Markers of infection in inpatients and outpatients with acute Q-fever

Background: Query-fever (Q-fever) is a zoonotic infection caused by the intracellular Gram-negative coccobacillus Coxiella burnetii. A large ongoing outbreak of Q-fever has been reported in the Netherlands. We studied various markers of infection in inpatients (hospitalised) and outpatients (treated by a general physician) with acute Q-fever in relation to disease severity. Methods: Leukocyte counts, C-reactive protein (CRP) and procalcitonin (PCT) concentrations were measured in 25 inpatients and 40 outpatients upon presentation with acute Q-fever. Chest X-rays, if available, were analysed and confusion, urea, respiratory rate, blood pressure-age 65 (CURB-65) scores, indicating severity of pneumonia, were calculated. Results: CRP was the only marker that significantly differentiated between inpatients and outpatients. It was increased in all patients from both groups. Leukocyte counts and PCT concentrations did not differ between inpatients and outpatients. Overall, only 13/65 patients had an increased leukocyte count and only 11/65 patients presented with PCT concentrations indicative of possible bacterial respiratory tract infection. Infiltrative changes on the chest X-ray were observed in the majority of patients. CURB-65 score was 0±1 (mean±SD). Conclusions: Acute Q-fever, a relatively mild pneumonia with low CURB-65 scores, specifically induces a response in CRP, while PCT concentrations and leukocytes are within the normal range or increased only marginally

Impact of the gating strategy for Ki-67 and Bcl-2 on the determination of proliferation and anti-apoptosis data by flow cytometry in non-malignant bone marrow aspirates and aspirates from patients with myeloid malignancies

This Data in Brief article displays a flow cytometric assay that was used for the acquisition and analyses of proliferative and anti-apoptotic activity in hematopoietic cells. This dataset includes analyses of the Ki-67 positive fraction (Ki-67 proliferation index) and Bcl-2 positive fraction (Bcl-2 anti-apoptotic index) of the different myeloid bone marrow (BM) cell populations in non-malignant BM, and in BM disorders, i.e. myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML). The present dataset comprises 1) the percentage of the CD34 positive blast cells, erythroid cells, myeloid cells and monocytic cells, and 2) the determined Ki-67 positive fraction and Bcl-2 positive fraction of these cell populations in tabular form. This allows the comparison and reproduction of the data when these analyses are repeated in a different setting. Because gating the Ki-67 positive and Bcl-2 positive cells is a critical step in this assay, different gating approaches were compared to determine the most sensitive and specific approach. BM cells from aspirates of 50 non-malignant, 25 MDS and 27 AML cases were stained with 7 different antibody panels and subjected to flow cytometry for determination of the Ki-67 positive cells and Bcl-2 positive cells of the different myeloid cell populations. The Ki-67 or Bcl-2 positive cells were then divided by the total number of cells of the respective cell population to generate the Ki-67 positive fraction (Ki-67 proliferation index) or the Bcl-2 positive fraction (Bcl-2 anti-apoptotic index). The presented data may facilitate the establishment and standardization of flow cytometric analyses of the Ki-67 proliferation index and Bcl-2 anti-apoptotic index of the different myeloid cell populations in non-malignant BM as well as MDS and AML patients in other laboratories. Directions for proper gating of the Ki-67 positive and Bcl-2 positive fraction are crucial for achieving standardization among different laboratories. In addition, the data and the presented assay allows application of Ki-67 and Bcl-2 in a research and clinical setting and this approach can serve as the basis for optimization of the gating strategy and subsequent investigation of other cell biological processes besides proliferation and anti-apoptosis. These data can also promote future research into the role of these parameters in diagnosis of myeloid malignancies, prognosis of myeloid malignancies and therapeutic resistance against anti-cancer therapies in these malignancies. As specific populations were identified based on cell biological characteristics, these data can be useful for evaluating gating algorithms in flow cytometry in general by confirming the outcome (e.g. MDS or AML diagnosis) with the respective proliferation and anti-apoptotic profile of these malignancies. The Ki-67 proliferation index and Bcl-2 anti-apoptotic index may potentially be used for classification of MDS and AML based on supervised machine learning algorithms, while unsupervised machine learning can be deployed at the level of single cells to potentially distinguish non-malignant from malignant cells in the identification of minimal residual disease. Therefore, the present dataset may be of interest for internist-hematologists, immunologists with affinity for hemato-oncology, clinical chemists with sub-specialization of hematology and researchers in the field of hemato-oncology