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1.
J Clin Monit Comput ; 31(2): 407-415, 2017 Apr.
Article in English | MEDLINE | ID: mdl-27039298

ABSTRACT

It is difficult to make a distinction between inflammation and infection. Therefore, new strategies are required to allow accurate detection of infection. Here, we hypothesize that we can distinguish infected from non-infected ICU patients based on dynamic features of serum cytokine concentrations and heart rate time series. Serum cytokine profiles and heart rate time series of 39 patients were available for this study. The serum concentration of ten cytokines were measured using blood sampled every 10 min between 2100 and 0600 hours. Heart rate was recorded every minute. Ten metrics were used to extract features from these time series to obtain an accurate classification of infected patients. The predictive power of the metrics derived from the heart rate time series was investigated using decision tree analysis. Finally, logistic regression methods were used to examine whether classification performance improved with inclusion of features derived from the cytokine time series. The AUC of a decision tree based on two heart rate features was 0.88. The model had good calibration with 0.09 Hosmer-Lemeshow p value. There was no significant additional value of adding static cytokine levels or cytokine time series information to the generated decision tree model. The results suggest that heart rate is a better marker for infection than information captured by cytokine time series when the exact stage of infection is not known. The predictive value of (expensive) biomarkers should always be weighed against the routinely monitored data, and such biomarkers have to demonstrate added value.


Subject(s)
Critical Illness , Cross Infection/diagnosis , Heart Rate , Adult , Aged , Aged, 80 and over , Area Under Curve , Biomarkers/blood , Calibration , Critical Care , Cytokines/blood , Decision Trees , Humans , Intensive Care Units , Male , Middle Aged , Monitoring, Physiologic , Predictive Value of Tests , Prospective Studies , Respiration, Artificial , Risk , Time Factors , Young Adult
2.
Hum Immunol ; 53(2): 216-23, 1997 Apr 01.
Article in English | MEDLINE | ID: mdl-9129981

ABSTRACT

Patients with a relapse of chronic myeloid leukemia (CML) after allogeneic bone marrow transplantation can be successfully treated with blood mononuclear cells from the original bone marrow donor. However, the antileukemic effect of this treatment is often accompanied by graft-versus-host disease (GVHD). Treatment with cytotoxic T-lymphocyte (CTL) lines or clones that are specifically generated against leukemic antigen-presenting cells from the patient, may separate antileukemic effects from GVHD. In this report we demonstrate that after culturing CD34-positive cells purified from bone marrow of patients with chronic phase CML in medium containing human serum, GM-CSF, TNF alpha, and IL-4 up to 28% of the cultured cells were dendritic cells, characterized by morphology, phenotypic analysis, and their efficient capacity to stimulate allogeneic T lymphocytes. The expression of HLA and costimulatory molecules and the stimulatory capacity of the dendritic cell-enriched cell suspensions were optimal between days 7 and 10 after onset of the cultures. Fluorescence in situ hybridization revealed that all cultured dendritic cells contained the CML specific t(9;22) translocation. PCR analysis showed expression of the translocation specific bcr-abl mRNA. These leukemic dendritic cells may enhance the induction and proliferation of CTL lines and clones with more specificity for the leukemic cells.


Subject(s)
Antigens, CD34/analysis , Dendritic Cells/cytology , Fusion Proteins, bcr-abl/biosynthesis , Fusion Proteins, bcr-abl/genetics , Hematopoietic Stem Cells/immunology , Leukemia, Myelogenous, Chronic, BCR-ABL Positive/immunology , Bone Marrow Cells , Cell Cycle/immunology , Cells, Cultured , Dendritic Cells/immunology , Dendritic Cells/metabolism , Fusion Proteins, bcr-abl/immunology , Humans , Leukemia, Myelogenous, Chronic, BCR-ABL Positive/genetics , Lymphocyte Activation , RNA, Messenger/analysis , Translocation, Genetic
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