. We also calculated the average AUC values and SDs for each local model, and the FedScore model showed promising accuracy and stability with a high average AUC value which was closest to the one of the pooled model and SD which was lower than that of most local models. CONCLUSION: This study demonstrates that FedScore is a privacy-preserving scoring system generator with potentially good generalizability.

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Artificial intelligence (AI) has demonstrated the ability to extract insights from data, but the fairness of such data-driven insights remains a concern in high-stakes fields. Despite extensive developments, issues of AI fairness in clinical contexts have not been adequately addressed. A fair model is normally expected to perform equally across subgroups defined by sensitive variables (e.g., age, gender/sex, race/ethnicity, socio-economic status, etc.). Various fairness measurements have been developed to detect differences between subgroups as evidence of bias, and bias mitigation methods are designed to reduce the differences detected. This perspective of fairness, however, is misaligned with some key considerations in clinical contexts. The set of sensitive variables used in healthcare applications must be carefully examined for relevance and justified by clear clinical motivations. In addition, clinical AI fairness should closely investigate the ethical implications of fairness measurements (e.g., potential conflicts between group- and individual-level fairness) to select suitable and objective metrics. Generally defining AI fairness as "equality" is not necessarily reasonable in clinical settings, as differences may have clinical justifications and do not indicate biases. Instead, "equity" would be an appropriate objective of clinical AI fairness. Moreover, clinical feedback is essential to developing fair and well-performing AI models, and efforts should be made to actively involve clinicians in the process. The adaptation of AI fairness towards healthcare is not self-evident due to misalignments between technical developments and clinical considerations. Multidisciplinary collaboration between AI researchers, clinicians, and ethicists is necessary to bridge the gap and translate AI fairness into real-life benefits.

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High positive charge-induced toxicity, easy lysosomal degradation of nucleic acid drugs, and poor lesion sites targeting are major problems faced in the development of gene carriers. Herein, we proposed the concept of self-escape non-cationic gene carriers for targeted delivery and treatment of photocontrolled hepatocellular carcinoma (HCC) with sufficient lysosome escape and multiple response capacities. Functional DNA was bound to the surface of biotin-PEG2000-modified graphitic carbon nitride (Bio-PEG-CN) nanosheets to form non-cationic nanocomplexes Bio-PEG-CN/DNA. These nanocomposites could actively target HCC tissue. Once these nanocomplexes were taken up by tumor cells, the accumulated reactive oxygen species (ROS) generated by Bio-PEG-CN under LED irradiation would disrupt the lysosome structure, thereby facilitating nanocomposites escape. Due to the acidic microenvironment and lipase in the HCC tissue, the reversible release of DNA could be promoted to complete the transfection process. Meanwhile, the fluorescence signal of Bio-PEG-CN could be monitored in real time by fluorescence imaging technology to investigate the transfection process and mechanism. In vitro and in vivo results further demonstrated that these nanocomplexes could remarkably upregulate the expression of tumor suppressor protein P53, increased tumor sensitivity to ROS generated by nanocarriers, and realized effective gene therapy for HCC via loading P53 gene.

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Hypoxia is an important feature, which can upregulate the hypoxia-inducible factor-1α (HIF-1α) expression and promote the activation of hepatic stellate cells (HSCs), leading to liver fibrosis. Currently, effective treatment for liver fibrosis is extremely lacking. Herein, a safe and effective method is established to downregulate the expression of HIF-1α in HSCs via targeted delivery of VA-PEG-modified CNs-based nanosheets-encapsulated (VA-PEG-CN@GQDs) HIF-1α small interfering RNA (HIF-1α-siRNA). Due to the presence of lipase in the liver, the reversible release of siRNA can be promoted to complete the transfection process. Simultaneously, VA-PEG-CN@GQD nanosheets enable trigger the water splitting process to produce O2 under near-infrared (NIR) irradiation, thereby improving the hypoxic environment of the liver fibrosis site and maximizing the downregulation of HIF-1α expression to improve the therapeutic effect, as demonstrated in liver fibrosis mice. Such combination therapy can inhibit the activation of HSCs via HIF-1α-mediated TGF-ß1/Smad pathway, achieving outstanding therapeutic effects in liver fibrosis mice. In conclusion, this study proposes a novel strategy for the treatment of liver fibrosis by regulating the hypoxic environment and the expression of HIF-1α at lesion site.

