Background: Ovarian cancer (OC) is a commonly diagnosed female cancer around the world. The Chinese herbal medicine has an anti-cancer effect. However, there is no relevant report on whether is effective in treating OC, and the corresponding mechanism is also unknown.
Objective: This study was projected to excavate the active components and underpinned molecular mechanisms of in treating ovarian cancer (OC) through network pharmacology combined with in vitro experiments.
Methods: The essential active components of were selected using the TCMSP database. The OC-related targets were selected by GeneCards, intersecting targets were obtained by Venn Diagram. The core targets were obtained through the PPI network and Cytoscape, and the key pathway was gained through GO and KEGG enrichment analyses. Meanwhile, docking conformation was observed as reflected by molecular docking. MTT, colony formation assay and flow cytometer (FCM) analysis were performed to determine cell proliferation and apoptosis, respectively. Finally, Levels of various signaling proteins were evaluated by western blotting.
Results: Luteolin, β-sitosterol and their corresponding targets were selected as the essential active components of . 76 intersecting targets were obtained by Venn Diagram. TP53, AKT1, and TNF were obtained through the PPI network and Cytoscape, and the key pathway PI3K/AKT was gained through GO and KEGG enrichment analyses. A good docking conformation was observed between luteolin and AKT1. Luteolin could hinder A2780 cell proliferation, induce cell apoptosis and enhance the inhibition of the PI3K/AKT pathway.
Conclusion: It was verified in vitro that luteolin could hinder OC cell proliferation and activate the PI3K/AKT pathway to lead to apoptosis.
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http://dx.doi.org/10.2174/1386207326666230627114111 | DOI Listing |
Purpose: To provide updated guidance regarding neoadjuvant chemotherapy (NACT) and primary cytoreductive surgery (PCS) among patients with stage III-IV epithelial ovarian, fallopian tube, or primary peritoneal cancer (epithelial ovarian cancer [EOC]).
Methods: A multidisciplinary Expert Panel convened and updated the systematic review.
Results: Sixty-one studies form the evidence base.
Purpose: Clinical variables alone have limited ability to determine which patients will have recurrence after radical prostatectomy (RP). We evaluated the ability of locked multimodal artificial intelligence (MMAI) algorithms trained on prostate biopsy specimens to predict prostate cancer specific mortality (PCSM) and overall survival (OS) among patients undergoing radical prostatectomy with digitized RP specimens.
Materials And Methods: The Prostate, Lung, Colorectal, and Ovarian Cancer Screening Randomized Controlled Trial randomized subjects from 1993-2001 to cancer screening or control.
J Ultrasound
January 2025
, Costa Contina street n. 19, 66054, Vasto, Chieti, Italy.
Aim: o point out how novel analysis tools of AI can make sense of the data acquired during OL and OC diagnosis and treatment in an effort to help improve and standardize the patient pathway for these disease.
Material And Methods: ultilizing programmed detection of heterogeneus OL and OC habitats through radiomics and correlate to imaging based tumor grading plus a literature review.
Results: new analysis pipelines have been generated for integrating imaging and patient demographic data and identify new multi-omic biomarkers of response prediction and tumour grading using cutting-edge artificial intelligence (AI) in OL and OC.
Ann Surg Oncol
January 2025
Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Background: Hematologic changes after splenectomy and hyperthermic intraperitoneal chemotherapy (HIPEC) can complicate postoperative assessment of infection. This study aimed to develop a machine-learning model to predict postoperative infection after cytoreductive surgery (CRS) and HIPEC with splenectomy.
Methods: The study enrolled patients in the national TriNetX database and at the Johns Hopkins Hospital (JHH) who underwent splenectomy during CRS/HIPEC from 2010 to 2024.
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