Staining human lymphocytes and onion root cell nuclei with madder root

We performed staining experiments on cells using natural dyes and different mordants using techniques that are used for wool and silk dyeing. The natural dye sources were madder root, daisy, corn cockle and yellow weed. Ferrous sulfate, copper sulfate, potassium tartrate, urea, potassium aluminum sulfate and potassium dichromate were used as mordants. Distilled water, distilled water plus ethanol, heptane, and distilled water plus methanol were used as solvents. All dye-mordant-solvent combinations were studied at pH 2.4, 3.2 and 4.2. The generic staining procedure was to boil 5–10 onion roots or stimulated human lymphocyte (SHL) preparations in a dye bath on a hot plate. Cells were examined at every half hour. For multicolor staining, madder-dyed lymphocytes were decolorized, then stained with Giemsa. The AgNOR technique was performed following the decolorization of Giemsa stained lymphocytes. Good results were obtained for both onion root cells and lymphocytes that were boiled for 3 h in a dye bath that included 4 g madder root, 4 g ferrous sulfate as mordant in 50 ml of 1:1 (v/v) methanol:distilled water. The pH was adjusted to 4.2 with 6 ml acetic acid. We conclude that madder root has potential as an alternative dye for staining biological materials.     

Importance of solid lipid nanoparticles (SLN) in various administration routes and future perspectives

Solid lipid nanoparticles (SLN) have been reported to be an alternative system to emulsions, liposomes, microparticles and their polymeric counterparts for various application routes since the early 1990s due to their advantages. Various research groups have also increasingly focused on improving their stability in body fluids after administration by coating of particles with hydrophilic molecules such as poly(ethylene)glycol (PEG) derivatives. Altering surface characteristics by coating SLN with hydrophilic molecules improves plasma stability and biodistribution, and subsequent bioavailability of drugs entrapped. Their storage stability is also increased. This paper basicly reviews types of SLN, principles of drug loading and models of drug incorporation. The influence of PEG coating on particle size and surface characteristics is discussed followed by alteration in pharmacokinetics and bioavailability of drugs in order to target the site of action via SLN. The future direction of research and clinical implications of SLN is also considered.     

Augmented cell-graphs for automated cancer diagnosis

This work reports a novel computational method based on augmented cell-graphs (ACG), which are constructed from low-magnification tissue images for the mathematical diagnosis of brain cancer (malignant glioma). An ACG is a simple, undirected, weighted and complete graph in which a node represents a cell cluster and an edge between a pair of nodes defines a binary relationship between them. Both the nodes and the edges of an ACG are assigned weights to capture more information about the topology of the tissue. In this work, the experiments are conducted on a dataset that is comprised of 646 human brain biopsy samples from 60 different patients. It is shown that the ACG approach yields sensitivity of 97.53% and specificities of 93.33 and 98.15% (for the inflamed and healthy, respectively) at the tissue level in glioma diagnosis.     

Epidemiological models of Mycobacterium tuberculosis complex infections

The resurgence of tuberculosis in the 1990s and the emergence of drug-resistant tuberculosis in the first decade of the 21st century increased the importance of epidemiological models for the disease. Due to slow progression of tuberculosis, the transmission dynamics and its long-term effects can often be better observed and predicted using simulations of epidemiological models. This study provides a review of earlier study on modeling different aspects of tuberculosis dynamics. The models simulate tuberculosis transmission dynamics, treatment, drug resistance, control strategies for increasing compliance to treatment, HIV/TB co-infection, and patient groups. The models are based on various mathematical systems, such as systems of ordinary differential equations, simulation models, and Markov Chain Monte Carlo methods. The inferences from the models are justified by case studies and statistical analysis of TB patient datasets.     

Multiscale feature analysis of salivary gland branching morphogenesis

Pattern formation in developing tissues involves dynamic spatio-temporal changes in cellular organization and subsequent evolution of functional adult structures. Branching morphogenesis is a developmental mechanism by which patterns are generated in many developing organs, which is controlled by underlying molecular pathways. Understanding the relationship between molecular signaling, cellular behavior and resulting morphological change requires quantification and categorization of the cellular behavior. In this study, tissue-level and cellular changes in developing salivary gland in response to disruption of ROCK-mediated signaling by are modeled by building cell-graphs to compute mathematical features capturing structural properties at multiple scales. These features were used to generate multiscale cell-graph signatures of untreated and ROCK signaling disrupted salivary gland organ explants. From confocal images of mouse submandibular salivary gland organ explants in which epithelial and mesenchymal nuclei were marked, a multiscale feature set capturing global structural properties, local structural properties, spectral, and morphological properties of the tissues was derived. Six feature selection algorithms and multiway modeling of the data was performed to identify distinct subsets of cell graph features that can uniquely classify and differentiate between different cell populations. Multiscale cell-graph analysis was most effective in classification of the tissue state. Cellular and tissue organization, as defined by a multiscale subset of cell-graph features, are both quantitatively distinct in epithelial and mesenchymal cell types both in the presence and absence of ROCK inhibitors. Whereas tensor analysis demonstrate that epithelial tissue was affected the most by inhibition of ROCK signaling, significant multiscale changes in mesenchymal tissue organization were identified with this analysis that were not identified in previous biological studies. We here show how to define and calculate a multiscale feature set as an effective computational approach to identify and quantify changes at multiple biological scales and to distinguish between different states in developing tissues.