Publications by authors named "Agathe Truchot"

Article Synopsis
  • The study investigates how competing risks, like allograft failure and death with a functioning graft, affect the performance of prognostic models used for kidney transplant recipients.
  • The research involves 11,046 kidney transplant recipients across 10 countries, developing models using various regression techniques to predict long-term graft failure while carefully evaluating their accuracy and reliability.
  • Results indicate that both standard Cox models and competing risk models provide similar predictions for graft failure, with high concordance indices, confirming their usefulness in clinical settings.
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Significance Statement: Why are there so few biomarkers accepted by health authorities and implemented in clinical practice, despite the high and growing number of biomaker studies in medical research ? In this meta-epidemiological study, including 804 studies that were critically appraised by expert reviewers, the authors have identified all prognostic kidney transplant biomarkers and showed overall suboptimal study designs, methods, results, interpretation, reproducible research standards, and transparency. The authors also demonstrated for the first time that the limited number of studies challenged the added value of their candidate biomarkers against standard-of-care routine patient monitoring parameters. Most biomarker studies tended to be single-center, retrospective studies with a small number of patients and clinical events.

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Understanding sex differences in graft outcomes within the course of kidney transplantation is needed to unravel factors leading to the observed disparities and further improve patient management. In this issue, Vinson et al. presented a relative survival analysis comparing the excess risk of mortality in female and male recipients after kidney transplantation.

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Machine learning (ML) models have recently shown potential for predicting kidney allograft outcomes. However, their ability to outperform traditional approaches remains poorly investigated. Therefore, using large cohorts of kidney transplant recipients from 14 centers worldwide, we developed ML-based prediction models for kidney allograft survival and compared their prediction performances to those achieved by a validated Cox-Based Prognostication System (CBPS).

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Background: The COVID-19 pandemic has severely affected health systems and medical research worldwide but its impact on the global publication dynamics and non-COVID-19 research has not been measured. We hypothesized that the COVID-19 pandemic may have impacted the scientific production of non-COVID-19 research.

Methods: We conducted a comprehensive meta-research on studies (original articles, research letters and case reports) published between 01/01/2019 and 01/01/2021 in 10 high-impact medical and infectious disease journals (New England Journal of Medicine, Lancet, Journal of the American Medical Association, Nature Medicine, British Medical Journal, Annals of Internal Medicine, Lancet Global Health, Lancet Public Health, Lancet Infectious Disease and Clinical Infectious Disease).

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Three dimensional cell culture systems better reflect the native structural architecture of tissues and are attractive to investigate cancer cell sensitivity to drugs. We have developed and compared several metastatic melanoma (MM) models cultured as a monolayer (2D) and cocultured on three dimensional (3D) dermal equivalents with fibroblasts to better unravel factors modulating cell sensitivity to vemurafenib, a BRAF inhibitor. The heterotypic 3D melanoma model we have established summarizes paracrine signalization by stromal cells and type I collagen matrix, mimicking the natural microenvironment of cutaneous MM, and allows for the identification of potent sensitive melanoma cells to the drug.

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