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Recent scientific advances have greatly enhanced our understanding of the complex link between the gut microbiome and cancer. Gut dysbiosis is an imbalance between commensal and pathogenic bacteria and the production of microbial antigens and metabolites. The immune system and the gut microbiome interact to maintain homeostasis of the gut, and alterations in the microbiome composition lead to immune dysregulation, promoting chronic inflammation and development of tumors. Gut microorganisms and their toxic metabolites may migrate to other parts of the body via the circulatory system, causing an imbalance in the physiological status of the host and secretion of various neuroactive molecules through the gut-brain axis, gut-hepatic axis, and gut-lung axis to affect inflammation and tumorigenesis in specific organs. Thus, gut microbiota can be used as a tumor marker and may provide new insights into the pathogenesis of malignant tumors.

Electrochemical C-N coupling of CO 2 with nitrogenous sources (e.g., N 2 , NO 3 − ) provides a promising method for urea production, whereas the current electrochemical methods are limited by low conversion efficiency or reliance on fossil fuel-derived NO 3 − feedstock. Here, we develop a plasma-electrocatalytic route for urea synthesis from ambient air and CO 2 , which starts with plasma-assisted air activation to generate reactive NO x − (92.1% NO 2 − ), followed by electrocatalytic co-reduction of CO 2  + NO x − to urea. By using a single-atom Ru 1 /CuO x catalyst in double chamber membrane electrode assembly, we achieve a urea yield rate of 106.9 mmol h −1  g cat −1 and a Faradaic efficiency of 86.7%. This plasma-electrocatalytic route demonstrates a paradigm-shifting strategy for revolutionizing urea synthesis, making a great leap toward decarbonized nitrogen economy. Current urea synthesis methods are challenged by low C-N coupling efficiency or reliance on fossil fuel-derived NH 3 /NO 3 − . Here, the authors report plasma-electrocatalytic synthesis of urea from air and CO 2 , which enables a high-efficiency C-N coupling and circumvents fossil fuel dependency.

The twin-screw compressor exhibits significant application value in the fields of energy, refrigeration, construction, transportation, and related domains. Owing to the benefits of short cycles and low costs, numerical simulation technology has attracted increasing attention. Over recent years, the numerical simulation technology for twin-screw compressors has advanced rapidly, and many important results have been achieved. This paper comprehensively discusses the modeling method of twin-screw compressors, the meshing technique, advances in numerical simulation of internal flow, the research status of numerical simulation research regarding structural operating conditions, and performance optimization. The synergistic potential between these technologies for improving the performance and efficiency of twin-screw compressors is investigated. The numerical simulation research progress of the internal flow and performance optimization of twin-screw compressors is systematically reviewed. Against the background of global energy saving and carbon reduction, this paper offers readers an in-depth understanding of the technical challenges, research hotspots, and development directions in the related field. It fills the relevant gaps within the current literature. The results highlight the role and potential of deep exploration of the intrinsic relationship between local complex flow characteristics and structural optimization for the performance optimization of twin-screw compressors. For conforming to actual conditions and pertinency, mathematical models such as multiphase flow and turbulence models should be further improved. The current research results remain constrained by the lack of comprehensive consideration of multi-field coupling. In the future development of energy-saving and environment-friendly high-performance twin-screw compressors, numerical simulation research should be developed for high precision, multi-physical field coupling, influencing mechanism research, energy-saving, and environmental friendliness, and intelligence. It establishes a theoretical foundation for further enhancing the performance and mechanism theory of twin-screw compressors.