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The p-i heterojunction imbedded underneath the perovskite layer plays a vital role in determining the efficiency and stability of inverted perovskite solar cells (PSCs). We found that poly[bis(4-phenyl) (2,4,6-trimethylphenyl) amine] (PTAA) suffers from the severely chain entanglement resulting in poor contact with perovskite. In this work, PTAA layer was treated by poly[(2,6-(4,8-bis(5-(2-ethylhexylthio)-4-fluorothiophen-2-yl)-benzo[1,2-b:4,5-b'] dithiophene))-alt-(5,5-(1',3'-di-2-thienyl-5',7'-bis(2-ethylhexyl) benzo[1',2'-c:4',5'-c'] dithiophene-4,8-dione)] (PBDB-T-SF) diluted solution in chlorobenzene. PBDB-T-SF, which contains dual carbonyl groups in its backbones and suitable electronic levels, can spontaneously fill the voids in chlorobenzene-washed PTAA (nano-PTAA). This not only promotes the work function of the substrate but also strengthens the coherence between perovskite and the substrate. Blade coated PSC (0.09 cm2) containing PBDB-T-SF (s-PSCs) realized a power conversion efficiency (PCE) of 21.83 %. After aging for more than 2000 h, s-PSCs maintains 88 % of the initial efficiency which is only 59 % for the control devices.

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OBJECTIVE: The proper handling of missing values is critical to delivering reliable estimates and decisions, especially in high-stakes fields such as clinical research. In response to the increasing diversity and complexity of data, many researchers have developed deep learning (DL)-based imputation techniques. We conducted a systematic review to evaluate the use of these techniques, with a particular focus on the types of data, intending to assist healthcare researchers from various disciplines in dealing with missing data. MATERIALS AND METHODS: We searched five databases (MEDLINE, Web of Science, Embase, CINAHL, and Scopus) for articles published prior to February 8, 2023 that described the use of DL-based models for imputation. We examined selected articles from four perspectives: data types, model backbones (i.e., main architectures), imputation strategies, and comparisons with non-DL-based methods. Based on data types, we created an evidence map to illustrate the adoption of DL models. RESULTS: Out of 1822 articles, a total of 111 were included, of which tabular static data (29%, 32/111) and temporal data (40%, 44/111) were the most frequently investigated. Our findings revealed a discernible pattern in the choice of model backbones and data types, for example, the dominance of autoencoder and recurrent neural networks for tabular temporal data. The discrepancy in imputation strategy usage among data types was also observed. The "integrated" imputation strategy, which solves the imputation task simultaneously with downstream tasks, was most popular for tabular temporal data (52%, 23/44) and multi-modal data (56%, 5/9). Moreover, DL-based imputation methods yielded a higher level of imputation accuracy than non-DL methods in most studies. CONCLUSION: The DL-based imputation models are a family of techniques, with diverse network structures. Their designation in healthcare is usually tailored to data types with different characteristics. Although DL-based imputation models may not be superior to conventional approaches across all datasets, it is highly possible for them to achieve satisfactory results for a particular data type or dataset. There are, however, still issues with regard to portability, interpretability, and fairness associated with current DL-based imputation models.

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Designing and synthesizing advanced electrocatalysts with superior intrinsic activity toward hydrogen evolution reaction (HER) in alkaline media is critical for the hydrogen economy. Herein, a novel Ir@Rhene heterojunction electrocatalyst is synthesized via epitaxially confining ultrasmall and low-coordinate Ir nanoclusters on the ultrathin Rh metallene accompanying the formation of Ir/IrO2 Janus nanoparticles. The as-prepared heterojunctions display outstanding alkaline HER activity, with an overpotential of only 17 mV at 10 mA cm-2 and an ultralow Tafel slope of 14.7 mV dec-1 . Both structural characterizations and theoretical calculations demonstrate that the Ir@Rhene heterointerfaces induce charge density redistribution, resulting in the increment of the electron density around the O atoms in the IrO2 site and thus delivering much lower water dissociation energy. In addition, the dual-site synergetic effects between IrO2 and Ir/Rh interface trigger and improve the interfacial hydrogen spillover, thereby subtly avoiding the steric blocking of the active site and eventually accelerating the alkaline HER kinetics.

