Hematology Analyzer Principle Pdf Free
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In 1852, Karl Vierordt published the first procedure for performing a blood count, which involved spreading a known volume of blood on a microscope slide and counting every cell. The invention of the hemocytometer in 1874 by Louis-Charles Malassez simplified the microscopic analysis of blood cells, and in the late 19th century, Paul Ehrlich and Dmitri Leonidovich Romanowsky developed techniques for staining white and red blood cells that are still used to examine blood smears. Automated methods for measuring hemoglobin were developed in the 1920s, and Maxwell Wintrobe introduced the Wintrobe hematocrit method in 1929, which in turn allowed him to define the red blood cell indices. A landmark in the automation of blood cell counts was the Coulter principle, which was patented by Wallace H. Coulter in 1953. The Coulter principle uses electrical impedance measurements to count blood cells and determine their sizes; it is a technology that remains in use in many automated analyzers. Further research in the 1970s involved the use of optical measurements to count and identify cells, which enabled the automation of the white blood cell differential.
Radiofrequency-based methods can be used in combination with impedance. These techniques work on the same principle of measuring the interruption in current as cells pass through an aperture, but since the high-frequency RF current penetrates into the cells, the amplitude of the resulting pulse relates to factors like the relative size of the nucleus, the nucleus's structure, and the amount of granules in the cytoplasm.[64][65] Small red cells and cellular debris, which are similar in size to platelets, may interfere with the platelet count, and large platelets may not be counted accurately, so some analyzers use additional techniques to measure platelets, such as fluorescent staining, multi-angle light scatter and monoclonal antibody tagging.[48]
Point-of-care testing refers to tests conducted outside of the laboratory setting, such as at a person's bedside or in a clinic.[78][79] This method of testing is faster and uses less blood than conventional methods, and does not require specially trained personnel, so it is useful in emergency situations and in areas with limited access to resources. Commonly used devices for point-of-care hematology testing include the HemoCue, a portable analyzer that uses spectrophotometry to measure the hemoglobin concentration of the sample, and the i-STAT, which derives a hemoglobin reading by estimating the concentration of red blood cells from the conductivity of the blood.[79] Hemoglobin and hematocrit can be measured on point-of-care devices designed for blood gas testing, but these measurements sometimes correlate poorly with those obtained through standard methods.[78] There are simplified versions of hematology analyzers designed for use in clinics that can provide a complete blood count and differential.[80]
Advanced hematology analyzers generate novel measurements of blood cells which have shown diagnostic significance in research studies but have not yet found widespread clinical use.[171] For example, some types of analyzers produce coordinate readings indicating the size and position of each white blood cell cluster. These parameters (termed cell population data)[176] have been studied as potential markers for blood disorders, bacterial infections and malaria. Analyzers that use myeloperoxidase staining to produce differential counts can measure white blood cells' expression of the enzyme, which is altered in various disorders.[75] Some instruments can report the percentage of red blood cells that are hypochromic in addition to reporting the average MCHC value, or provide a count of fragmented red cells (schistocytes),[171] which occur in some types of hemolytic anemia.[177] Because these parameters are often specific to particular brands of analyzers, it is difficult for laboratories to interpret and compare results.[171]
The first techniques for measuring hemoglobin were devised in the late 19th century, and involved visual comparisons of the colour of diluted blood against a known standard.[205] Attempts to automate this process using spectrophotometry and colorimetry were limited by the fact that hemoglobin is present in the blood in many different forms, meaning that it could not be measured at a single wavelength. In 1920, a method to convert the different forms of hemoglobin to one stable form (cyanmethemoglobin or hemiglobincyanide) was introduced, allowing hemoglobin levels to be measured automatically. The cyanmethemoglobin method remains the reference method for hemoglobin measurement and is still used in many automated hematology analyzers.[57][210][211]
Abstract:This paper describes a free energy principle that tries to explain the ability of biological systems to resist a natural tendency to disorder. It appeals to circular causality of the sort found in synergetic formulations of self-organization (e.g., the slaving principle) and models of coupled dynamical systems, using nonlinear Fokker Planck equations. Here, circular causality is induced by separating the states of a random dynamical system into external and internal states, where external states are subject to random fluctuations and internal states are not. This reduces the problem to finding some (deterministic) dynamics of the internal states that ensure the system visits a limited number of external states; in other words, the measure of its (random) attracting set, or the Shannon entropy of the external states is small. We motivate a solution using a principle of least action based on variational free energy (from statistical physics) and establish the conditions under which it is formally equivalent to the information bottleneck method. This approach has proved useful in understanding the functional architecture of the brain. The generality of variational free energy minimisation and corresponding information theoretic formulations may speak to interesting applications beyond the neurosciences; e.g., in molecular or evolutionary biology.Keywords: ergodicity; Bayesian; random dynamical system; self-organization; free energy; surprisePACS Codes:87.18.Vf, 02.70.Rr
The ADVIA 2120 Hematology System was recently released by Bayer HealthCare, Diagnostics Division, as a bench-top analyzer designed for medium- to large-volume laboratories. This flow cytometry-based system uses light scatter, differential white blood cell (WBC) lysis, and myeloperoxidase and oxazine 750 staining to provide a complete blood cell count, a WBC differential, and a reticulocyte count. A cyanide-free method is used to measure hemoglobin colorimetrically. The system is automation ready; in addition to its capability for analyzing peripheral blood specimens, the analyzer is also equipped to analyze cerebrospinal fluid samples. In this article we explain the underlying technology of the ADVIA 2120, provide linearity ranges, method-specific reference ranges, and stability data, and describe novel parameters and applications that are unique to the methodology used by this instrument. Finally, we discuss research applications and future directions, such as the use of this hematology analyzer in the determination of fetal lung maturity. 153554b96e
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