| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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López-Hoyos, Marcos
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (3/3 displayed)
- 2023Total error in lymphocyte subpopulations by flow cytometry-based in state of the art using Spanish EQAS datacitations
- 2016High expression of CD38, CD69, CD95 and CD154 biomarkers in cultured peripheral T lymphocytes correlates with an increased risk of acute rejection in liver allograft recipients.citations
- 2014Should IFN-γ, IL-17 and IL-2 be considered predictive biomarkers of acute rejection in liver and kidney transplant? Results of a multicentric study.citations
Places of action
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article
High expression of CD38, CD69, CD95 and CD154 biomarkers in cultured peripheral T lymphocytes correlates with an increased risk of acute rejection in liver allograft recipients.
Abstract
The mayor goal still outstanding into the solid organ transplantation field involves the search of surrogate biomarkers able to predict several clinical events, such as acute rejection (AR) or opportunistic infection. In the present multicenter study, a series of interesting surface antigens with important activator or inhibitory immune functions on cultured peripheral T cells were monitored in liver transplant recipients drawn at baseline and up to one year after transplantation. Sixty-four patients were included in the multicenter study during 3 years. Pre- and post-transplantation surface antigens levels displayed significant differences between AR and non acute rejection (NAR) groups, and also this differential expression was used to construct a risk predictive model based on a composite panel of outcome biomarkers (CD38, CD69, CD95 and CD154). The model was able to stratify these patients at high risk of AR. These preliminary results could provide basic information to improve the immunosuppressive treatment and it might better help to predict AR episodes.