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The physiological fate of cells that die by apoptosis is their prompt and efficient removal by efferocytosis. During these processes, apoptotic cells release intracellular constituents that include purine nucleotides, lysophosphatidylcholine (LPC), and Sphingosine-1-phosphate (S1P) that induce migration and chemo-attraction of phagocytes as well as mitogens and extracellular membrane-bound vesicles that contribute to apoptosis-induced compensatory proliferation and alteration of the extracellular matrix and the vascular network. Additionally, during efferocytosis, phagocytic cells produce a number of anti-inflammatory and resolving factors, and, together with apoptotic cells, efferocytic events have a homeostatic function that regulates tissue repair. These homeostatic functions are dysregulated in cancers, where, aforementioned events, if not properly controlled, can lead to cancer progression and immune escape. Here, we summarize evidence that apoptosis and efferocytosis are exploited in cancer, as well as discuss current translation and clinical efforts to harness signals from dying cells into therapeutic strategies.Gastric cancer (GC), which includes cancer of the esophagus, the oesophagogastric junction, and the stomach fundus, is highly deadly with strong regional influence, Asia being the most affected. GC is often detected at late stages, with 30% of metastatic cases at diagnosis. Opicapone molecular weight Many authors have devised models to both unravel the mechanisms of GC development and to evaluate candidate therapeutics. Among these models, 2D-cell cultures are progressively replaced by 3D-cell cultures that recapitulate, much more comprehensively, tumor cellular and genetic heterogeneity, as well as responsiveness to environmental changes, such as exposure to drugs or irradiation. With respect to the specifics of GC, there are high hopes from such model systems, especially with the aim of identifying prognostic markers and novel drug targets.The aim of this study was to identify placental DNA methylation (DNAm) variations associated with adiposity at 3 years of age. We quantified placental DNAm using the Infinium MethylationEPIC BeadChips. We assessed associations between DNAm at single-CpGs and skinfold thickness using robust linear regression models adjusted for gestational age, child's sex, age at follow-up and cellular heterogeneity. We sought replication of DNAm association with child adiposity in an independent cohort. We quantified placental mRNA levels for annotated gene using qRT-PCR and tested for correlation with DNAm. Lower DNAm at cg22593959 and cg22436429 was associated with higher adiposity (β = -1.18, q = 0.002 and β = -0.82, q = 0.04). The cg22593959 is located in an intergenic region (chr7q31.3), whereas cg22436429 is within the TFAP2E gene (1p34.3). DNAm at cg22593959 and cg22436429 was correlated with mRNA levels at FAM3C (rs = -0.279, p = 0.005) and TFAP2E (rs = 0.216, p = 0.03). In an independent cohort, the association between placental DNAm at cg22593959 and childhood adiposity was of similar strength and direction (β = -3.8 ± 4.1, p = 0.36), yet non-significant. Four genomic regions were also associated with skinfold thickness within FMN1, MAGI2, SKAP2 and BMPR1B genes. We identified placental epigenetic variations associated with adiposity at 3 years of age suggesting that childhood fat accretion patterns might be established during fetal life.Plant life in alpine habitats is shaped by harsh abiotic conditions and cold climates. Phenotypic variation of morphological characters and reproduction can be influenced by temperature stress. Nevertheless, little is known about the performance of different cytotypes under cold stress and how epigenetic patterns could relate to phenotypic variation. Ranunculus kuepferi, a perennial alpine plant, served as a model system for testing the effect of cold stress on phenotypic plasticity, reproduction mode, and epigenetic variation. Diploid and autotetraploid individuals were placed in climate growth cabinets under warm and cold conditions. Morphological traits (height, leaves and flowers) and the proportion of well-developed seeds were measured as fitness indicators, while flow cytometric seed screening (FCSS) was utilized to determine the reproduction mode. Subsequently, comparisons with patterns of methylation-sensitive amplified fragment-length polymorphisms (AFLPs) were conducted. Diploids grew better under warm conditions, while tetraploids performed better in cold treatments. Epigenetic patterns were correlated with the expressed morphological traits. Cold stress reduced the reproduction fitness but did not induce apomixis in diploids. Overall, our study underlines the potential of phenotypic plasticity for acclimation under environmental conditions and confirms the different niche preferences of cytotypes in natural populations. Results help to understand the pattern of geographical parthenogenesis in the species.Macroporous poly(vinyl alcohol) cryogels (PVACGs) are physical gels formed via cryogenic processing of polymer solutions. The properties of PVACGs depend on many factors the characteristics and concentration of PVA, the absence or presence of foreign solutes, and the freezing-thawing conditions. These factors also affect the macroporous morphology of PVACGs, their total porosity, pore size and size distribution, etc. In this respect, there is the problem with developing a scientifically-grounded classification of the morphological features inherent in various PVACGs. In this study PVA cryogels have been prepared at different temperatures when the initial polymer solutions contained chaotropic or kosmotropic additives. After the completion of gelation, the rigidity and heat endurance of the resultant PVACGs were evaluated, and their macroporous structure was investigated using optical microscopy. The images obtained were treated mathematically, and deep neural networks were used for the classification of these images. Training and test sets were used for their classification. The results of this classification for the specific deep neural network architecture are presented, and the morphometric parameters of the macroporous structure are discussed. It was found that deep neural networks allow us to reliably classify the type of additive or its absence when using a combined dataset.
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