Revealing the potential role of hub metabolism‐related genes and their correlation with immune cells in acute ischemic stroke

Abstract Objectives Acute ischemic stroke (AIS) is caused by cerebral ischemia due to thrombosis in the blood vessel. The purpose of this study is to identify key genes related to metabolism to aid in the mechanism research and management of AIS. Materials and Methods Gene expression data were downloaded from the Gene Expression Omnibus database. Weighted gene co‐expression network analysis, Gene Ontology and kyoto encyclopedia of genes and genomes analysis were used to identify metabolism‐related genes that may be involved in the regulation of AIS. A protein protein interaction network was mapped using Cytoscape based on the STRING database. Subsequently, hub metabolism‐related genes were identified based on Cytoscape‐CytoNCA and Cytoscape‐MCODE plug‐ins. Least absolute shrinkage and selection operator algorithm and differential expression analysis. In addition, drug prediction, molecular docking, ceRNA network construction, and correlation analysis with immune cell infiltration were performed to explore their potential molecular mechanisms of action in AIS. Finally, the expression of hub gene was verified by real‐time PCR. Results Metabolism‐related genes FBL, HEATR1, HSPA8, MTMR4, NDUFC1, NDUFS8 and SNU13 were identified. The AUC values of FBL, HEATR1, HSPA8, MTMR4, NDUFS8 and SNU13 were all greater than 0.8, suggesting that they had good diagnostic accuracy. Correlation analysis found that their expression levels were also related to the infiltration levels of multiple immune cells, such as Activated.CD8.T.cell and Activated.dendritic.cell. It was found that only HSPA8 was successfully matched to drugs with literature support, and these drugs were acetaminophen, bupivacaine, dexamethasone, gentamicin, tretinoin and cisplatin. Moreover, it was also identified that the ENSG000000218510‐hsa‐miR‐330‐3p‐HEATR1 axis may be involved in regulating AIS. Conclusions The identification of FBL, HEATR1, HSPA8, MTMR4, NDUFC1, NDUFS8 and SNU13 provides a new research direction for exploring the molecular mechanisms of AIS, which can help in clinical management and diagnosis

ECON: On the Detection and Resolution of Evidence Conflicts

The rise of large language models (LLMs) has significantly influenced the quality of information in decision-making systems, leading to the prevalence of AI-generated content and challenges in detecting misinformation and managing conflicting information, or “inter-evidence conflicts.” This study introduces a method for generating diverse, validated evidence conflicts to simulate real-world misinformation scenarios. We evaluate conflict detection methods, including Natural Language Inference (NLI) models, factual consistency (FC) models, and LLMs, on these conflicts (RQ1) and analyze LLMs’ conflict resolution behaviors (RQ2). Our key findings include: (1) NLI and LLM models exhibit high precision in detecting answer conflicts, though weaker models suffer from low recall; (2) FC models struggle with lexically similar answer conflicts, while NLI and LLM models handle these better; and (3) stronger models like GPT-4 show robust performance, especially with nuanced conflicts. For conflict resolution, LLMs often favor one piece of conflicting evidence without justification and rely on internal knowledge if they have prior beliefs

Nonflammable Electrolyte Based on Fluoroethylene Carbonate for High-Voltage LiCoO₂/Si–Graphite Lithium-Ion Batteries

An electrolyte for high-voltage LiCoO2/Si–graphite pouch cells is proposed in this work, which is composed of LiPF6 salt and fluoroethylene carbonate solvent as well as tris(trimethylsilyl) phosphate and (ethoxy)pentafluorocyclotriphosphazene additives. Tris(trimethylsilyl) phosphate additive plays a role in improving the interfacial properties of electrodes, and (ethoxy)pentafluorocyclotriphosphazene additive makes this electrolyte nonflammable. Its conductivity is as high as 5.48 mS cm–1 at 25 °C. This safe electrolyte also enables the high-voltage LiCoO2/Si–graphite pouch cells to obtain the ideal electrochemical performances. At room temperature, the capacity retention reaches 81.7% after 200 cycles at 1C, and the discharge capacity at 3C still retains about 77% of the capacity at 1C. Furthermore, the interfacial properties of electrodes are analyzed by scanning electron microscopy and X-ray photoelectron spectroscopy measurements