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Cell Counting - Crossref

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Last Updated: 10 January 2023

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Counting Cells by Age Tells Us About How, and Why, and When, We Grow, and Become Old and Ill

1-4 Since Verhulst's invention Optical Equation, exponential growth with a countervailing linear decrease in rate — a countervailing linear decline in rate — U2013 biologists have been looking for ever more dense-dependent growth equations, 6-12 none of which accurately portray the relationship between size and time for real animals. Here we report that analysis in units of cells, N, Cellular Phylodynamic Analysis (6), indicates that cell growth, longevity, and mortality are related to the decrease in the number of cells dividing, which is a common representation, the Universal Mitotic Fraction Equation. Lifespan is correlated with an age where fewer than one-in-a-thousand cells are dividing, demonstrating the long-awaited process of aging, cell death, cell metabolism, and DNA repair at mitosis are all linked.

Source link: https://doi.org/10.1101/2023.01.05.23284244


Tomographic brain imaging with nucleolar detail and automatic cell counting

Imaging the human brain in three dimensions has always been a challenge on the cell level. We show that hard X-ray phase tomography can visualise a volume of up to 43 mm 3 of human post mortem or biopsy brain samples by providing the procedure on the cerebellum. In phase tomograms obtained with isotropic resolution in a label-free manner, the method shows that automatic cell feature quantification of human tissues is possible.

Source link: https://doi.org/10.1038/srep32156


Ultrasensitive proteomic quantitation of cellular signaling by digitized nanoparticle-protein counting

Protein determination in single cells is extremely sensitive, beating traditional methods of determining total diffuse fluorescence determination and delivering a major increase in protein quantitation. 1 identified pAKT and pERK phospho-heterogeneity and insensitivity in individual leukemia cells treated with a multi-drug panel of FDA-approved kinase inhibitors, and 2 reported subpopulations of drug-insensitive CD34+ stem cells with elevated pCRKL and pSTAT5 expression in chronic myeloid leukemia patient blood samples, and 2 reported subpopulations of pCRKL and mad.

Source link: https://doi.org/10.1038/srep28163


Automatic cell counting from stimulated Raman imaging using deep learning

SRS microscopy has advanced tumor diagnosis and surgery by analyzing lipids and proteins from fresh specimens and achieving a fast and detailed report of tumor diagnostic signatures by high resolution. However, cell counting from label-free SRS images has been difficult, due to the limited comparison of cells and tissue, as well as the heterogeneity of tissue morphology and biochemical compositions. Cell counting correlations between SRS and histological images with hematoxylin and eosin staining is found in 88 percent of area under the curve and R = 0. 97 in terms of cell counting correlation. The proposed cell counting scheme highlights the possibility and possibility of cell counting in near real time, and encourages the use of deep learning techniques in biomedical and pathological image analysis.

Source link: https://doi.org/10.1371/journal.pone.0254586


Mechanical slowing-down of cytoplasmic diffusion allows in vivo counting of proteins in individual cells

Abstract Many key regulatory proteins in bacteria are present in too small amounts to be detected with standard methods, making it a challenge for single-cell analyses in the case of phenotypic heterogeneity. Although SprE is important for RpoS degradation, it is expressed at 33%4 molecules per average cell cycle, and SprE fluctuations are similar to Poisson distribution during exponential phase with no sign of bursting.

Source link: https://doi.org/10.1038/ncomms11641


Automatic Counting of Intra-Cellular Ribonucleo-Protein Aggregates in Saccharomyces cerevisiae Using a Textural Approach

We show that finding a precise number is a matter by itself, and as a result, we suggest three textural options: a classical point of view using Haralick features, a frequency point of view with generalized Fourier descriptors, and a structural point of view using Zernike time descriptors. An experiment using a specific Saccharomyces cerevisiae strain that demonstrated a combination of a protein found in RNPs and the green fluorescent protein was carried out to measure this strategy.

Source link: https://doi.org/10.1017/s1431927619000084


Automatic Biological Cell Counting Using a Modified Gradient Hough Transform

Abstract We present a computational method for pseudo-circular object detection and quantitative analysis in digital images using the gradient accumulation matrix as a starting point. These changes have made the GAT algorithm a more precise and robust way to automatically detect pseudo-circular objects in a microscopic photograph. In microbiological images, we then present an example of the procedure to cell counting.

Source link: https://doi.org/10.1017/s1431927616012617


Cryostat Slice Irregularities May Introduce Bias in Tissue Thickness Estimation: Relevance for Cell Counting Methods

As tissue volume/area, the thickness of tissue was determined by geometric section thickness. For all thickness experiments and counting frames, significant differences were found between T max and T geom. In subcellular samples, the T geom / T max average rate was 66. 7 percent and 83. 3 percent. Confocal microscopy can help to detect tissue irregularities, which may lead to an overestimation of tissue volume if section thickness is determined by focusing on the top and bottom of the sections.

Source link: https://doi.org/10.1017/s143192761501380x


Study of the Growth Curve of Vero Cell with Statistical Counting Method Based on the SEM Images

Cell growth curve - cell density versus time - can be illustrated by a growth curve, which shows cell density change throughout the entire growth process, including the first adhesion stage; expanding and division stage; and the final confluent stage - as previous studies reported. Therefore, the cell growth curve is not only the primary basis for measuring growth kinetics but also for the development of biological reactors. Traditionally, total cell counts for the microcarrier cultures were determined by the crystal violet method, and cell nuclei were counted using a hemacytometer. As a result, the moist cells and microcarriers in SEM images display their natural characteristics, because this environmental SEM enables biological samples to be obtained directly in an environmental specimen chamber with no need for biological sample preparation. In light of this, a statistical counting device based on the SEM images for determining cell concentration during microcarrier culture was developed.

Source link: https://doi.org/10.1017/s1431927600026301

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions