OBJECTIVE To comprehensively evaluate the quality of adverse drug reaction （ADR） reports in clinical departments or ward （hereinafter referred to as “department”） based on game theory combinatorial weighting-technique for order preference by similarity to ideal solution （TOPSIS）-rank-sum ratio （RSR） method， providing a reference for the further standardization of ADR reporting. METHODS Based on relevant documents and scoring criteria， the ADR report quality evaluation standards previously developed by our team were modified. Using game theory principles， the fusion of subjective and objective weights for each indicator was determined. A game theory combinatorial weighting-TOPSIS-RSR model was developed to evaluate and categorize the quality of raw ADR reports submitted by departments to the pharmacy department at Bozhou Hospital of Anhui Medical University. RESULTS A total of 222 ADR reports from 23 departments were included. The game theory combinatorial weighting method identifies weak points in management， such as ADR symptoms and signs， the description of underlying diseases， timing of ADR， by optimizing the weightings of the indicators. The TOPSIS-RSR method calculates that the mean relative closeness of the departments was 0.401 7， indicating that the overall report quality ranged from moderate to substandard. 20095） Three departments， including neurosurgery， demonstrated medium reporting quality [estinate closeness （Ĉ）i ≥0.506]， while two departments， such as the respiratory department，were rated as unqualified （Ĉi＜0.278）. The remaining departments were all deemed qualified （0.278≤ Ĉi＜0.506）. CONCLUSIONS The developed game theory combinatorial weighting-TOPSIS-RSR method provides an effective approach for the quality evaluation of ADR reports， which not only balances subjective and objective weights but also facilitates comparisons among different departments. There is still room for improvement in the ADR report quality at the hospital.

Objective: To investigate the potential mechanisms of Xanthatin in inhibiting the proliferation of laryngeal squamous cell carcinoma(LSCC) cells by integrating network pharmacology and in vitro experiments. Methods: The targets of Xanthatin were identified using databases such as PharmMapper, while disease-related targets for LSCC were obtained from databases such as DisGeNET. The overlapping targets between Xanthatin and LSCC were determined by intersecting these datasets. A protein-protein interaction(PPI) network was constructed based on the overlapping targets, and key targets were identified. Gene ontology(GO) and Kyoto encyclopedia of genes and genomes(KEGG) enrichment analyses of the overlapping targets were performed using R software. A "Xanthatin-target-pathway" network was visualized using Cytoscape 3.8.0 software. The preliminary validation of the aforementioned results was performed using molecular docking and transcriptomics. The effects of Xanthatin on the proliferation of TU177 cells were assessed using CCK-8 and colony formation assays. Additionally, Western blot analysis was employed to measure the expression levels of PI3K, p-PI3K, Akt, and p-Akt proteins. Results: A total of 159 overlapping targets between Xanthatin and LSCC were identified, and seven key targets, including AKT1, were screened. GO enrichment analysis yielded 2 455 entries, and KEGG enrichment analysis identified 172 pathways, such as the PI3K-Akt signaling pathway. Xanthatin exhibited favorable binding activity with the core target proteins of LSCC in molecular docking experiments. The transcriptomics results showed high consistency with the predictions from network pharmacology. CCK-8 and colony formation assays demonstrated that Xanthatin at concentrations of 1, 2, and 4 μmol/L significantly inhibited the proliferation of TU177 cells in a dose-dependent manner. The expression levels of p-PI3K and p-Akt proteins decreased with increasing concentrations of Xanthatin. Conclusion Xanthatin may exert an inhibitory effect on the proliferation of LSCC cells by modulating the PI3K-Akt signaling pathway.