Idiopathic pulmonary fibrosis (IPF) is an interstitial lung disease with a fatal outcome, known for its rapid progression and unpredictable clinical course. However, the tools available for diagnosing and treating IPF are quite limited. This study aims to identify and screen potential biomarkers for IPF diagnosis, thereby providing new diagnostic approaches. We choosed datasets from the Gene Expression Omnibus (GEO) database, including samples from both IPF patients and healthy controls. For the training set, we combined two gene array datasets (GSE24206 and GSE10667) and utilized GSE32537 as the test set. We identified differentially expressed genes (DEGs) between IPF and normal tissues and determined IPF-related modules using Weighted Gene Co-expression Network Analysis (WGCNA). Subsequently, we employed two machine learning strategies to screen potential diagnostic biomarkers. Candidate biomarkers were quantitatively evaluated using Receiver Operating Characteristic (ROC) curves to identify key diagnostic genes, followed by the construction of a nomogram. Further validation of the expression of these genes through transcriptomic sequencing data from IPF and normal group animal models. Next, we conducted immune infiltration analysis, single-gene Gene Set Enrichment Analysis (GSEA), and targeted drug prediction. Finally, we created an artificial neural network model specifically for IPF. We identified ASPN, COMP, and GPX8 as candidate biomarker genes for IPF, all of which exhibited Area Under the Curve (AUC) above 0.90. These genes were validated by RT-qPCR. Immune infiltration analysis revealed that specific immune cell types are closely related to IPF, suggesting that these immune cells may play a significant role in the pathogenesis of IPF. ASPN, COMP, and GPX8 have been identified as potential diagnostic genes for IPF, and the most relevant immune cell types have been determined. Our research results propose potential biomarkers for diagnosing IPF and present new pathways for investigating its pathogenesis and devising novel therapeutic approaches.

Over the past few years, the combination of solar power with refrigeration technology has matured, providing a promising solution for sustainable cooling. However, a key challenge remains, namely the inherent intermittency of solar energy. Due to its uneven temporal distribution, it is difficult to ensure continuous 24 h operation when relying solely on solar energy. To address this issue, thermal energy storage technology has emerged as a viable solution. This paper presents a comprehensive systematic review of phase-change material (PCM) applications in solar refrigeration systems. It systematically categorizes solar energy conversion methodologies and refrigeration system configurations while elucidating the fundamental operational principles of each solar refrigeration system. A detailed examination of system components is provided, encompassing photovoltaic panels, condensers, evaporators, solar collectors, absorbers, and generators. The analysis further investigates PCM integration strategies with these components, evaluating integration effectiveness and criteria for PCM selection. The critical physical parameters of PCMs are comparatively analyzed, including phase transition temperature, latent heat capacity, specific heat, density, and thermal conductivity. Through conducting a critical analysis of existing studies, this review comprehensively evaluates current research progress within PCM integration techniques, methodological classification frameworks, performance enhancement approaches, and system-level implementation within solar refrigeration systems. The investigation concludes by presenting strategic recommendations for future research priorities based on a comprehensive systematic evaluation of technological challenges and knowledge gaps within the domain.

During solar cell operation, most light energy converts to heat, raising the battery temperature and reducing photoelectric conversion efficiency. Thus, lowering the temperature of solar cells is essential. Nanofluids, with their superior heat transfer capabilities, present a potential solution to this issue. This study investigates the mechanism of enhanced heat transfer by nanofluids in two-dimensional rectangular microchannels using the two-phase lattice Boltzmann method. The results indicate a 3.53% to 22.40% increase in nanofluid heat transfer, with 0.67% to 6.24% attributed to nanoparticle–fluid interactions. As volume fraction (φ) increases and particle radius (R) decreases, the heat transfer capability of the nanofluid improves, while the frictional resistance is almost unaffected. Therefore, the performance evaluation criterion (PEC) of the nanofluid increases, reaching a maximum value of 1.225 at φ = 3% and R = 10 nm. This paper quantitatively analyzes the interaction forces and thermal physical parameters of nanofluids, providing insights into their heat transfer mechanisms. Additionally, the economic feasibility of nanofluids is examined, facilitating their practical application, particularly in solar cell cooling.

Swine wastewater (SW) poses a great threat to the environment due to its high-nutrient profiles if not properly managed. Advanced biological treatment method is an efficient method to treat SW by screening potent microalgae or bacterial strains. In this study, activated sludge, three locally isolated heterotrophic nitrification bacteria and one microalgal strain (Chlorella) were used as inoculums in treating a local SW. Their treatment efficiencies were compared, while the nitrogen removal mechanisms and microbiome profile were explored in detail. It was found that certain heterotrophic nitrification strains had a slight advantage in removing chemical oxygen demand and phosphorus from SW, with the highest removal efficiencies of 83.9% and 76.2%, respectively. The removal efficiencies of ammonia nitrogen and total nitrogen in wastewater by microalgae reached 80.9% and 66.0% respectively, which were far higher than all the heterotrophic nitrification strains. Biological assimilation was the main pathway of nitrogen conversion by microalgae and heterotrophic nitrifying bacteria; especially microalgae showed excellent biological assimilation performance. Correlation analysis showed that Comamonas was highly positively correlated with nitrogen assimilation, while Acidovorax was closely correlated with simultaneous nitrification and denitrification. This study gives a comparison of microalgae and heterotrophic nitrifying bacteria on the nitrogen transfer and transformation pathways.

