13 research outputs found

    Automatic segmentation of skin cells in multiphoton data using multi-stage merging

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    We propose a novel automatic segmentation algorithm that separates the components of human skin cells from the rest of the tissue in fluorescence data of three-dimensional scans using non-invasive multiphoton tomography. The algorithm encompasses a multi-stage merging on preprocessed superpixel images to ensure independence from a single empirical global threshold. This leads to a high robustness of the segmentation considering the depth-dependent data characteristics, which include variable contrasts and cell sizes. The subsequent classification of cell cytoplasm and nuclei are based on a cell model described by a set of four features. Two novel features, a relationship between outer cell and inner nucleus (OCIN) and a stability index, were derived. The OCIN feature describes the topology of the model, while the stability index indicates segment quality in the multi-stage merging process. These two new features, combined with the local gradient magnitude and compactness, are used for the model-based fuzzy evaluation of the cell segments. We exemplify our approach on an image stack with 200 × 200 × 100 μm3, including the skin layers of the stratum spinosum and the stratum basale of a healthy volunteer. Our image processing pipeline contributes to the fully automated classification of human skin cells in multiphoton data and provides a basis for the detection of skin cancer using non-invasive optical biopsy

    Update on the third international stroke trial (IST-3) of thrombolysis for acute ischaemic stroke and baseline features of the 3035 patients recruited

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    Intravenous recombinant tissue plasminogen activator (rtPA) is approved in Europe for use in patients with acute ischaemic stroke who meet strictly defined criteria. IST-3 sought to improve the external validity and precision of the estimates of the overall treatment effects (efficacy and safety) of rtPA in acute ischaemic stroke, and to determine whether a wider range of patients might benefit

    FROG analysis ensures the reproducibility of genome scale metabolic models

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    Genome scale metabolic models (GEMs) and other constraint-based models (CBMs) play a pivotal role in understanding biological phenotypes and advancing research in areas like metabolic engineering, human disease modelling, drug discovery, and personalized medicine. Despite their growing application, a significant challenge remains in ensuring the reproducibility of GEMs, primarily due to inconsistent reporting and inadequate model documentation of model results. Addressing this gap, we introduce FROG analysis, a community driven initiative aimed at standardizing reproducibility assessments of CBMs and GEMs. The FROG framework encompasses four key analyses including Flux variability, Reaction deletion, Objective function, and Gene deletion to produce standardized, numerically reproducible FROG reports. These reports serve as reference datasets, enabling model evaluators, curators, and independent researchers to verify the reproducibility of GEMs systematically. BioModels, a leading repository of systems biology models, has integrated FROG analysis into its curation workflow, enhancing the reproducibility and reusability of submitted GEMs. In our study evaluating 65 GEM submissions from the community, approximately 40\% reproduced without intervention, 28\% requiring minor adjustments, and 32\% needing input from authors. The standardization introduced by FROG analysis facilitated the detection and resolution of issues, ultimately leading to the successful reproduction of all models. By establishing a standardized and comprehensive approach to evaluating GEM reproducibility, FROG analysis significantly contributes to making CBMs and GEMs more transparent, reusable, and reliable for the broader scientific community.Competing Interest StatementThe authors have declared no competing interest.info:eu-repo/semantics/publishedVersio

    Occupational position and consumption of news: A research note

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    Contains fulltext : occupoanc.pdf ( ) (Open Access

    Label-free volumetric quantitative imaging of human osteosarcoma cells by hyperspectral coherent anti-Stokes Raman scattering

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    Quantitative determination of the chemical composition of unstained samples, non-invasively, with high three- dimensional spatio-temporal resolution, will accelerate progress in cell biology. The current state of the art in bioimaging is dominated by either chemically non-specific or invasive methods. In this work, we demonstrate label-free, non-invasive quantitative volumetric imaging of human osteosarcoma cells using coherent anti-Stokes Raman scattering microscopy. A data analysis method developed in-house was applied to represent the chemical composition of the cells as volumetric three-dimensional images indicating water, proteins, DNAP (mixture of DNA and proteins), and lipids, and to determine the dry masses of the organic components with picogram resolution.</p

    Vibrational phase contrast CARS microscopy for quantitative analysis

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    In biological samples the resonant CARS signal of less abundant constituents can be overwhelmed by the nonresonant background, preventing detection of those molecules. We demonstrate a method to obtain the phase of the oscillators in the focal volume that allows discrimination of those hidden molecules. The phase is measured with respect to the local excitation fields using a cascaded phase-preserving chain. It is measured point-bypoint and takes into account refractive index changes in the sample, phase curvature over the field-of-view and interferometric instabilities. The detection of the phase of the vibrational motion can be regarded as a vibrational extension of the linear (refractive index) phase contrast microscopy introduced by Zernike around 193

    Non-invasive imaging of skin physiology and percutaneous penetration using fluorescence spectral and lifetime imaging with multiphoton and confocal microscopy

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    New multiphoton and confocal microscope technologies and fluorescence lifetime imaging techniques are now being used to non-invasively image, in space (three dimensions),in time, in spectra, in lifetime and in fluorescence anisotropy (total of 7 dimensions), fluorescent molecules in in situ and in vivo biological tissue, including skin. The process involves scanning a 2D area and measuring fluorescence at a given tissue depth below the surface after excitation by a laser beam with a wavelength within the one-photon or two-photon absorption band of the fluorophores followed by the stacking together of a series of 2D images from different depths to reconstruct the full spatial structure of the sample. Our aim in this work is to describe the principles, opportunities, limitations and applications of this new technology and its application in defining skin morphology, disease and skin penetration in vitro and in vivo by drugs, chemicals and nanoparticles. A key emphasis is in the use of fluorescence lifetime imaging to add additional specificity and quantitation to the detection of the various exogenous chemicals and nanoparticles that may be applied to the skin as well as endogenous fluorescent species in the skin. Examples given include equipment configuration; components in skin autofluorescence in various skin strata; imaging and quantification of coexisting drugs and their metabolites; skin pH; nanoparticle zinc oxide skin penetration; liposome delivery of drugs to deeper tissues; and observations in skin ageing and in various skin diseases.
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