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Methane (CH4) and nitrous oxide (N2O) are major greenhouse gases that are predominantly generated by microbial activities in anoxic environments. N2O inhibition of methanogenesis has been reported, but comprehensive efforts to obtain kinetic information are lacking. Using the model methanogen Methanosarcina barkeri strain Fusaro and digester sludge-derived methanogenic enrichment cultures, we conducted growth yield and kinetic measurements and showed that micromolar concentrations of N2O suppress the growth of methanogens and CH4 production from major methanogenic substrate classes. Acetoclastic methanogenesis, estimated to account for two-thirds of the annual 1 billion metric tons of biogenic CH4, was most sensitive to N2O, with inhibitory constants (KI) in the range of 18-25 μM, followed by hydrogenotrophic (KI, 60-90 μM) and methylotrophic (KI, 110-130 μM) methanogenesis. Dissolved N2O concentrations exceeding these KI values are not uncommon in managed (i.e. fertilized soils and wastewater treatment plants) and unmanaged ecosystems. Future greenhouse gas emissions remain uncertain, particularly from critical zone environments (e.g. thawing permafrost) with large amounts of stored nitrogenous and carbonaceous materials that are experiencing unprecedented warming. Incorporating relevant feedback effects, such as the significant N2O inhibition on methanogenesis, can refine climate models and improve predictive capabilities.

Abstract Background Since 2012, the World Health Organization has recommended seasonal malaria chemoprevention (SMC) with sulfadoxine-pyrimethamine plus amodiaquine (SPAQ) for children aged 3–⁠59 months in regions where malaria transmission is seasonal. Full ingestion of SMC medicines without spitting or vomiting during a complete 3-day course is critical to ensure effectiveness of SMC medicines and to avoid development of antimalarial resistance. Although evidence suggests that spitting or vomiting is not rare, there is limited analytical evidence on potential factors associated with spitting or vomiting in SMC campaigns. Methods We utilized data from SMC coverage surveys conducted in Burkina Faso, Chad, Togo and Nigeria between 2020 and 2022. Episodes of spitting or vomiting were defined as SMC-eligible children spitting out most of the dose or vomiting the entire dose within 30 min of SPAQ administration as reported by primary caregivers. We conducted a cross-sectional study using mixed-effects logistic regression with variables including household socioeconomic variables and caregiver knowledge of SMC, to identify factors associated with spitting or vomiting. Results The proportion of SMC-eligible children spitting or vomiting SPAQ doses ranged from 1.81% in Nigeria to 4.36% in Chad. The odds of spitting or vomiting were lower among children administered medicines under community distributor (CD) supervision, and whose primary caregivers had a high degree of knowledge of SMC. Spitting or vomiting were negatively associated with caregiver adherence to AQ administration and caregiver reporting of children’s adverse reactions to SMC medicines. Over half of the children experiencing a spitting or vomiting episode did not receive a replacement dose from CDs. Redosing was positively associated with caregiver educational attainment, caregiver knowledge of SMC, and directly supervised medicine administration. Conclusions CD-supervised administration of SPAQ can strengthen community engagement strategies to enhance appropriate administration and full ingestion of SMC medicines according to the SMC delivery protocol.

Abstract Behavioral and environmental risk factors are critical in the development and progression of cardiovascular disease (CVD). Understanding the molecular mechanisms underlying these risk factors will offer valuable insights for targeted preventive and therapeutic strategies. Epigenetic modifications, including DNA methylation, histone modifications, chromatin remodeling, noncoding RNA (ncRNA) expression, and epitranscriptomic modifications, have emerged as key mediators connecting behavioral and environmental risk factors to CVD risk and progression. These epigenetic alterations can profoundly impact on cardiovascular health and susceptibility to CVD by influencing cellular processes, development, and disease risk over an individual's lifetime and potentially across generations. This review examines how behavioral and environmental risk factors affect CVD risk and health outcomes through epigenetic regulation. We review the epigenetic effects of major behavioral risk factors (such as smoking, alcohol consumption, physical inactivity, unhealthy diet, and obesity) and environmental risk factors (including air and noise pollution) in the context of CVD pathogenesis. Additionally, we explore epigenetic biomarkers, considering their role as causal or surrogate indicators, and discuss epigenetic therapeutics targeting the mechanisms through which these risk factors contribute to CVD. We also address future research directions and challenges in leveraging epigenetic insights to reduce the burden of CVD related to behavioral and environmental factors and improve public health outcomes. This review aims to provide a comprehensive understanding of behavioral and environmental epigenetics in CVD and offer valuable strategies for therapeutic intervention.

