Papel actual del acetato de ulipristal en el tratamiento de los síntomas asociados a los miomas uterinos. [Current role of ulipristal acetate in the management of uterine fibroids.]

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In Vitro Effect of Δ9-Tetrahydrocannabinol and Cannabidiol on Cancer-Associated Fibroblasts Isolated from Lung Cancer

There is evidence that demonstrates the effect of cannabinoid agonists inhibiting relevant aspects in lung cancer, such as proliferation or epithelial-to-mesenchymal transition (EMT). Most of these studies are based on evidence observed in in vitro models developed on cancer cell lines. These studies do not consider the complexity of the tumor microenvironment (TME). One of the main components of the TME is cancer-associated fibroblasts (CAFs), cells that are relevant in the control of proliferation and metastasis in lung cancer. In this work, we evaluated the direct effects of two cannabinoid agonists, tetrahydrocannabinol (THC) and cannabidiol (CBD), used alone or in combination, on CAFs and non-tumor normal fibroblasts (NFs) isolated from adenocarcinoma or from healthy lung tissue from the same patients. We observed that these compounds decrease cell density in vitro and inhibit the increase in the relative expression of type 1 collagen (COL1A1) and fibroblast-specific protein 1 (FSP1) induced by transforming growth factor beta (TGFβ). On the other hand, we studied whether THC and CBD could modulate the interactions between CAFs or NFs and cancer cells. We conditioned the culture medium with stromal cells treated or not with THC and/or CBD and cultured A549 cells with them. We found that culture media conditioned with CAFs or NFs increased cell density, induced morphological changes consistent with EMT, inhibited cadherin-1 (CDH1) gene expression, and induced an increase in the relative expression of cadherin-2 (CDH2) and vimentin (VIM) genes in A549 cells. These changes were inhibited or decreased by THC and CBD administered alone or in combination. In another series of experiments, we conditioned culture media with A549 cells treated or not with THC and/or CBD, in the presence or absence of TGFβ. We observed that culture media conditioned with A549 in the presence of TGFβ induced an increase in the expression of COL1A1 and VIM, both in CAFs and in non-tumor NFs. Both THC and CBD ameliorated these effects. In summary, the results presented here reinforce the usefulness of cannabinoid agonists for the treatment of some relevant aspects of lung cancer pathology, and demonstrate in a novel way their possible effects on CAFs as a result of their relationship with cancer cells. Likewise, the results reinforce the usefulness of the combined use of THC and CBD, which has important advantages in relation to the possibility of using lower doses, thus minimizing the psychoactive effects of THC

Incremental Gaussian Discriminant Analysis based on Graybill and Deal weighted combination of estimators for brain tumour diagnosis

Item does not contain fulltextIn the last decade, machine learning (ML) techniques have been used for developing classifiers for automatic brain tumour diagnosis. However, the development of these ML models rely on a unique training set and learning stops once this set has been processed. Training these classifiers requires a representative amount of data, but the gathering, preprocess, and validation of samples is expensive and time-consuming. Therefore, for a classical, non-incremental approach to ML, it is necessary to wait long enough to collect all the required data. In contrast, an incremental learning approach may allow us to build an initial classifier with a smaller number of samples and update it incrementally when new data are collected. In this study, an incremental learning algorithm for Gaussian Discriminant Analysis (iGDA) based on the Graybill and Deal weighted combination of estimators is introduced. Each time a new set of data becomes available, a new estimation is carried out and a combination with a previous estimation is performed. iGDA does not require access to the previously used data and is able to include new classes that were not in the original analysis, thus allowing the customization of the models to the distribution of data at a particular clinical center. An evaluation using five benchmark databases has been used to evaluate the behaviour of the iGDA algorithm in terms of stability-plasticity, class inclusion and order effect. Finally, the iGDA algorithm has been applied to automatic brain tumour classification with magnetic resonance spectroscopy, and compared with two state-of-the-art incremental algorithms. The empirical results obtained show the ability of the algorithm to learn in an incremental fashion, improving the performance of the models when new information is available, and converging in the course of time. Furthermore, the algorithm shows a negligible instance and concept order effect, avoiding the bias that such effects could introduce