Cell counting

Abstract: Non-surgical and surgical asplenia predisposes to fatal infections; therefore, simple, non-invasive and repeatable tests for assessing splenic function are required, even in non-specialized medical institutions. Howell-Jolly bodies are the most characteristic peripheral blood abnormality after splenectomy, but their counting is not considered a reliable measure of splenic function. In this study, in a group of splenectomized subjects and of patients with non-surgical hyposplenism, we have compared counting of Howell-Jolly bodies, stained by both the May-Grunwald/Giemsa method and the Feulgen reaction, with pitted cell counting which is considered a reliable technique for the assessment of splenic hypofunction. A significant correlation has been found between Howell-Jolly body counts, stained by either technique, and pitted cell counts (P less than 0.0001). Through Howell-Jolly bodies were never detectable when pitted cell counts fell between 4 and 8%, values consistent with a very mild splenic hypofunction, for pitted cell counts above 8% their increase was always associated with increasing Howell-Jolly body counts. These data suggest that, although pitted cell counting represents a more sensitive method for evaluating splenic function, Howell-Jolly body counting may still be regarded as a simple and reliable technique for identifying and monitoring those cases associated with a real risk of overwhelming infections.

Abstract: BACKGROUND: Semiquantitative evaluation and manual cell counting are the commonly used procedures to assess positive staining of molecular markers in tissue sections. Manual counting is also a laborious task in which consistent objectivity is difficult to achieve. Recently, image analysis has been explored, but the studies reported were limited to histological images acquired at high magnification and containing uniformly stained cells. METHODS: The analyzed material consisted of histological sections from different squamous cell cancers that had stained for proliferation using Ki-67 and cyclin A detection. The first step of the method was based on detecting the overall number of cells irrespective to their stain, using second-order edge detection methodology. Then proliferating cells were located using principal component analysis (PCA) of the color image, combined with histogram thresholding. RESULTS: The algorithms' performances were validated on tissue section images encountered in routine clinical practice by comparison with objective measures of performance and manual cell identification. The algorithms correlated closely with manual counting of all cells (r(2) = 0.96-0.97) and stained cells (4-7% cell count error). CONCLUSIONS: Cell counting in complex large-scale histological images could be applied in routine practice using edge and color information. The proposed technique provides several benefits, such as speed of analysis, consistency, and automation. Moreover, it is faster than human observation and could replace the laborious task of manual cell counting.

Abstract: In this study, we investigated the relationship between cell counting units and biomass forPseudomonas aeruginosa since microbial parameters often need to be defined as mass rather than colony forming unit (CFU) or optical density (OD) which is easily measurable using plate counting method or spectrophotometer especially for any attempt in modeling of contaminant transport and biodegradation in aquifer systems. Results showed that 1.0 OD corresponded to 2.04 × 108 CFU/ml and 2.085 mg/ml with well defined linear relationships of Y (CFU/ml) = 2.0 × 108 X (OD600) + 4.0 × 106 and Y (mg/ml) =2.0087 × (OD600) + 0.0764. Key words: Cell counting unit, colony forming unit, optical density, Pseudomonas aeruginosa.