Abstract Introduction Ciprofol is a novel propofol analogue with a characteristic of hemodynamic stability. At present, there is a lack of research comparing the hemodynamic stability of ciprofol and propofol during painless colonoscopy. In this study, we aim to test the hypothesis that ciprofol is superior to propofol in terms of hemodynamic stability for sedation anesthesia in patients undergoing colonoscopy. Methods A total of 222 patients were randomized into two groups. Patients in group P (n = 112) and group C (n = 110) received propofol and ciprofol sedation, respectively. Noninvasive blood pressure were monitored starting from induction (T 0) to the end of the procedure, at 2-min intervals (T 1 to T 10). Heart rate variability (HRV), pain injection, Modified Observer's Assessment of Alertness and Sedation (MOAA/S) score, body movement, doses of norepinephrine, modified Aldrete score, drug-related adverse reactions, and patient satisfaction and endoscopist satisfaction were recorded. Results In group C, fewer patients experienced a decrease in blood pressure with a higher HRV after induction sedation, the incidence of pain injection was reduced, the amount of norepinephrine dose was decreased, patient satisfaction was increased compared with group P (all P

Abstract This systematic review aimed to evaluate the effectiveness of combining radiomic and genomic models in predicting the long-term prognosis of patients with lung cancer and to contribute to the further exploration of radiomics. This study retrieved comprehensive literature from multiple databases, including radiomics and genomics, to study the prognosis of lung cancer. The model construction consisted of the radiomic and genomic methods. A comprehensive bias assessment was conducted, including risk assessment and model performance indicators. Ten studies between 2016 and 2023 were analyzed. Studies were mostly retrospective. Patient cohorts varied in size and characteristics, with the number of patients ranging from 79 to 315. The construction of the model involves various radiomic and genotic datasets, and most models show promising prediction performance with the area under the receiver operating characteristic curve (AUC) values ranging from 0.64 to 0.94 and the concordance index (C-index) values from 0.28 to 0.80. The combination model typically outperforms the single method model, indicating higher prediction accuracy and the highest AUC was 0.99. Combining radiomics and genomics in the prognostic model of lung cancer may improve the predictive performance. However, further research on standardized data and larger cohorts is needed to validate and integrate these findings into clinical practice. Critical relevance statement The combination of radiomics and genomics in the prognostic model of lung cancer improved prediction accuracy in most included studies. Key Points The combination of radiomics and genomics can improve model performance in most studies. The results of establishing prognosis models by different methods are discussed. The combination of radiomics and genomics may be helpful to provide better treatment for patients. Graphical Abstract

Abstract Resilience, the ability to maintain fundamental functionality amidst failures and errors, is crucial for complex networked systems. Most analytical approaches rely on predefined equations for node activity dynamics and simplifying assumptions on network topology, limiting their applicability to real-world systems. Here, we propose ResInf, a deep learning framework integrating transformers and graph neural networks to infer resilience directly from observational data. ResInf learns representations of node activity dynamics and network topology without simplifying assumptions, enabling accurate resilience inference and low-dimensional visualization. Experimental results show that ResInf significantly outperforms analytical methods, with an F1-score improvement of up to 41.59% over Gao-Barzel-Barabási framework and 14.32% over spectral dimension reduction. It also generalizes to unseen topologies and dynamics and maintains robust performance despite observational disturbances. Our findings suggest that ResInf addresses an important gap in resilience inference for real-world systems, offering a fresh perspective on incorporating data-driven approaches to complex network modeling.

Abstract Agent-based modeling and simulation have evolved as a powerful tool for modeling complex systems, offering insights into emergent behaviors and interactions among diverse agents. Recently, integrating large language models into agent-based modeling and simulation presents a promising avenue for enhancing simulation capabilities. This paper surveys the landscape of utilizing large language models in agent-based modeling and simulation, discussing their challenges and promising future directions. In this survey, since this is an interdisciplinary field, we first introduce the background of agent-based modeling and simulation and large language model-empowered agents. We then discuss the motivation for applying large language models to agent-based simulation and systematically analyze the challenges in environment perception, human alignment, action generation, and evaluation. Most importantly, we provide a comprehensive overview of the recent works of large language model-empowered agent-based modeling and simulation in multiple scenarios, which can be divided into four domains: cyber, physical, social, and hybrid, covering simulation of both real-world and virtual environments, and how these works address the above challenges. Finally, since this area is new and quickly evolving, we discuss the open problems and promising future directions. We summarize the representative papers along with their code repositories in https://github.com/tsinghua-fib-lab/LLM-Agent-Based-Modeling-and-Simulation .

