Publications by authors named "D B Corrigan"

Article Synopsis
  • Aspartate aminotransferase (AST), alanine aminotransferase (ALT), and other liver biomarkers play crucial roles in diagnosing and monitoring liver conditions like cirrhosis and fatty liver disease.
  • Accurate detection of these biomarkers is essential for understanding liver health, and there are various analyzers and biosensors for testing them.
  • Electrochemical detection of these biomarkers is highlighted as the most effective method due to its simplicity, low cost, and high sensitivity, making it suitable for future research and development.
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Electrochemical detection methods hold many advantages over their optical counterparts, such as operation in complex sample matrices, low-cost and high volume manufacture and possible equipment miniaturisation. Despite these advantages, the use of electrochemical detection is currently limited in the clinical setting. There is a wide range of potential electrode materials, selected for optimal signal-to-noise ratios and reproducibility when detecting target analytes.

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Earlier access to patients' biomarker status could transform disease management. However, gold-standard techniques such as enzyme-linked immunosorbent assays (ELISAs) are typically not deployed at the point-of-care due to their cumbersome instrumentation and complexity. Electrochemical immunosensors can be disruptive in this sector with their small size and lower cost but, without further modifications, the performance of these sensors in complex media (e.

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Article Synopsis
  • The study aims to improve the histological grading system for prostate cancer to better predict metastatic potential by focusing on specific high-risk patterns known as 'unfavourable histology.'
  • Two patient cohorts were analyzed, one with long-term follow-up and the other with confirmed metastatic disease, to assess the impact of unfavourable histology on outcomes like biochemical recurrence and death.
  • The findings showed that unfavourable histology significantly predicts outcomes, with high sensitivity for predicting recurrence and metastasis, suggesting that incorporating this model could enhance current grading practices in prostate cancer.
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