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Background: Based on real-world medical data, the artificial neural network model was used to predict the risk factors of linezolid-induced thrombocytopenia to provide a reference for better clinical use of this drug and achieve the timely prevention of adverse reactions.Methods: The artificial neural network algorithm was used to construct the prediction model of the risk factors of linezolid-induced thrombocytopenia and further evaluate the effectiveness of the artificial neural network model compared with the traditional Logistic regression model.Results: A total of 1,837 patients receiving linezolid treatment in a hospital in Xi ‘an, Shaanxi Province from 1 January 2011 to 1 January 2021 were recruited. According to the exclusion criteria, 1,273 cases that did not meet the requirements of the study were excluded. A total of 564 valid cases were included in the study, with 89 (15.78%) having thrombocytopenia. The prediction accuracy of the artificial neural network model was 96.32%, and the AUROC was 0.944, which was significantly higher than that of the Logistic regression model, which was 86.14%, and the AUROC was 0.796. In the artificial neural network model, urea, platelet baseline value and serum albumin were among the top three important risk factors.Conclusion: The predictive performance of the artificial neural network model is better than that of the traditional Logistic regression model, and it can well predict the risk factors of linezolid-induced thrombocytopenia.

Introduction: Rheumatoid arthritis (RA) is a chronic autoimmune disease, characterized by activated M1-like macrophage in the joint. Xanthium mongolicum Kitag (X. mongolicum) is a traditional medicinal plant that has long been used to treat RA and other immune diseases in China.Methods: Fractions of X. mongolicum were separated based on polarity. Anti-RA activity of the fractions were screened by LPS-stimulated RAW264.7 macrophage in vitro. The major active compounds were identified by UPLC-MS and quantified by HPLC. The anti-RA effects of the active fraction was evaluated in complete freund’s adjuvant (CFA)-induced arthritis and collagen-induced arthritis (CIA) mouse models in vivo and LPS-stimulated macrophage in vitro.Results: Sesquiterpene lactones-enriched fraction from X. mongolicum (SL-XM) exhibited the strongest anti-RA activity among all components in vitro. Five major constituents i.e., Xanthinosin (1), Xanthatin (2), Mogolide D (3), Mogolide E (4), and Mogolide A (5) were identified as major compounds of SL-XM. SL-XM ameliorated symptoms of CFA and CIA induced arthritis mice model. Furthermore, SL-XM treatment inhibited LPS-induced M1 macrophages polarization. In addition, SL-XM inhibited the phosphorylation of NF-κB and MAPK signaling pathways in LPS-induced macrophage and CIA-challenged mice.Discussion: The main anti-RA active fraction of X. mongolicum may be the Sesquiterpene lactones, which includes five key compounds. SL-XM may exert its anti-RA effect by suppressing M1 macrophage polarization via the NF-κB and MAPK signaling pathway.

Vancomycin-associated acute kidney injury (AKI) continues to pose a major challenge to both patients and healthcare providers. The purpose of this study is to construct a machine learning framework for stratified predicting and interpreting vancomycin-associated AKI. Our study is a retrospective analysis of medical records of 724 patients who have received vancomycin therapy from 1 January 2015 through 30 September 2020. The basic clinical information, vancomycin dosage and days, comorbidities and medication, laboratory indicators of the patients were recorded. Machine learning algorithm of XGBoost was used to construct a series risk prediction model for vancomycin-associated AKI in different underlying diseases. The vast majority of sub-model performed best on the corresponding sub-dataset. Additionally, the aim of this study was to explain each model and to explore the influence of clinical variables on prediction. As the results of the analysis showed that in addition to the common indicators (serum creatinine and creatinine clearance rate), some other underappreciated indicators such as serum cystatin and cumulative days of vancomycin administration, weight and age, neutrophils and hemoglobin were the risk factors for cancer, diabetes mellitus, heptic insufficiency respectively. Stratified analysis of the comorbidities in patients with vancomycin-associated AKI further confirmed the necessity for different patient populations to be studied.

Frankincense-Myrrh is a classic drug pair that promotes blood circulation, and eliminates blood stasis. The combination of the two drugs has a definite clinical effect on the treatment of cerebrovascular diseases (CBVDs), but its mechanism of action and compatibility have not been elucidated. In this study, the bioactive components, core targets, and possible synergistic mechanisms of Frankincense-Myrrh in the treatment of CBVDs are explored through systems pharmacology combined with in vivo and in vitro experiments. Comparing target genes of components in Frankincense and Myrrh with CBVD-related genes, common genes were identified; 15 core target genes of Frankincense-Myrrh for the treatment of CBVDs were then identified using protein-protein interaction (PPI) analysis. It was also predicted through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis that the molecular mechanism of Frankincense-Myrrh action on CBVDs was mainly related to the regulation of neurotrophic factors and inflammatory responses. Frankincense-Myrrh significantly improved neurological function, decreased infarct volume, alleviated histopathological damage, inhibited microglial expression, and promoted the expression of neurons in middle cerebral artery occlusion (MCAO)-induced rats. The results of this study not only provide important theoretical support and experimental basis for the synergistic effect of Frankincense-Myrrh, but also provide new ideas for the prevention and treatment of cerebral ischemic injuries.