Abstract Metabolic comorbidities, such as obesity and diabetes, are associated with subclinical alterations in both cardiac structure/function and natriuretic peptides prior to the onset of heart failure (HF). Despite this, the exact metabolic pathways of cardiac dysfunction which precede HF are not well-defined. Among older individuals without HF in the Multi-Ethnic Study of Atherosclerosis (MESA), we evaluated the associations of 47 circulating metabolites measured by 1H-NMR with echocardiographic measures of cardiac structure and function. We then evaluated associations of significant metabolites with circulating N-terminal pro-B-type natriuretic peptide (NT-proBNP). In a separate cohort, we evaluated differences between top metabolites in patients with HF with preserved ejection fraction (HFpEF) and comorbidity-matched controls. Genetic variants associated with top metabolites (mQTLs) were then related to echocardiographic measures and NT-proBNP. Among 3440 individuals with metabolic and echocardiographic data in MESA (62 ± 10 years, 52% female, 38% White), 10 metabolites broadly reflective of glucose and amino acid metabolism were associated with at least 1 measure of cardiac structure or function. Of these 10 metabolites, 4 (myo-inositol, glucose, dimethylsulfone, carnitine) were associated with higher NT-proBNP and 2 (d-mannose, acetone) were associated with lower NT-proBNP. In a separate cohort, patients with HFpEF had higher circulating myo-inositol levels compared with comorbidity-matched controls. Genetic analyses revealed that 1 of 6 known myo-inositol mQTLs conferred risk of higher NT-proBNP. In conclusion, metabolomic profiling identifies several novel metabolites associated with cardiac dysfunction in a cohort at high risk for HF, revealing pathways potentially relevant to future HF risk.

Abstract Objective To conduct a bibliometric analysis of the prospects and obstacles associated with dual- and multi-energy CT in thoracic disease, emphasizing its current standing, advantages, and areas requiring attention. Methods The Web of Science Core Collection was queried for relevant publications in dual- and multi-energy CT and thoracic applications without a limit on publication date or language. The Bibliometrix packages, VOSviewer, and CiteSpace were used for data analysis. Bibliometric techniques utilized were co-authorship analyses, trend topics, thematic map analyses, thematic evolution analyses, source’s production over time, corresponding author’s countries, and a treemap of authors’ keywords. Results A total of 1992 publications and 7200 authors from 313 different sources were examined in this study. The first available document was published in November 1982, and the most cited article was cited 1200 times. Siemens AG in Germany emerged as the most prominent author affiliation, with a total of 221 published articles. The most represented scientific journals were the “European Radiology” (181 articles, h-index = 46), followed by the “European Journal of Radiology” (148 articles, h-index = 34). Most of the papers were from Germany, the USA, or China. Both the keyword and topic analyses showed the history of dual- and multi-energy CT and the evolution of its application hotspots in the chest. Conclusion Our study illustrates the latest advances in dual- and multi-energy CT and its increasingly prominent applications in the chest, especially in lung parenchymal diseases and coronary artery diseases. Photon-counting CT and artificial intelligence will be the emerging hot technologies that continue to develop in the future. Critical relevance statement This study aims to provide valuable insights into energy-based imaging in chest disease, validating the clinical application of multi-energy CT together with photon-counting CT and effectively increasing utilization in clinical practice. Key Points Bibliometric analysis is fundamental to understanding the current and future state of dual- and multi-energy CT. Research trends and leading topics included coronary artery disease, pulmonary embolism, and radiation dose. All analyses indicate a growing interest in the use of energy-based imaging techniques for thoracic applications. Graphical Abstract

Abstract Background Obstructed labor (OL) and uterine rupture (UR) are common obstetric complications. This study explored the burden, risk factors, decomposition, and health inequalities associated with OL and UR to improve global maternal health. Methods This was a cross-sectional analysis study including data on OL and UR from the Global Burden of Diseases, and Risk Factors Study (GBD) 2019. The main outcome measures included the number and age-standardized rate (ASR) of incidence, disability-adjusted life years (DALYs), prevalence, and deaths. Results The global burden of OL and UR has declined, with a decrease in incidence (number in 2019: 9,410,500.87, 95%UI 11,730,030.94 to 7,564,568.91; ASR in 2019: 119.64 per 100,000, 95%UI 149.15 to 96.21; estimated annual percentage change [EAPC] from 1990 to 2019: -1.34, 95% CI -1.41 to -1.27) and prevalence over time. However, DALYs (number in 2019: 999,540.67, 95%UI 1,209,749.35 to 817,352.49; ASR in 2019: 12.92, 95%UI 15.63 to 10.56; EAPC from 1990 to 2019: -0.91, 95% CI -1.26 to -0.57) and deaths remain significant. ASR of DALYs increased for the 10–14 year-old age group (2.01, 95% CI 1.53 to 2.5), the 15–19 year-old age group (0.07, 95% CI -0.47 to 0.61), Andean Latin America (3.47, 95% CI 3.05 to 3.89), and Caribbean (4.16, 95% CI 6 to 4.76). Iron deficiency was identified as a risk factor for OL and UR, and its impact varied across different socio-demographic indices (SDIs). Decomposition analysis showed that population growth primarily contributed to the burden, especially in low SDI regions. Health inequalities were evident, the slope and intercept for DALYs were − 47.95 (95% CI -52.87 to -43.02) and − 29.29 (95% CI -32.95 to -25.63) in 1990, 39.37 (95%CI 36.29 to 42.45) and 24.87 (95%CI 22.56 to 27.18) in 2019. Concentration indices of ASR-DALYs were − 0.2908 in 1990 and − 0.2922 in 2019. Conclusion This study highlights the significant burden of OL and UR and emphasizes the need for continuous efforts to reduce maternal mortality and morbidity. Understanding risk factors and addressing health inequalities are crucial for the development of effective interventions and policies to improve maternal health outcomes globally.

Abstract Ferroptosis is an iron-dependent cell death form characterized by reactive oxygen species (ROS) overgeneration and lipid peroxidation. Myricetin, a flavonoid that exists in numerous plants, exhibits potent antioxidant capacity. Given that iron accumulation and ROS-provoked dopaminergic neuron death are the two main pathological hallmarks of Parkinson’s disease (PD), we aimed to investigate whether myricetin decreases neuronal death through suppressing ferroptosis. The PD models were established by intraperitoneally injecting 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) into rats and by treating SH-SY5Y cells with 1-methyl-4-phenylpyridinium (MPP+), respectively. Ferroptosis was identified by assessing the levels of Fe2+, ROS, malondialdehyde (MDA), and glutathione (GSH). The results demonstrated that myricetin treatment effectively mitigated MPTP-triggered motor impairment, dopamine neuronal death, and α-synuclein (α-Syn) accumulation in PD models. Myricetin also alleviated MPTP-induced ferroptosis, as evidenced by decreased levels of Fe2+, ROS, and MDA and increased levels of GSH in the substantia nigra (SN) and serum in PD models. All these changes were reversed by erastin, a ferroptosis activator. In vitro, myricetin treatment restored SH-SY5Y cell viability and alleviated MPP+-induced SH-SY5Y cell ferroptosis. Mechanistically, myricetin accelerated nuclear translocation of nuclear factor E2-related factor 2 (Nrf2) and subsequent glutathione peroxidase 4 (Gpx4) expression in MPP+-treated SH-SY5Y cells, two critical inhibitors of ferroptosis. Collectively, these data demonstrate that myricetin may be a potential agent for decreasing dopaminergic neuron death by inhibiting ferroptosis in PD.

Abstract During early embryonic development, the transition from totipotency to pluripotency is a fundamental and critical process for proper development. However, the regulatory mechanisms governing this transition remain elusive. Here, we conducted a comprehensive genome-wide CRISPR/Cas9 screen to investigate the 2-cell-like cells (2CLCs) phenotype in mouse embryonic stem cells (mESCs). This effort led to the identification of ten regulators that play a pivotal role in determining cell fate during this transition. Notably, our study revealed Mdm2 as a significant negative regulator of 2CLCs, as perturbation of Mdm2 resulted in a higher proportion of 2CLCs. Mdm2 appears to influence cell fate through its impact on cell cycle progression and H3K27me3 epigenetic modifications. In summary, the results of our CRISPR/Cas9 screen have uncovered several genes with distinct functions in regulating totipotency and pluripotency at various levels, offering a valuable resource for potential targets in future molecular studies.

