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Breast Cancer Research and Treatment - 相似文献
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Isabel De Bruin Cardoso Lopa Bhattacharjee Claire Cody Joanna Wakia Jade Tachie Menson Maricruz Tabbia 《Vulnerable children and youth studies》2020,15(2):114-123
ABSTRACT Between 2015 and 2018, the RISE Learning Network facilitated learning on approaches, practices, methods, and tools that promote recovery and reintegration of children affected by sexual exploitation. Spanning three regions (Sub-Saharan Africa, South Central Asia, and Latin America and the Caribbean), the RISE Learning Network implemented two learning projects. The first project focused on monitoring (M&E Learning Project) and aimed to generate understanding of approaches and tools that could effectively monitor children and families’ reintegration outcomes. The specific purpose of RISE is to promote learning on reintegration of children affected by sexual exploitation; however, the remit of this Learning Project was to generate evidence on the reintegration of children who have been separated from their families for a range of reasons. This is to ensure that learning from different, but often related, areas of work can be included and compared to strengthen understanding of what successful reintegration of children could look like. The mid- and end-term reviews of the M&E Learning Project have captured lessons learned on how practitioners can approach monitoring of reintegration to mainstream it into their programme cycle. Key lessons learned include the importance of focusing on monitoring outcomes through participatory tools and the benefit of flexible, peer-to-peer learning approaches between practitioners using a variety of monitoring tools. This learning contributes to the nascent evidence base on what effective and efficient capturing of reintegration outcomes on children can look like, in addition to strengthening understanding of what successful reintegration for children and families means. The learnings can inform programming; monitoring, evaluation and learning frameworks; and other interventions around reintegration to ensure the holistic wellbeing of children and families. 相似文献
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Usha Rani Gogoi Mrinal Kanti Bhowmik Debotosh Bhattacharjee Anjan Kumar Ghosh 《Australasian physical & engineering sciences in medicine / supported by the Australasian College of Physical Scientists in Medicine and the Australasian Association of Physical Sciences in Medicine》2018,41(4):861-879
The purpose of this study is to develop a novel breast abnormality detection system by utilizing the potential of infrared breast thermography (IBT) in early breast abnormality detection. Since the temperature distributions are different in normal and abnormal thermograms and hot thermal patches are visible in abnormal thermograms, the abnormal thermograms possess more complex information than the normal thermograms. Here, the proposed method exploits the presence of hot thermal patches and vascular changes by using the power law transformation for pre-processing and singular value decomposition to characterize the thermal patches. The extracted singular values are found to be statistically significant (p?<?0.001) in breast abnormality detection. The discriminability of the singular values is evaluated by using seven different classifiers incorporating tenfold cross-validations, where the thermograms of the Department of Biotechnology-Tripura University-Jadavpur University (DBT-TU-JU) and Database of Mastology Research (DMR) databases are used. In DMR database, the highest classification accuracy of 98.00% with the area under the ROC curve (AUC) of 0.9862 is achieved with the support vector machine using polynomial kernel. The same for the DBT-TU-JU database is 92.50% with AUC of 0.9680 using the same classifier. The comparison of the proposed method with the other reported methods concludes that the proposed method outperforms the other existing methods as well as other traditional feature sets used in IBT based breast abnormality detection. Moreover, by using Rank1 and Rank2 singular values, a breast abnormality grading (BAG) index has also been developed for grading the thermograms based on their degree of abnormality. 相似文献
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Alejandra C. Ventura Alan Bush Gustavo Vasen Matías A. Goldín Brianne Burkinshaw Nirveek Bhattacharjee Albert Folch Roger Brent Ariel Chernomoretz Alejandro Colman-Lerner 《Proceedings of the National Academy of Sciences of the United States of America》2014,111(37):E3860-E3869
Cell signaling systems sense and respond to ligands that bind cell surface receptors. These systems often respond to changes in the concentration of extracellular ligand more rapidly than the ligand equilibrates with its receptor. We demonstrate, by modeling and experiment, a general “systems level” mechanism cells use to take advantage of the information present in the early signal, before receptor binding reaches a new steady state. This mechanism, pre-equilibrium sensing and signaling (PRESS), operates in signaling systems in which the kinetics of ligand-receptor binding are slower than the downstream signaling steps, and it typically involves transient activation of a downstream step. In the systems where it operates, PRESS expands and shifts the input dynamic range, allowing cells to make different responses to ligand concentrations so high as to be otherwise indistinguishable. Specifically, we show that PRESS applies to the yeast directional polarization in response to pheromone gradients. Consideration of preexisting kinetic data for ligand-receptor interactions suggests that PRESS operates in many cell signaling systems throughout biology. The same mechanism may also operate at other levels in signaling systems in which a slow activation step couples to a faster downstream step.Detecting and responding