Connection

Francis Spinale to Biomarkers

This is a "connection" page, showing publications Francis Spinale has written about Biomarkers.
Connection Strength

1.035
  1. Biomarkers and heart disease: what is translational success? J Cardiovasc Transl Res. 2013 Aug; 6(4):447-8.
    View in: PubMed
    Score: 0.295
  2. Cytokine Signaling and Matrix Remodeling Pathways Associated with Cardiac Sarcoidosis Disease Activity Defined Using FDG PET Imaging. Int Heart J. 2021 Sep 30; 62(5):1096-1105.
    View in: PubMed
    Score: 0.131
  3. Development of a biomarker panel to predict cardiac resynchronization therapy response: Results from the SMART-AV trial. Heart Rhythm. 2019 05; 16(5):743-753.
    View in: PubMed
    Score: 0.108
  4. Integrating the myocardial matrix into heart failure recognition and management. Circ Res. 2013 Aug 30; 113(6):725-38.
    View in: PubMed
    Score: 0.075
  5. Preoperative steroid treatment does not improve markers of inflammation after cardiac surgery in neonates: results from a randomized trial. J Thorac Cardiovasc Surg. 2014 Mar; 147(3):902-8.
    View in: PubMed
    Score: 0.074
  6. Plasma biomarkers that reflect determinants of matrix composition identify the presence of left ventricular hypertrophy and diastolic heart failure. Circ Heart Fail. 2011 May; 4(3):246-56.
    View in: PubMed
    Score: 0.063
  7. Plasma profiling determinants of matrix homeostasis in paediatric dilated cardiomyopathy. Cardiol Young. 2011 Feb; 21(1):52-61.
    View in: PubMed
    Score: 0.061
  8. Circulating matrix metalloproteinase levels after ventricular septal defect repair in infants. J Thorac Cardiovasc Surg. 2010 Dec; 140(6):1257-65.
    View in: PubMed
    Score: 0.060
  9. Specific temporal profile of matrix metalloproteinase release occurs in patients after myocardial infarction: relation to left ventricular remodeling. Circulation. 2006 Sep 05; 114(10):1020-7.
    View in: PubMed
    Score: 0.046
  10. Plasma matrix metalloproteinase and inhibitor profiles in patients with heart failure. J Card Fail. 2002 Dec; 8(6):390-8.
    View in: PubMed
    Score: 0.036
  11. Interpretable machine learning predicts cardiac resynchronization therapy responses from personalized biochemical and biomechanical features. BMC Med Inform Decis Mak. 2022 10 31; 22(1):282.
    View in: PubMed
    Score: 0.035
  12. Ensemble machine learning model identifies patients with HFpEF from matrix-related plasma biomarkers. Am J Physiol Heart Circ Physiol. 2022 05 01; 322(5):H798-H805.
    View in: PubMed
    Score: 0.034
  13. Plasma profiles of matrix metalloproteinases and tissue inhibitors of the metalloproteinases predict recurrence of atrial fibrillation following cardioversion. J Cardiovasc Transl Res. 2013 Aug; 6(4):528-35.
    View in: PubMed
    Score: 0.018
Connection Strength

The connection strength for concepts is the sum of the scores for each matching publication.

Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.