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Saint Petersburg, Russia

Sharma P.,Jawaharlal Institute of Postgraduate Medical Education & Research | Bhargava M.,Sir Ganga Ram Hospital | Sukhachev D.,LabTech Ltd | Datta S.,Sir Ganga Ram Hospital | Wattal C.,Sir Ganga Ram Hospital
International Journal of Laboratory Hematology | Year: 2014

Introduction: Tropical febrile illnesses such as malaria and dengue are challenging to differentiate clinically. Automated cellular indices from hematology analyzers may afford a preliminary rapid distinction. Methods: Blood count and VCS parameters from 114 malaria patients, 105 dengue patients, and 105 febrile controls without dengue or malaria were analyzed. Statistical discriminant functions were generated, and their diagnostic performances were assessed by ROC curve analysis. Results: Three statistical functions were generated: (i) malaria-vs.-controls factor incorporating platelet count and standard deviations of lymphocyte volume and conductivity that identified malaria with 90.4% sensitivity, 88.6% specificity; (ii) dengue-vs.-controls factor incorporating platelet count, lymphocyte percentage and standard deviation of lymphocyte conductivity that identified dengue with 81.0% sensitivity and 77.1% specificity; and (iii) febrile-controls-vs.-malaria/dengue factor incorporating mean corpuscular hemoglobin concentration, neutrophil percentage, mean lymphocyte and monocyte volumes, and standard deviation of monocyte volume that distinguished malaria and dengue from other febrile illnesses with 85.1% sensitivity and 91.4% specificity. Conclusions: Leukocyte abnormalities quantitated by automated analyzers successfully identified malaria and dengue and distinguished them from other fevers. These economic discriminant functions can be rapidly calculated by analyzer software programs to generate electronic flags to trigger-specific testing. They could potentially transform diagnostic approaches to tropical febrile illnesses in cost-constrained settings. © 2013 John Wiley & Sons Ltd. Source


Golubeva V.,Saint Petersburg State University | Mikhalevich J.,LabTech Ltd | Novikova J.,City Clinical Hospital | Tupizina O.,City Clinical Hospital | And 2 more authors.
Transfusion and Apheresis Science | Year: 2014

Autologous hematopoietic stem cell transplantation (AHSCT) is a necessary component for many oncohematological diseases treatment. For a successful result of AHSCT a sufficient quantity of hematopoietic stem cells (HSCs) is needed. It has been proposed that morphological changes of myeloid cells could reflect the processes of bone marrow stimulation and may provide useful information to predict the stimulation efficiency and expected outcome of CD34+ stem cells. The Beckman Coulter Cellular Analysis System DxH800 performs Flow Cytometric Digital Morphology analysis of leukocytes. All leukocyte cellular measurements can be reported as numerical values called Cell Population Data (CPD), which are able to detect morphological changes in the cell size and distribution of neutrophils. Our findings suggest that the changes in neutrophil CPD were detectable 2-4days before the observed increase in CD34+ count in the peripheral blood and can potentially improve the management of patients. There was also a good correlation between MN-V-NE and ImmNeIndex with the CD34+ count suggesting they can be used as a surrogate for the CD34+ count (r=0.67 and 0.65 p<0.005 respectively). © 2013. Source


Vagner A.,Debrecen University | Farkas L.,LabTech Ltd | Juhasz I.,Debrecen University
Advances in Intelligent and Soft Computing | Year: 2011

Holter electrocardiographic (ECG) recordings are ambulatory long-term registers that are used to detect heart diseases. These recordings normally include more than one channel and their durations are up to 24 hours. The principal problem of the cardiologists is the manual inspection of the whole Holter ECG in order to find all those beats which morphologically differ from the normal beats. In this paper we present our method. Firstly, we apply a grid clustering technique. Secondly, we use a special density-based clustering algorithm, named Optics. Then we visualize every heart beat in the record, heartbeats in a cluster, furthermore we represent every cluster with median of heartbeats. We can perform manual. With this method the ECG is easily analyzed and the time of processing is optimized. © Springer-Verlag Berlin Heidelberg 2011. Source


Celik I.H.,Zekai Tahir Burak Maternity | Demirel G.,Zekai Tahir Burak Maternity | Sukhachev D.,LabTech Ltd | Erdeve O.,Zekai Tahir Burak Maternity | And 2 more authors.
International Journal of Laboratory Hematology | Year: 2013

Introduction: Neonatal sepsis remains an important clinical syndrome despite advances in neonatology. Current hematology analyzers can determine cell volume (V), conductivity for internal composition of cell (C) and light scatter for cytoplasmic granularity and nuclear structure (S), and standard deviations which are effective in the diagnosis of sepsis. Statistical models can be used to strengthen the diagnosis. Effective modeling of molecular activity (EMMA) uses combinatorial algorithm of the selection parameters for regression equation based on modified stepwise procedure. It allows obtaining different regression models with different combinations of parameters. Methods: We investigated these parameters in screening of neonatal sepsis. We used LH780 hematological analyzer (Beckman Coulter, Fullerton, CA, USA). We combined these parameters with interleukin-6 (IL-6) and C-reactive protein (CRP) and developed models by EMMA. Results: A total of 304 newborns, 76 proven sepsis, 130 clinical sepsis and 98 controls, were enrolled in the study. Mean neutrophil volume (MNV) and volume distribution width (VDW) were higher in both proven and clinical sepsis groups. We developed three models using MNV, VDW, IL-6, and CRP. These models gave more sensitivity and specificity than the usage of each marker alone. Conclusions: We suggest to use the combination of MNV and VDW with markers such as CRP and IL-6, and use diagnostic models created by EMMA. © 2012 Blackwell Publishing Ltd. Source


The present disclosure is directed to systems and methods of managing remote devices. The system can include a server with memory, a detection module, and a collection module. The memory can store a management information base (MIB) having a hierarchical tree of object identifiers and corresponding object values. The detection module can query devices and receive a first object identifier and its first object value, which can vary from those in the MIB; and use patterns to match the first object identifier and object value; and generate an identification of the device from the matches. The collection module can use the identification to select a collection template, which can indicate a subtree of the MIB and a collection pattern; traverse the subtree and identify a second object identifier that matches the collection pattern, and its second object value; and associate the second object value with the first object value.

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