Objective: Systemic inflammation response index (SIRI) is a new inflammation-based evaluation system that has been reported for predicting survival in multiple tumors, but the prognostic significance of SIRI in cancers has not been evinced.
Methods: Eligible studies updated on December 31, 2019, were selected according to inclusion criteria, the literature searching was performed in PubMed, Web of Science, Google Scholar, and Cochrane. Hazard ratios (HRs), and 95% confidence intervals (CIs) were extracted and pooled by using Stata/SE 14.1.
Results: 11 publications involving 19 cohort studies with a total of 5,605 subjects were included. Meta-analysis results evinced that high SIRI was associated with worse OS (HR = 2.30, 95% CI: 1.87-2.83, ≤ 0.001), poor CSS/DSS (HR = 2.83, 95% CI: 1.98-4.04, ≤ 0.001), and inferior MFS/DFS/PFS/RFS/TTP (HR = 1.88, 95% CI: 1.65-2.15, ≤ 0.001). The association of SIRI with OS was not significantly affected when stratified by diverse confounding factors. It was suggested that tumor patients with high pretreatment SIRI levels would suffer from adverse outcomes.
Conclusion: High SIRI is associated with unfavorable clinical outcomes in human malignancies; pretreatment SIRI level might be a useful and promising predictive indicator of prognosis in cancers.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7479458 | PMC |
http://dx.doi.org/10.1155/2020/8854267 | DOI Listing |
Nat Cancer
January 2025
Institute for Artificial Intelligence in Medicine, University Hospital Essen (AöR), Essen, Germany.
Despite advances in precision oncology, clinical decision-making still relies on limited variables and expert knowledge. To address this limitation, we combined multimodal real-world data and explainable artificial intelligence (xAI) to introduce AI-derived (AID) markers for clinical decision support. We used xAI to decode the outcome of 15,726 patients across 38 solid cancer entities based on 350 markers, including clinical records, image-derived body compositions, and mutational tumor profiles.
View Article and Find Full Text PDFReprod Sci
January 2025
Department of Embryology, Reproductive Biomedicine Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran.
The metabolomic approach has recently been used in the assessment of semen quality and male fertility. Additionally, the crucial roles of branched-chain amino acids (BCAAs) and aromatic amino acids (AAAs) in metabolic syndrome (MetS) were reported. However, little information exists about the association between BCAAs and AAAs with semen parameters, particularly in men with and without MetS.
View Article and Find Full Text PDFNucleic Acids Res
January 2025
Department of Physiology and Biophysics, Virginia Commonwealth University, School of Medicine, Richmond, VA 23298, United States.
The Rep68 protein from Adeno-Associated Virus (AAV) is a multifunctional SF3 helicase that performs most of the DNA transactions necessary for the viral life cycle. During AAV DNA replication, Rep68 assembles at the origin of replication, catalyzing the DNA melting and nicking reactions during the hairpin rolling replication process to complete the second-strand synthesis of the AAV genome. We report the cryo-electron microscopy structures of Rep68 bound to the adeno-associated virus integration site 1 in different nucleotide-bound states.
View Article and Find Full Text PDFEur Heart J Cardiovasc Imaging
January 2025
PULS/e group, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Aims: Image-based, patient-specific rupture risk analysis of AAAs is promising but it is limited by invasive and costly imaging modalities. Ultrasound (US) offers a safe, more affordable alternative, allowing multiple assessments during follow-up and enabling longitudinal studies on AAA rupture risk.
Methods And Results: This study used time-resolved three-dimensional US to assess AAA rupture risk parameters over time, based on vessel and intraluminal thrombus (ILT) geometry.
J Endovasc Ther
January 2025
Department of Vascular Surgery, Rijnstate, Arnhem, The Netherlands.
Purpose: The goal of the study described in this protocol is to build a multimodal artificial intelligence (AI) model to predict abdominal aortic aneurysm (AAA) shrinkage 1 year after endovascular aneurysm repair (EVAR).
Methods: In this retrospective observational multicenter study, approximately 1000 patients will be enrolled from hospital records of 5 experienced vascular centers. Patients will be included if they underwent elective EVAR for infrarenal AAA with initial assisted technical success and had imaging available of the same modality preoperatively and at 1-year follow-up (CTA-CTA or US-US).
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!