2019
DOI: 10.1186/s12879-019-4207-9
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Immune function as predictor of infectious complications and clinical outcome in patients undergoing solid organ transplantation (the ImmuneMo:SOT study): a prospective non-interventional observational trial

Abstract: Background Solid organ transplantation (SOT) is a well-established and life-saving treatment for patients with end-stage organ failure. Organ rejection and infections are among the main complications to SOT and largely determines the clinical outcome. The correct level of immunosuppression is of major importance to prevent these complications. However, it is a consistent observation that in recipients on the same immunosuppressive regimens the clinical outcome varies, and no reliable marker exists… Show more

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Cited by 10 publications
(9 citation statements)
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“…Currently, clinicians usually assess patients' immune function based on whether patients have underlying diseases, and this has lots of inaccuracy. Lymphocyte function is one of the most important characteristics to reflect host immunity; however, lymphocyte function assessment has not been widely used in clinical practice (Drabe et al, 2019 ; Luo et al, 2019 ).…”
Section: Introductionmentioning
confidence: 99%
“…Currently, clinicians usually assess patients' immune function based on whether patients have underlying diseases, and this has lots of inaccuracy. Lymphocyte function is one of the most important characteristics to reflect host immunity; however, lymphocyte function assessment has not been widely used in clinical practice (Drabe et al, 2019 ; Luo et al, 2019 ).…”
Section: Introductionmentioning
confidence: 99%
“…Besides, other parameters, such as mRNA expression, surface activation-markers, cell viability, morphology, intra-cellular cytokine levels, etc. can be determined by recovery of the cellular components from these cultures as well (33,44,45).…”
Section: Discussionmentioning
confidence: 99%
“…The accuracy score for the classification performance was 94. and can assist in the early screening of COVID19 positive cases, reducing the burden on healthcare systems [36]. Also, the machine learning (ML) technique was implemented in Denmark to classify patients with chronic lymphocytic leukemia (CLL) who were at risk of infection due to immune dysfunction [23][24].…”
Section: ) Diagnostic Support Systemsmentioning
confidence: 99%