Influence of components of tumour microenvironment on the response of HCT-116 colorectal cancer to the ruthenium-based drug NAMI-A

Solid tumours are constituted of tumour cells, healthy cells recruited from the host tissues and soluble factors released by both these cell types. The present investigation examines the capacity of co-cultures between the HCEC colon epithelial cells and the HCT-116 colorectal cancer cells (mimicking the primary site of tumour growth) and between IHH hepatocytes and the HCT-116 colorectal cancer cells (metastatic site) to influence the effects of NAMI-A (imidazolium trans-imidazoledimethylsulphoxidetetrachloro ruthenate) on the tumour cells themselves. The growth of HCT-116 cells is significantly influenced when the cancer cells are sown on a monolayer of HCEC. The release of soluble factors by the healthy cells promotes, in HCT-116 colorectal cancer cells, the transcription of genes involved in growth, invasion and migration. NAMI-A is not cytotoxic to HCT-116 cells grown on plastics or co-cultured with HCEC or IHH cells, and maintains its ability to control the cell pseudo-metastatic ability, mimicked by the migration in the scratch test. The effects of NAMI-A on HCT-116 migration are supported by its inhibition of the transcription of the ABL-2, ATF-3 and RND-1 genes. In conclusion the study highlights the need of test systems more complex than a single cancer cell culture to study an anticancer drug in vitro and reinforces the hypothesis that NAMI-A targets the ability of the cancer cell to interact with the tumour microenvironment and with the signals that support its metastatic ability

Ice Core Science Meets Computer Vision: Challenges and Perspectives

Polar ice cores play a central role in studies of the earth's climate system through natural archives. A pressing issue is the analysis of the oldest, highly thinned ice core sections, where the identification of paleoclimate signals is particularly challenging. For this, state-of-the-art imaging by laser-ablation inductively-coupled plasma mass spectrometry (LA-ICP-MS) has the potential to be revolutionary due to its combination of micron-scale 2D chemical information with visual features. However, the quantitative study of record preservation in chemical images raises new questions that call for the expertise of the computer vision community. To illustrate this new inter-disciplinary frontier, we describe a selected set of key questions. One critical task is to assess the paleoclimate significance of single line profiles along the main core axis, which we show is a scale-dependent problem for which advanced image analysis methods are critical. Another important issue is the evaluation of post-depositional layer changes, for which the chemical images provide rich information. Accordingly, the time is ripe to begin an intensified exchange among the two scientific communities of computer vision and ice core science. The collaborative building of a new framework for investigating high-resolution chemical images with automated image analysis techniques will also benefit the already wide-spread application of LA-ICP-MS chemical imaging in the geosciences.Comment: 9 pages, 2 figures, submitted to Frontiers in Computer Science, section Computer Visio

Detection of ice core particles via deep neural networks

Insoluble particles in ice cores record signatures of past climate parameters like vegetation, volcanic activity or aridity. Their analytical detection depends on intensive bench microscopy investigation and requires dedicated sample preparation steps. Both are laborious, require in-depth knowledge and often restrict sampling strategies. To help overcome these limitations, we present a framework based on Flow Imaging Microscopy coupled to a deep neural network for autonomous image classification of ice core particles. We train the network to classify 7 commonly found classes: mineral dust, felsic and basaltic volcanic ash (tephra), three species of pollen (Corylus avellana, Quercus robur, Quercus suber) and contamination particles that may be introduced onto the ice core surface during core handling operations. The trained network achieves 96.8 % classification accuracy at test time. We present the system’s potentials and limitations with respect to the detection of mineral dust, pollen grains and tephra shards, using both controlled materials and real ice core samples. The methodology requires little sample material, is non destructive, fully reproducible and does not require any sample preparation step. The presented framework can bolster research in the field, by cutting down processing time, supporting human-operated microscopy and further unlocking the paleoclimate potential of ice core records by providing the opportunity to identify an array of ice core particles. Suggestions for an improved system to be deployed within a continuous flow analysis workflow are also presented

Detection of ice core particles via deep neural networks

Insoluble particles in ice cores record signatures of past climate parameters like vegetation dynamics, volcanic activity, and aridity. For some of them, the analytical detection relies on intensive bench microscopy investigation and requires dedicated sample preparation steps. Both are laborious, require in-depth knowledge, and often restrict sampling strategies. To help overcome these limitations, we present a framework based on flow imaging microscopy coupled to a deep neural network for autonomous image classification of ice core particles. We train the network to classify seven commonly found classes, namely mineral dust, felsic and mafic (basaltic) volcanic ash grains (tephra), three species of pollen (Corylus avellana, Quercus robur, Quercus suber), and contamination particles that may be introduced onto the ice core surface during core handling operations. The trained network achieves 96.8 % classification accuracy at test time. We present the system's potential and its limitations with respect to the detection of mineral dust, pollen grains, and tephra shards, using both controlled materials and real ice core samples. The methodology requires little sample material, is non-destructive, fully reproducible, and does not require any sample preparation procedures. The presented framework can bolster research in the field by cutting down processing time, supporting human-operated microscopy, and further unlocking the paleoclimate potential of ice core records by providing the opportunity to identify an array of ice core particles. Suggestions for an improved system to be deployed within a continuous flow analysis workflow are also presented

Supercritical fluid extraction of Eucalyptus globulus bark: a promising approach for triterpenoid production

Eucalyptus bark contains significant amounts of triterpenoids with demonstrated bioactivity, namely triterpenic acids and their acetyl derivatives (ursolic, betulinic, oleanolic, betulonic, 3-acetylursolic, and 3-acetyloleanolic acids). In this work, the supercritical fluid extraction (SFE) of Eucalyptus globulus deciduous bark was carried out with pure and modified carbon dioxide to recover this fraction, and the results were compared with those obtained by Soxhlet extraction with dichloromethane. The effects of pressure (100-200 bar), co-solvent (ethanol) content (0, 5 and 8% wt), and multistep operation were studied in order to evaluate the applicability of SFE for their selective and efficient production. The individual extraction curves of the main families of compounds were measured, and the extracts analyzed by GC-MS. Results pointed out the influence of pressure and the important role played by the co-solvent. Ethanol can be used with advantage, since its effect is more important than increasing pressure by several tens of bar. At 160 bar and 40 degrees C, the introduction of 8% (wt) of ethanol greatly improves the yield of triterpenoids more than threefold