Background/Aims: Although endoscopic resection is an effective treatment of rectal neuroendocrine neoplasms (R-NENs) with low malignant potential, there is no consensus on the most recommended endoscopic method. This study aimed to assess the efficacy and acceptability of different endoscopic treatments for R-NENs with low malignant potential. Materials and Methods: We searched databases for studies on treatments of R-NENs using endoscopic resection. These studies comprised techniques such as endoscopic mucosal resection (EMR), endoscopic submucosal dissection (ESD), modified endoscopic mucosal resection (EMRM), modified endoscopic submucosal dissection (ESDM), and transanal endoscopic microsurgery (TEM). The primary outcomes assessed were histological complete resection (HCR). Results: Overall, 38 retrospective studies (3040 R-NENs) were identified. Endoscopic mucosal resection with a cap (EMRC), endoscopic mucosal resection with ligation (EMRL), ESD, ESDM, and TEM demonstrated higher resectability than did EMR in achieving HCR. Endoscopic mucosal resection, EMRC, EMRL, EMRP, EMRD, and EMRU required shorter operation times than did ESD. Endoscopic mucosal resection, EMRC, ESDM, and TEM incurred lower risks than did ESD. Conclusion: Regarding R-NENs ≤20 mm with low malignant potential, ESD could be used as the primary treatment. However, TEM may be more effective if supported by economic conditions and hospital facility. With respect to R-NENs ≤16 mm with low malignant potential, EMRL could be used as the primary treatment. In regard to R-NENs ≤10 mm with low malignant potential, EMRL, EMRC, and ESD could be used as the primary treatment. However, EMRL and EMRC might be better when operational difficulties and economic conditions were considered.

BACKGROUND: Hemorrhagic shock (HS) causes severe organ damage, worsened by high-altitude conditions with lower oxygen and temperatures. Existing research lacks specific insights on brain and heart damage under these conditions. This study hypothesizes that high-altitude and cold (HAC) environments exacerbate HS-induced damage in the brain and heart, aiming to improve treatment strategies. MATERIALS AND METHODS: Twenty-four male Sprague-Dawley (SD) rats (200–250 g of weight) were randomly assigned into sham, HS + normal, HS + HAC (4,000 m), and HS + HAC (6,000 m). The HS model was established in SD rats (35% loss of total blood volume), and histopathological injuries of the brain and heart were detected using hematoxylin and eosin staining, Sirius red staining, and immunohistochemistry. Apoptosis of the brain and heart tissues was detected by terminal transferase-mediated dUTP nick end labeling (TUNEL) immunofluorescence staining. To determine the levels of tumor necrosis factor-α (TNF-α), interferon-gamma (IFN-γ), monocyte chemoattractant protein-1 (Mcp-1), BCL2-associated X (BAX), and myeloid cell leukemia-1 (Mcl-1) protein, western blotting assay was used. RESULTS: The HAC environment induced pathological damage to the brain and heart and aggravated the degree of cardiac fibrosis in HS rats. However, it did not cause apoptosis of the brain and heart. In addition, it upregulated TNF-α, IFN-γ, Mcp-1, and BAX protein levels, but downregulated Mcl-1 protein levels (P < 0.05). CONCLUSIONS: The HAC environment aggravated the degree of brain and heart damage in HS rats, which may be related to neuron nucleus pyknosis, myocardial fibrosis, and inflammatory and apoptosis activation.