The frequency domain analysis (FDA) offers greater computational efficiency than the method of characteristic (MOC) in simulating transient flow in pressurized pipes. However, its accuracy is hindered by linearisation errors. Violations of the assumptions for linearisation in friction term and valve equations during water distribution systems (WDSs) burst simulations make the FDA results meaningless. Linearisation procedures are modified as follows using the Heaviside property of pipeline bursts to address this problem: (1) the linearization of the friction term is adjusted, and (2) the valve equation is approximated using a three-step approach. The higher-order term dropped by the original FDA is linearly approximated to achieve better accuracy. The modified FDA is compared to the MOC in a real-life WDS by numerical experiment. Excellent precision can be observed even for a highly nonlinear case where the burst flow is 20% of the initial total demand. The simulation time is significantly shorter than when using the MOC. The proposed modification dramatically improves the applicability of the FDA for solving the nonlinear error issue during the simulation of the pipeline burst. This result implies the potential for its application in quick inverse analysis of pipeline bursts.

The fraud phenomenon is currently widespread in the traditional Chinese medicine Radix Astragali (RA) market, especially where high-quality RA is substituted with low-quality RA. In this case, focused on polysaccharides from RA, the classification models were established for discrimination of RA from different growth patterns, origins, species, and growth years. 1H Nuclear Magnetic Resonance (H1-NMR) was used to establish the spectroscopy of polysaccharides from RA, which were used to distinguish RA via chemical pattern recognition methods. Specifically, orthogonal partial least squares discriminant analysis (OPLS-DA) and linear discriminant analysis (LDA) were used to successfully establish the classification models for RA from different growth patterns, origins, species, and growth years. The satisfactory parameters and high accuracy of internal and external verification of each model exhibited the reliable and good prediction ability of the developed models. In addition, the polysaccharide content and immunological activity were also tested, which was evaluated by the phagocytic activity of RAW 264.7. And the result showed that growth patterns and origins significantly affected the quality of RA. However, there was no significant difference in the aspects of origins and growth years. Accordingly, the developed strategy combined with chemical information, biological activity, and multivariate statistical method can provide new insight for the quality evaluation of traditional Chinese medicine.

Background This study aims to investigate the prevalence and morphology of middle mesial canal (MMC) in mandibular first molar (M1M) among a Northwestern Chinese population, and to analyze their relationship with anatomical aspects of the mesial root. Methods Cone beam computed tomography (CBCT) was utilized to evaluate 898 M1Ms and assess the incidence and morphology of MMC. The following parameters for M1M with or without MMC were obtained: the vertical distance between the first appearance of MMC and canal orifices (D), the distance between mesiobuccal (MB) and mesiolingual (ML) canals (D1), the buccolingual width(L1) and mesiodistal width (L2) of mesial roots, and the flatness degree(L1/L2) of mesial roots. The results were statistically analyzed. Results The prevalence of MMC was 9.6% when considering the number of teeth and 7.2% when considering individuals. The presence of MMC was not significantly associated with sex ( p  = 0.993) or age ( p  = 0.211). Type 1-3-2 emerged as the most prevalent root canal morphology. MMC primarily manifested within 4 mm below the canal orifices. In cases where MMC was present, the MB-ML distance was significantly greater ( p  = 0.017). Conversely, no significant correlation was found between the presence of MMC and the length, width, or flatness degree of the mesial roots. Conclusions The morphology of MMCs is complex, and most of them exhibit confluent canals. In instances where MMCs are present, the MB-ML distance is significantly larger. For effective detection of MMC, a thorough examination of the area within 4 mm beneath the canal orifice is recommended.