Adjustment and quantification of UV-visible spectrophotometry analysis: an accurate and rapid method for estimating Cladosporium spp. spore concentration in a water suspension

Cladosporium spp. are among the most important plant pathogens, plant endophytes, insect parasites and human pathogens in nature. The aim of this study was to increase the speed and accuracy of Cladosporium spp. spore counting using UV-visible spectrophotometry based on the regression model in a water suspension. Spores of C. ramotenellum AM55, C. limoniforme Br15, C. tenuissimum K15 and C. cladosporioides Ld13 fungi were diluted in sterile distilled water several times. Spore concentration/ml (SC) was counted with a hemocytometer. The spectrophotometer visible light absorption (ABS) was measured under 14 wavelengths from 300 to 950 nm for each dilution. The results showed that the morphological variation of the spores greatly affect the determination of the suitable wavelength. 650, 750, 500 and 400 nm wavelengths had the highest coefficient of determination (R²) values respectively for C. ramotenellum AM55, C. limoniforme Br15, C. tenuissimum K15 and C. cladosporioides Ld13 on the linear regression model. R² values were 0.9874, 0.9647, 0.8856 and 0.9711 respectively, for the 650, 750, 500 and 400 nm wavelengths. The linear equation of SC = 10⁷ × ABS-133,040 with the highest R2 value of 0.9532 had the best fit under a combinatorial regression model where SC and ABS of all Cladosporium spp. were presented. The proposed linear regression models can be used under in vivo and in vitro conditions for medicine or plant pathology studies which certainly increase the accuracy and speed of the future experiments compared to the hemocytometer method.

The Absolute Basophil Count

The absolute basophil count (cells/L) can be determined by manual counting of peripheral blood smears or using cell counting chambers as well as by automated hematology analyzers and fluorescence flow cytometry. Manual basophil counting of peripheral blood smears is currently regarded as the reference method, although the limitations of this method (distribution, observer, and statistical errors) are widely recognized. Automated hematology analyzers offer an advantage of larger numbers of counted cells and high throughput but are characterized by inconsistent analytical performance for basophil enumeration. Flow cytometric enumeration of circulating basophils using panels of monoclonal antibodies is being developed as novel candidate reference method for the absolute basophil count in peripheral blood. Basophil counting using fluorescence flow cytometry is characterized by high precision and statistical superiority. Emerging innovative technologies for absolute cell counts include imaging flow cytometry, mass cytometry, and on-chip blood counting, but their analytical performance for absolute basophil counts is yet to be established. Here, we describe various techniques for absolute basophil counting in peripheral blood including manual basophil counts in smears and hemocytometers and flow cytometric methodologies using double-platform, bead-based, and volumetric approaches.

Detection of acute lymphoblastic leukemia using image segmentation and data mining algorithms

Blood is composed of white blood cells, red blood cells, and platelets. Segmentation of the blood smear cells and extraction of features of the cells is essential in the field of medicine. Acute lymphoblastic leukemia is a form of blood cancer caused due to the abnormal increase in the production of immature white blood cells in the bone marrow. It mostly affects the children below 5 years and adults above 50 years of age. Due to the late diagnosis and cost of the devices used for the determination, the mortality rate has increased drastically. Flow cytometry technique that performs automated counting fails to identify the abnormal cells. Manual recount performed using hemocytometer are prone to errors and are imprecise. The proposed work aims to survey different computer-aided system techniques used to segment the blood smear image. The primary objective here is to derive knowledge from the different methodologies used for extracting features from white blood cells and develop a system that would accurately segment the blood smear image by overcoming the drawbacks of the previous works. The objective mentioned above is achieved in two ways. Firstly, a novel algorithm is developed to segment the nucleus and cytoplasm of white blood cell. Secondly, a model is built to extract the features and train the model. The different supervised classifiers are compared, and the one with the highest accuracy is used for the classification. Six hundred images are used in the experimentation. InfoGainAttributeEval and the Ranker Search method are used to achieve the feature selection which in turn helps in improvising the classifier performance. The result shows the classification of the acute lymphoblastic leukemia into its three respective categories namely: ALL-L1, ALL-L2, ALL-L3. The model can differentiate between a normal peripheral blood smear and an abnormal blood smear. The extracted feature values of a cancerous cell and a normal cell are also shown. The performance of the model is evaluated using the test images stained with various stains. The proposed algorithm achieved an overall accuracy of 98.6%. The promising results show that it can be used as a diagnostic tool by the pathologists. Graphical abstract.

Improving reliability of live/dead cell counting through automated image mosaicing

Cell counting is one of the basic needs of most biological experiments. Numerous methods and systems have been studied to improve the reliability of counting. However, at present, manual cell counting performed with a hemocytometer still represents the gold standard, despite several problems limiting reproducibility and repeatability of the counts and, at the end, jeopardizing their reliability in general. We present our own approach based on image processing techniques to improve counting reliability. It works in two stages: first building a high-resolution image of the hemocytometer's grid, then counting the live and dead cells by tagging the image with flags of different colours. In particular, we introduce GridMos (http://sourceforge.net/p/gridmos), a fully-automated mosaicing method to obtain a mosaic representing the whole hemocytometer's grid. In addition to offering more significant statistics, the mosaic "freezes" the culture status, thus permitting analysis by more than one operator. Finally, the mosaic achieved can thus be tagged by using an image editor, thus markedly improving counting reliability. The experiments performed confirm the improvements brought about by the proposed counting approach in terms of both reproducibility and repeatability, also suggesting the use of a mosaic of an entire hemocytometer's grid, then labelled trough an image editor, as the best likely candidate for the new gold standard method in cell counting.