Introduction: Nonalcoholic steatohepatitis (NASH), also known as metabolic steatohepatitis, is a clinical syndrome with pathological changes like alcoholic hepatitis but without a history of excessive alcohol consumption. NASH is closely related to metabolic disorders such as obesity, insulin resistance, type 2 diabetes mellitus, and hyperlipidemia. Its main characteristics are hepatocyte steatosis with hepatocyte injury and inflammation. In severe cases, it can develop into liver cirrhosis. At present, there is no special treatment for NASH. Theabrownin (TB) is the main pigment substance in fermented tea. Theabrownin has beneficial effects on lipid metabolism and intestinal flora. However, the effect of theabrownin on NASH has not been studied. Methods: This study was aimed at exploring the effects of theabrownin from Fuzhuan brick tea on NASH. 8-week-old mice were randomly assigned to three groups and fed with chow diet (CD), methionine and choline sufficient (MCS) diet (MCS Ctrl), which is a Methionine/choline deficient (MCD) control diet, and MCD diet. After 5 weeks of feeding, the MCD group mice were randomly divided into two groups and were gavaged with double distilled water (MCD Ctrl) or theabrownin (MCD TB) (200mg/kg body weight, dissolved in double distilled water) every day for another 4 weeks respectively, while continuing MCD diet feeding.Results: We found that theabrownin treatment could not improve liver mass loss and steatosis. However, theabrownin ameliorated liver injury and decreased liver inflammatory response. Theabrownin also alleviated liver oxidative stress and fibrosis. Furthermore, our results showed that theabrownin increased hepatic level of fibroblast growth factor 21 (FGF21) and reduced the phosphorylation of mitogen-activated protein kinase p38 in MCD diet-fed mice.

Recommender systems have long struggled with challenges such as cold start and data sparsity, which can lead to poor recommendation performance. While previous approaches have attempted to address these issues by incorporating side information, they often introduce noise, lack flexibility for data expansion, and suffer from inconsistent data quality—factors that hinder accurate user preference inference and reduce recommendation performance. With the vast knowledge bases and advanced reasoning capabilities of large language models (LLMs), these models are particularly well-suited to supplement auxiliary information and capture implicit user intent. To address these challenges, we propose a novel framework, ER2ALM, which leverages the capabilities of LLMs enhanced by Retrieval-Augmented Generation (RAG) to improve recommendation outcomes. Our framework specifically addresses the challenges by flexibly and accurately augmenting auxiliary information and capturing users’ implicit preferences and interests. Additionally, to mitigate the risk of introducing noise, we incorporate a noise reduction strategy to ensure the reliability of the augmented information. Experimental validation on two real-world datasets demonstrates the efficacy of our approach, significantly enhancing both the accuracy and robustness of recommendations compared to state-of-the-art methods. This demonstrates the potential of our framework as a new paradigm for preference mining in recommendation systems.

Fire emergencies pose significant risks to occupants in high-rise buildings, particularly individuals with functional limitations who struggle with conventional evacuation facilities like stairs. The objective of the study was to survey current literature to identify safe fire evacuation solutions for functionally limited groups. A systematic analysis of 156 journal articles (2000–2024) was conducted to identify factors affecting the evacuation, and their impact on evacuation efficiency. The findings were categorized into four main types: (1) human behavior during fire emergencies, (2) architectural and environmental factors, (3) fire and smoke risk calculation and control, and (4) evacuation models and tools. Additionally, our findings highlight the limitations of current research for individuals with disabilities, including evacuation methods, building design, fire risk calculation and control, evacuation models, and elevator operation strategies. The study concludes with recommendations for future research to address the identified gaps. This study underscores the need for further research on expanding solutions for different emergencies (e.g., earthquakes), addressing special building environments (e.g., hospitals), and leveraging digital technologies to improve evacuation processes for vulnerable populations. Future efforts will focus on incorporating rescuers and rescue methodologies into the evacuation framework to further enhance the safety and protection of vulnerable populations.

The construction supply chain confronts interruption risks that raise significant concerns regarding industry safety and stability. Consequently, exploring risk management strategies from both enterprise and supply chain network perspectives is crucial. This study employs complex network theory and the cascade failure model to propose a methodology tailored to the unique characteristics of the construction supply chain, facilitating the identification of key risk nodes and the conduct of invulnerability analyses. By evaluating the importance of construction enterprise nodes and their risk propagation ability during cascade failures, this method enables the comprehensive identification of key risk node enterprises within the construction supply chain network. Furthermore, this study examines and discusses strategies for enhancing network invulnerability by taking into account node capacity, load, and resilience. Empirical results indicate that the key nodes and risk nodes in the construction supply chain network are mainly located upstream and downstream, displaying specific distribution patterns. In addition to core enterprises, key risk nodes comprise some strong suppliers at the intermediary and lower tiers of the supply chain. Adjustments to node enterprise parameters like capacity, load, and resilience have diverse impacts on the invulnerability of the construction supply chain network. This study clarifies the distribution patterns of key risk nodes within the construction supply chain network and the variations in network invulnerability under particular conditions, providing valuable insights for risk management decision-making.

An improved SIRS model is constructed as a supply chain risk propagation model, based on the actual situation that node enterprises are eliminated in the supply chain network. From the perspective of the small-world network, simulation is carried out to analyze the risk transmission mechanism in the supply chain network and the influence of various parameters on the supply chain risk transmission and the node enterprises in the supply chain network. Research shows that the process of eliminating node enterprises is also a process of supply chain network’s adjustment and gradual stability. In order to reduce the negative impact of this process on the supply chain network, node enterprises should take effective measures based on their own conditions to improve risk prevention capabilities, coordinate risk transmission, and enhance risk immunity, so as to improve the resistance of the entire supply chain to risks.

Background: The trends in usage of tumor markers, including CEA, SCC, NSE, Cyfra21‐1, and ProGRP, in Chinese lung cancer patients in the real‐world setting are not fully investigated.Methods: A retrospective descriptive study was conducted using the database of Qilu Hospital of Shandong University, China between January 2013 and December 2017, involving patients primarily diagnosed with NSCLC or SCLC. Utilization trends by first discharge year, utilization rates within different durations before and after first discharge date, and combined utilization patterns of multiple tumor markers were analyzed.Results: The utilization of all these tumor markers showed increased from 2013 to 2017. CEA, Cyfra21‐1, and NSE were the most frequently detected, which increased slightly from around 50% in 2013 to around 78% in 2017 in NSCLC and from around 70% in 2013 to around 92% in 2017 in SCLC. CEA, Cyfra21‐1, and NSE were the most commonly measured within 3 months before first diagnosis with approximately 65% in NSCLC and 80% in SCLC, and ProGRP had the lowest utilization (around 30%). CEA, NSE, and Cyfra21‐1 had the highest utilization rates after first diagnosis with both around 80% in NSCLC or SCLC. Combined usage of five tumor markers was ranked the first pattern in combined utilization.Conclusions: This study suggests CEA, Cyfra21‐1, and NSE are the most frequently detected before or after first diagnosis of NSCLC or SCLC. However, SCC and ProGRP tests appeared to have relatively low usages. The utilization pattern was consistent with recommendations of guideline, but underutilization still existed.