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The AutoScore framework can automatically generate data-driven clinical scores in various clinical applications. Here, we present a protocol for developing clinical scoring systems for binary, survival, and ordinal outcomes using the open-source AutoScore package. We describe steps for package installation, detailed data processing and checking, and variable ranking. We then explain how to iterate through steps for variable selection, score generation, fine-tuning, and evaluation to generate understandable and explainable scoring systems using data-driven evidence and clinical knowledge. For complete details on the use and execution of this protocol, please refer to Xie et al. (2020),1 Xie et al. (2022)2, Saffari et al. (2022)3 and the online tutorial https://nliulab.github.io/AutoScore/.

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Background: Traditional Chinese medicines (TCMs), such as Tripterygium wilfordii Hook F (TwHF), Glycyrrhiza uralensis, Caulis sinomenii and others have anti-inflammatory effects. They are widely used in China to treat rheumatoid arthritis (RA), but proof of their use as an evidence-based medicine is little. The aim of this network meta-analysis (NMA) was to evaluate the efficacy and safety of TCMs. Methods: By searching online databases and using a manual retrieval method, randomized controlled trials (RCTs) that met specific selection criteria were included in the meta-analysis. The search included papers that were published between the establishment of the databases and November 10, 2022. Analyses were performed using Stata software (version 14) and Review Manager (version 5.3). Results: 61 papers with 6316 subjects were included in the current NMA. For ACR20, MTX plus SIN therapy (94.30%) may be a significant choice. For ACR50 and ACR70, MTX plus IGU therapy (95.10%, 75.90% respectively) performed better than other therapies. IGU plus SIN therapy (94.80%) may be the most promising way to reduce DAS-28, followed by MTX plus IGU therapy (92.80%) and TwHF plus IGU therapy (83.80%). In the analysis of the incidence of adverse events, MTX plus XF therapy (92.50%) had the least potential, while LEF therapy (22.10%) may cause more adverse events. At the same time, TwHF therapy, KX therapy, XF therapy and ZQFTN therapy were not inferior to MTX therapy. Conclusions: TCMs with anti-inflammatory effect were not inferior to MTX therapy in the treatment of RA patients. Combining with TCMs can improve the clinic efficacy and reduce the possibility of adverse events of DMARDs, which may be a promising regimen. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022313569.

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The environmental behavior of heavy metals in soil is significantly regulated by their binding with dissolved organic matter (DOM), which is affected by soil moisture contents. However, the mechanism of this interaction in soils with varying moisture is still not well understood. Using a combination of ultrafiltration, Cu(II) titration, and multispectral (ultraviolet-visible absorption, 3D fluorescence, Fourier transform infrared) analysis techniques, we studied the differences in the spectral characteristics and Cu(II) binding properties of soil dissolved organic matter (DOM) and its different molecular weight (MW) fractions with moisture gradients. We found that the abundance and spectral characters of soil DOM changed with increasing soil moisture, i.e., the increase in abundance while the decrease in aromaticity and humification index. The components of DOM, shown by Fluorescence region-integration (FRI) analysis, also changed, with an increase in the proportion of protein-like substances and a decrease of humic-like and fulvic-like substances. The overall Cu(II) binding potential of soil DOM diminished with increasing soil moisture, as indicated by the fluorescence parallel factor (PARAFAC) analysis. This is aligns with the changes in DOM composition, as the humic-like and fulvic-like fractions exhibited higher Cu(II) binding potential compared to the protein-like fractions. The low MW fraction of the MW-fractionated samples showed a stronger binding potential for Cu(II) compared to the high MW fraction. Finally, the active binding site of Cu(II) in DOM, as revealed by UV-difference spectroscopy and 2D-FTIR-COS analysis, decreased with increasing soil moisture, with the order of preferentially functional groups shifting from OH, NH, and CO to CN and CO. This study emphasizes the impact of moisture variations on the characteristics of DOM and its interaction with Cu(II), providing insight into the environmental fate of heavy metal contaminants in soil in areas with alternating land and water conditions.