to a chemical gradient is a central feature of a multitude of biological processes (1). For this behavior, organisms use signaling systems that sense information about the extracellular world, transmit this information into the cell, and orchestrate a response. Measurements of the direction and proximity of the extracellular stimuli usually rely on the binding of diffusing chemical particles (ligands) to specific cell surface receptors. Different organisms have evolved different strategies to make use of this information. Small motile organisms, including certain bacteria, use a temporal sensing strategy, measuring and comparing concentration signals over time along their swimming tracks (2). In contrast, some eukaryotic cells, including Saccharomyces cerevisiae, are sufficiently large to implement a spatial sensing mechanism, measuring concentration differences across their cell bodies (3).The observation that some eukaryotes that use spatial sensing exhibit remarkable precision in response to shallow gradients (1–2% differences in ligand concentration between front and rear) (4, 5) has led to several proposed models in which large amplification is achieved by positive feedback loops in the signaling pathways triggered by the ligand-receptor binding (6, 7). Here, we describe a different mechanism, dependent on ligand-receptor binding dynamics, which improves gradient sensing when the concentration of external ligand is close to saturation. We use the budding yeast S. cerevisiae to study the efficiency of this mechanism.Haploid yeast cells exist in two mating types, MATa and MATα (also referred to as a and α cells). Mating occurs when a and α cells sense each other’s secreted mating pheromones: a-factor and α-factor (αF) (8). The pheromone secreted by the nearby mating partner diffuses, forming a gradient surrounding the sensing cell. Pheromone binds a membrane receptor, Ste2, in MATa yeast (9) that activates a pheromone response system (PRS), which cells use to decide whether to fuse with a mating partner or not. At high enough αF concentrations, cells develop a polarized chemotropic growth toward the pheromone source (4). To do that, the nonmotile yeast determines the direction of the potential mating partner measuring on which side there are more bound pheromone receptors, which are initially distributed homogeneously on the cell surface (10). However, this sensing modality can only work when external pheromone is nonsaturating: If all receptors are bound, cells should not be able to determine the direction of the gradient. Surprisingly, even at high pheromone concentrations, yeast tend to polarize in the correct direction (4, 11). Different amplification mechanisms have been proposed to account for the conversion of small differences in ligand concentration across the yeast cell, as is the case for dense mating mixtures, into chemotropic growth (6).We previously studied induction of reporter gene output by the PRS after step increases in the concentration of αF. We found large cell-to-cell variability, the bulk of which was due to large differences in the ability of individual cells to send signal through the system and in their general capacity to express proteins (12). The level of induced gene expression matches well the equilibrium binding curve of αF to receptor (13, 14), a phenomenon known as dose–response alignment (DoRA), common to many other signaling systems (14). In the PRS, DoRA persists for several hours of stimulation.During these studies, we realized that the binding dynamics of αF to its receptor is remarkably slow: At concentrations near the dissociation constant (Kd), binding takes about 20 min to reach 90% of the equilibrium level (15, 16). This dynamics is slow relative not only to the 90-min cell division cycle but also to the pheromone-dependent activation of the mitogen-activated protein kinase (MAPK) Fus3, which takes 2 to 5 min to reach steady-state levels (14). An unavoidable conclusion is that the machinery downstream of the αF receptor must be using pre-equilibrium binding information for its operation.This observation led us to study the consequences of fast and slow ligand-receptor dynamics on the ability of cells to sense extracellular cues. In biology, the rates of ligand binding and unbinding to membrane receptors span a large range, including many cases with dynamics similar to, or even slower than, that of mating pheromone (e.g., rates for EGF, insulin, glucagon, IFN-α1a, and IL-2 in Receptor Ligand Cell type k− (1/s) Kd (M) τ (at L = Kd), s Ref. Fcε IgE Human basophils 2.50E-05 4.80E-10 20,000.00 (17) Fcγ 2.4G2 monoclonal Fab Mouse macrophage 3.80E-05 7.70E-10 13,157.89 (18) Canabinoid receptor CP55,940 Rat brain 1.32E-04 2.10E-08 3,787.88 (19) IL-2 receptor IL-2 T cells 2.00E-04 7.40E-12 2,500.00 (20) α1-Adrenergic Prazosin BC3H1 3.00E-04 7.50E-11 1,666.67 (21) Glucagon receptor Glucagon Rat hepatocytes 4.30E-04 3.06E-10 1,162.79 (22) Formyl peptide receptor (FPR) fMLP Rat neutrophils 5.50E-04 3.45E-08 909.09 (23) Ste2 (αF receptor) αF S. cerevisiae 1.00E-03 5.50E-09 500.00 (15, 16) IFN Human IFN-α1a A549 1.20E-03 3.30E-10 416.67 (24) Transferrin Transferrin HepG2 1.70E-03 3.30E-08 294.12 (25) EGF receptor EGF Fetal rat lung 2.00E-03 6.70E-10 250.00 (26) TNF TNF A549 2.30E-03 1.50E-10 217.39 (24) Insulin receptor Insulin Rat fat cells 3.30E-03 2.10E-08 151.52 (27) FPR FNLLP Rabbit neutrophils 6.70E-03 2.00E-08 74.63 (28) Total fibronectin receptors Fibronectin Fibroblasts 1.00E-02 8.60E-07 50.00 (29) T-cell receptor Class II MHC-peptide 2B4 T-cells 5.70E-02 6.00E-05 8.77 (30) FPR N-formyl peptides Human neutrophils 1.70E-01 1.20E-07 2.94 (31) cAMP receptor cAMP D. discoideum 1.00E+00 3.30E-09 0.50 (32) IL-5 receptor IL-5 COS 1.47E+00 5.00E-09 0.34 (33) NMDA receptor Glutamate Hippocampal neurons 5.00E+00 1.00E-06 0.10 (34) Adenosine A2A Adenosine HEK 293 (human) 1.75E+01 5.20E-08 0.03 (35) AMPA receptor Glutamate HEK 293 (human) 2.00E+03 5.00E-04 2.50E-04 (36)