Large Language Model Based Semantic Parsing for Intelligent Database Query Engine

With the rapid development of artificial intelligence, large language models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation. These models have great potential to enhance database query systems, enabling more intuitive and semantic query mechanisms. Our model leverages LLM’s deep learning architecture to interpret and process natural language queries and translate them into accurate database queries. The system integrates an LLM-powered semantic parser that translates user input into structured queries that can be understood by the database management system. First, the user query is pre-processed, the text is normalized, and the ambiguity is removed. This is followed by semantic parsing, where the LLM interprets the pre-processed text and identifies key entities and relationships. This is followed by query generation, which converts the parsed information into a structured query format and tailors it to the target database schema. Finally, there is query execution and feedback, where the resulting query is executed on the database and the results are returned to the user. The system also provides feedback mechanisms to improve and optimize future query interpretations. By using advanced LLMs for model implementation and fine-tuning on diverse datasets, the experimental results show that the proposed method significantly improves the accuracy and usability of database queries, making data retrieval easy for users without specialized knowledge.

LIMP-2 enhances cancer stem-like cell properties by promoting autophagy-induced GSK3β degradation in head and neck squamous cell carcinoma

Cancer stem cell-like cells(CSCs)play an integral role in the heterogeneity,metastasis,and treatment resistance of head and neck squamous cell carcinoma(HNSCC)due to their high tumor initiation capacity and plasticity.Here,we identified a candidate gene named LIMP-2 as a novel therapeutic target regulating HNSCC progression and CSC properties.The high expression of LIMP-2 in HNSCC patients suggested a poor prognosis and potential immunotherapy resistance.

Engineering prodrug nanomicelles as pyroptosis inducer for codelivery of PI3K/mTOR and CDK inhibitors to enhance antitumor immunity

Aberrant activation of oncogenic signaling pathways in tumors can promote resistance to the antitumor immune response.However,single blockade of these pathways is usually ineffective because of the complex crosstalk and feedback among oncogenic signaling pathways.The enhanced toxicity of free small molecule inhibitor combinations is considered an insurmountable barrier to their clinical applications.To circumvent this issue,we rationally designed an effective tumor microenvironment-activatable prodrug nanomicelle(PNM)for cancer therapy.PNM was engineered by integrating the PI3K/m TOR inhibitor PF-04691502(PF)and the broad spectrum CDK inhibitor flavopiridol(Flav)into a single nanoplatform,which showed tumor-specific accumulation,activation and deep penetration in response to the high glutathione(GSH)tumoral microenvironment.The codelivery of PF and Flav could trigger gasdermin E(GSDME)-based immunogenic pyroptosis of tumor cells to elicit a robust antitumor immune response.Furthermore,the combination of PNM-induced immunogenic pyroptosis with antiprogrammed cell death-1(a PD-1)immunotherapy further boosted the antitumor effect and prolonged the survival time of mice.Collectively,these results indicated that the pyroptosis-induced nanoplatform codelivery of PI3K/m TOR and CDK inhibitors can reprogram the immunosuppressive tumor microenvironment and efficiently improve checkpoint blockade cancer immunotherapy.