Publications by authors named "C Aguayo"
Background/objectives: Antimicrobial resistance (AMR) is a major public health threat, which is exacerbated by the lack of new antibiotics and the emergence of multidrug-resistant (MDR) superbugs. Comprehensive efforts and alternative strategies to combat AMR are urgently needed to prevent social, medical, and economic consequences. is a pathogen responsible for a wide range of infections, from soft tissue infections to life-threatening conditions such as bacteremia and pneumonia.
View Article and Find Full Text PDFCorrection: Prediction model for major bleeding in anticoagulated patients with cancer-associated venous thromboembolism using machine learning and natural language processing.
Clin Transl Oncol
November 2024
Unlabelled: Convalescent Plasma (CP) from patients who recovered from COVID-19 may present neutralizing antibodies against viral protein S of SARS-CoV-2 and emerged as a potential therapeutic alternative for patients with severe infection at the beginning of the COVID-19 pandemic breakout. Thus, this study aimed to evaluate the effect and safety of CP treatment in patients with severe COVID-19.
Methods: We designed a quasi-experimental study that included 156 patients with SARS-CoV-2 infection confirmed by RT-qPCR and severe symptoms who received CP.
Acute myocardial infarction (AMI) results from vulnerable plaque rupture, causing ischemic cardiomyocyte necrosis and intense inflammation. Paradoxically, this inflammation releases factors that aid heart repair. Recent findings suggest a role for extracellular vesicles (EVs) in intercellular communication during post-AMI cardiac repair.
View Article and Find Full Text PDFArticle Synopsis
- A predictive model was created to estimate the risk of major bleeding in cancer patients undergoing anticoagulant treatment for venous thromboembolism (VTE) within six months following their diagnosis.
- The study analyzed data from electronic health records across nine hospitals in Spain, using natural language processing and machine learning to identify key predictors of bleeding and develop various predictive algorithms.
- Findings indicated that about 10.9% of the patients experienced major bleeding events after VTE diagnosis, with significant predictors being factors like hemoglobin levels and age, and the new models outperformed the existing CAT-BLEED score.