Freeze‒thaw induced landslides (FTILs) in grasslands on the Tibetan Plateau are a geological disaster leading to soil erosion. These landslides reduce biodiversity and intensify landscape fragmentation, which in turn are strengthen by the persistent climate change and increased anthropogenic activities. However, conventional techniques for mapping FTILs on a regional scale are impractical due to their labor-intensive, costly, and time-consuming nature. This study focuses on improving FTILs detection by implementing image fusion-based Google Earth Engine (GEE) and a random forest algorithm. Integration of multiple data sources, including texture features, index features, spectral features, slope, and vertical‒vertical polarization data, allow automatic detection of the spatial distribution characteristics of FTILs in Zhidoi county, which is located within the Qinghai‒Tibet Engineering Corridor (QTEC). We employed statistical techniques to elucidate the mechanisms influencing FTILs occurrence. The enhanced method identifies two schemes that achieve high accuracy using a smaller training sample (scheme A: 94.1%; scheme D: 94.5%) compared to other methods (scheme B: 50.0%; scheme C: 95.8%). This methodology is effective in generating accurate results using only ∼10% of the training sample size necessitated by other methods. The spatial distribution patterns of FTILs generated for 2021 are similar to those obtained using various other training sample sources, with a primary concentration observed along the central region traversed by the QTEC. The results highlight the slope as the most crucial feature in the fusion images, accounting for 93% of FTILs occurring on gentle slopes ranging from 0° to 14°. This study provides a theoretical framework and technological reference for the identification, monitoring, prevention and control of FTILs in grasslands. Such developments hold the potential to benefit the management of grassland ecosystem, reduce economic losses, and promote grassland sustainability.

Abstract Background and purpose The persistent progression of pneumonia is a critical determinant of adverse outcomes in patients afflicted with COVID-19. This study aimed to predict personalized COVID-19 pneumonia progression between the duration of two weeks and 1 month after admission by integrating radiological and clinical features. Methods A retrospective analysis, approved by the Institutional Review Board, encompassed patients diagnosed with COVID-19 pneumonia between December 2022 and February 2023. The cohort was divided into training and validation groups in a 7:3 ratio. A trained multi-task U-Net network was deployed to segment COVID-19 pneumonia and lung regions in CT images, from which quantitative features were extracted. The eXtreme Gradient Boosting (XGBoost) algorithm was employed to construct a radiological model. A clinical model was constructed by LASSO method and stepwise regression analysis, followed by the subsequent construction of the combined model. Model performance was assessed using ROC and decision curve analysis (DCA), while Shapley’s Additive interpretation (SHAP) illustrated the importance of CT features. Results A total of 214 patients were recruited in our study. Four clinical characteristics and four CT features were identified as pivotal components for constructing the clinical and radiological models. The final four clinical characteristics were incorporated as well as the RS_radiological model to construct the combined prediction model. SHAP analysis revealed that CT score difference exerted the most significant influence on the predictive performance of the radiological model. The training group’s radiological, clinical, and combined models exhibited AUC values of 0.89, 0.72, and 0.92, respectively. Correspondingly, in the validation group, these values were observed to be 0.75, 0.72, and 0.81. The DCA curve showed that the combined model exhibited greater clinical utility than the clinical or radiological models. Conclusion Our novel combined model, fusing quantitative CT features with clinical characteristics, demonstrated effective prediction of COVID-19 pneumonia progression from 2 weeks to 1 month after admission. This comprehensive model can potentially serve as a valuable tool for clinicians to develop personalized treatment strategies and improve patient outcomes.

With the rapid development of the Internet, digital technology is increasingly integrating into the modern circulation system. The influence mechanisms of land prices on the development of the circulation industry are gradually transforming. Using data from 104 major cities in China from 2003 to 2021, this study uses the panel two-way fixed effects model, moderating effects model and threshold regression method to examine the mechanisms by which land prices affect the circulation industry development and the moderating role of Internet development. The results show that elevated land prices significantly stimulate the growth of the circulation industry, leading to an expansion in the circulation industry scale, optimization of its structure, and enhancement of its facilities. The findings remain robust even after addressing endogeneity concerns and conducting various robustness tests. Moreover, the impact of high land prices on the circulation industry is more pronounced in small and medium-sized cities group as well as central region cities group. Real estate investment and industrial structure upgrading are the key channels through which high land prices promote the development of the circulation industry. Additionally, Internet development has a positive moderating effect on the promotion that high land prices stimulate the development of the circulation industry. Notably, only when Internet development surpasses a certain threshold that the promotional effect of land prices on the circulation industry development be fully realized. Based on the above conclusions, the government should continue to deepen the market-oriented reform of land elements, implement precise industrial policy support for the circulation industry and promote circulation digital transformation.

