Background : We explored the efficacy and main biological mechanism of geniposide intervention in sepsis. Methods : A sepsis model was established in male BALB/c mice through cecal ligation and puncture (CLP). Different doses of geniposide (20 or 40 mg/kg) were administered intravenously at 0 and/or 24 h after CLP surgery. The survival rate of different groups was observed. In addition, the expression levels of CD16 and major histocompatibility complex class II in monocytes were assessed using flow cytometry. The concentrations of TNF-α, IL-1β, IL-6, and IL-10 in the serum were measured by ELISA. We also observed the biological effects of geniposide on CD16 and MHC-II expression levels in RAW264.7 cells, as well as the secretion of TNF-α, IL-1β, IL-6, and IL-10 in the LPS-induced RAW264.7 cell model. The PPARγ levels were determined using western blot analysis. Results : Intravenous administration of 40 mg/kg of geniposide at 0 h after CLP significantly improved the survival outcomes in the septic mouse model, with no significant benefits from low dosing (20 mg/kg) or delayed administration (24 h). The effective dose of geniposide significantly decreased the serum cytokine TNF-α, IL-1β, IL-6, and IL-10 concentrations in septic mice ( P < 0.05). Notably, in vitro assays showed that geniposide specifically increased the IL-10 level. Geniposide significantly reduced the CD16 expression ( P < 0.05) and increased MHC-II expression in monocytes ( P < 0.05). In addition, geniposide elevated the PPARγ level in monocytes ( P < 0.05). Conclusions : High-dose early-stage geniposide administration significantly improved the survival rate in a CLP mouse sepsis model by modulating the monocyte phenotype and regulating the cytokine network (IL-6/IL-10 levels). The pharmacological mechanism of geniposide action might be exerted primarily through PPARγ upregulation.
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http://dx.doi.org/10.1097/SHK.0000000000002239 | DOI Listing |
Mol Genet Genomics
January 2025
Department of Emergency, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, 441000, China.
Acute kidney injury (AKI) is one of the most serious and common complications in the course of sepsis, known for its poor prognosis and high mortality rate. Recently, ferroptosis, as a newly discovered regulatory cell death, might be closely associated with the progression of AKI. METTL14 is a writer of RNA m6A, an abundant epigenetic modification in transcriptome with broad function.
View Article and Find Full Text PDFMol Oncol
January 2025
Department of Gastrointestinal Cancer Translational Research, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, China.
Gastric cancer (GC) is the third leading cause of cancer-related deaths worldwide, with gastrectomy being the primary treatment option. Sepsis, a systemic inflammatory response to infection, may influence tumor growth by creating an immunosuppressive environment conducive to cancer cell proliferation and metastasis. Here, the effect of abdominal infection on tumor growth and metastasis was investigated through the implementation of a peritoneal metastasis model and a subcutaneous tumor model.
View Article and Find Full Text PDFClin Transl Sci
January 2025
Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
This study aimed to develop and validate a nomogram based on lymphocyte subtyping and clinical factors for the early and rapid prediction of Intra-abdominal candidiasis (IAC) in septic patients. A prospective cohort study of 633 consecutive patients diagnosed with sepsis and intra-abdominal infection (IAI) was performed. We assessed the clinical characteristics and lymphocyte subsets at the onset of IAI.
View Article and Find Full Text PDFFront Med (Lausanne)
January 2025
Hepatobiliary Pancreatic Surgery Department, Huadu District People's Hospital of Guangzhou, Guangzhou, China.
Background: Sepsis is a life-threatening disease associated with a high mortality rate, emphasizing the need for the exploration of novel models to predict the prognosis of this patient population. This study compared the performance of traditional logistic regression and machine learning models in predicting adult sepsis mortality.
Objective: To develop an optimum model for predicting the mortality of adult sepsis patients based on comparing traditional logistic regression and machine learning methodology.
Front Med (Lausanne)
January 2025
Department of Stomatology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Sepsis remains a leading cause of morbidity and mortality worldwide due to its rapid progression and heterogeneous nature. This review explores the potential of Artificial Intelligence (AI) to transform sepsis management, from early detection to personalized treatment and real-time monitoring. AI, particularly through machine learning (ML) techniques such as random forest models and deep learning algorithms, has shown promise in analyzing electronic health record (EHR) data to identify patterns that enable early sepsis detection.
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