The first World Cell Race

Motility is a common property of animal cells. Cell motility is required for embryogenesis [1], tissue morphogenesis [2] and the immune response [3] but is also involved in disease processes, such as metastasis of cancer cells [4]. Analysis of cell migration in native tissue in vivo has yet to be fully explored, but motility can be relatively easily studied in vitro in isolated cells. Recent evidence suggests that cells plated in vitro on thin lines of adhesive proteins printed onto culture dishes can recapitulate many features of in vivo migration on collagen fibers 5, 6. However, even with controlled in vitro measurements, the characteristics of motility are diverse and are dependent on the cell type, origin and external cues. One objective of the first World Cell Race was to perform a large-scale comparison of motility across many different adherent cell types under standardized conditions. To achieve a diverse selection, we enlisted the help of many international laboratories, who submitted cells for analysis. The large-scale analysis, made feasible by this competition-oriented collaboration, demonstrated that higher cell speed correlates with the persistence of movement in the same direction irrespective of cell origin

A comparison of machine learning techniques for survival prediction in breast cancer

<p>Abstract</p> <p>Background</p> <p>The ability to accurately classify cancer patients into risk classes, i.e. to predict the outcome of the pathology on an individual basis, is a key ingredient in making therapeutic decisions. In recent years gene expression data have been successfully used to complement the clinical and histological criteria traditionally used in such prediction. Many "gene expression signatures" have been developed, i.e. sets of genes whose expression values in a tumor can be used to predict the outcome of the pathology. Here we investigate the use of several machine learning techniques to classify breast cancer patients using one of such signatures, the well established <it>70-gene signature</it>.</p> <p>Results</p> <p>We show that Genetic Programming performs significantly better than Support Vector Machines, Multilayered Perceptrons and Random Forests in classifying patients from the NKI breast cancer dataset, and comparably to the scoring-based method originally proposed by the authors of the 70-gene signature. Furthermore, Genetic Programming is able to perform an automatic feature selection.</p> <p>Conclusions</p> <p>Since the performance of Genetic Programming is likely to be improvable compared to the out-of-the-box approach used here, and given the biological insight potentially provided by the Genetic Programming solutions, we conclude that Genetic Programming methods are worth further investigation as a tool for cancer patient classification based on gene expression data.</p

Specific activation of photosensitizer with extrinsic enzyme for precisive photodynamic therapy

Title on author’s file: Bioorthogonal Activation of Photosensitizer with Extrinsic Enzyme for Precisive Photodynamic Therapy202305 bcchAccepted ManuscriptRGCPublishe

One-pot synthesis of a cyclic antimicrobial peptide-conjugated phthalocyanine for synergistic chemo-photodynamic killing of multidrug-resistant bacteria

Dr. Clarence T. T. Wong, affiliated with the Hong Kong Polytechnic University at the time of final publication.202204 bcfcAccepted ManuscriptRGCPublishe

One-pot peptide cyclisation and surface modification of photosensitiser-loaded red blood cells for targeted photodynamic therapy

202209_bcwwRGCPublished12 month

Using a web-based platform as an alternative for conducting international, multidisciplinary medical conferences during the novel COVID-19 pandemic: Analysis of a conference

10.2196/23980JMIR Medical Education72e23980

Impact of epidermal growth factor receptor inhibitor in non-small cell lung cancer on progression-free an overall survival: a meta-analysis

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