IntroductionWith the accelerating process of globalization, trade activities have had profound impacts on both the environment and economic development.MethodsThis paper comprehensively evaluates the synergistic effect of bilateral trade on “pollution reduction” and “carbon mitigation,” as well as “economic growth,” utilizing panel data from China and RCEP countries spanning the period from 1997 to 2020.ResultsThe empirical results reveal that bilateral trade significantly propels economic growth in RCEP countries and exhibits a positive “pollution reduction” effect, whereas its “carbon reduction” effect remains uncertain. To delve into the underlying reasons for bilateral trade’s failure to effectively mitigate carbon emissions, this paper undertakes a mechanism test along two opposing paths. Notably, the influence of bilateral trade on economic growth and “pollution reduction” in RCEP countries varies regionally. Specifically, bilateral trade is more efficacious in fostering economic growth in ASEAN countries. Additionally, bilateral trade enhances environmental quality in ASEAN countries, yet it deteriorates in non-ASEAN countries. Furthermore, this paper examines the intricate relationship between bilateral trade, economic growth, and “pollution reduction” through the application of the PVAR model. It concludes that optimizing environmental quality in RCEP countries is conducive to both local economic growth and bilateral trade.ConclusionThis study not only bears significant implications for understanding the complex interplay between economic growth, environmental quality, trade liberalization, and environmental policies, but also provides invaluable guidance for policy formulation and implementation aimed at achieving green transformation and fostering sustainable economic development.

Chemotherapy and radiotherapy in combination with sequence regimens are recognized as the current major strategy for suppressing postoperative tumor recurrence. However, systemic side effects and poor in-field cooperation of the two therapies seriously impair the therapeutic efficacy of patients. The combination of brachytherapy and chemotherapy through innovative biomaterials has proven to be an important strategy to achieve synergistic effects of radiotherapy and chemotherapy in-time and in-field. However, for postoperative chemoradiotherapy, as far as we know, there are few relevant reports. Herein, an injectable pH-responsive polypeptide-polysaccharide depot for concurrent in situ chemotherapy and brachytherapy was developed by encapsulating vincristine into iodine-125 radionuclide labeled hydrogel. This depot hydrogel was prepared by dynamic covalent bonds of Schiff base between aldehydeated hyaluronic acid and polyethylene glycol-polytyrosine. Therefore, this hydrogel enables smart response to tumor acidic microenvironment, rapid release of the encapsulated vincristine and an enhanced uptake effect by tumor cells, which significantly reduces IC50 of vincristine for the anaplasia Wilms' tumor cells in vitro. This depot hydrogel shows excellent stability and biocompatibility, and maintains for 14 days after in situ injection in a postoperative model of anaplasia Wilms' tumor. After injection at the cavity of tumor excision, responsively-released vincristine and the radioactive iodine-125 exerted excellent killing effects on residual tumor cells, inhibiting tumor relapse and liver metastasis of the recurrent tumor. Hence, this study proposes an effective therapeutic strategy for inhibiting anaplasia Wilms’ tumor recurrence, which provides a new approach for concurrent postoperative chemo-radiotherapy and a desirable guidance in regimen execution of pediatric refractory tumors.

Water bodies are crucial components of urban ecology. The development of rapid and timely water-quality assessment tools using easily measured variables is essential for the health management of urban water bodies. In this study, we focused on the dissolved oxygen (DO) of Baiyangdian Lake using 251 sets of empirically measured water quality data and corresponding Sentinel-2 satellite images. Nine machine learning algorithms were then used to develop a rapid detection algorithm for the spatial distribution of the DO concentration in Baiyangdian Lake. This study successfully applied these methods to invert the DO concentration in Baiyangdian Lake during spring, summer, and autumn. The results indicated that extra tree regression (ETR) provided the most accurate and stable results for inverting the DO concentration among the nine machine learning methods. In contrast, AdaBoost regression (ABR), Bayesian ridge regression (BRR), and support vector machines (SVM) exhibit relatively poor regression performance and lack sensitivity to DO concentrations. Moreover, the DO concentration in Baiyangdian Lake ranged from approximately 0 to 12 mg/L, with notable spatiotemporal variations. The highest overall DO concentration was observed in the spring, particularly in the southern region. The DO concentration significantly decreased during summer compared to that in spring, with higher values in the southwestern area and lower values in the northern region. The DO concentration reached its lowest value in autumn, with slightly higher values in the southern region. This study focused on the estimation and inversion of DO concentrations in the water bodies of Baiyangdian Lake. By introducing and comparing the performances of commonly used machine learning models, a rapid estimation of the DO concentration was achieved, thereby overcoming the limitations of traditional water quality monitoring methods in DO inversion. It not only intuitively explained the temporal and spatial variation patterns of DO concentration but also laid a foundation for further in-depth exploration of the interactions between DO and other water quality parameters.