Mifepristone prevents repopulation of ovarian cancer cells escaping cisplatin-paclitaxel therapy

<p>Abstract</p> <p>Background</p> <p>Advanced ovarian cancer is treated with cytoreductive surgery and combination platinum- and taxane-based chemotherapy. Although most patients have acute clinical response to this strategy, the disease ultimately recurs. In this work we questioned whether the synthetic steroid mifepristone, which as monotherapy inhibits the growth of ovarian cancer cells, is capable of preventing repopulation of ovarian cancer cells if given after a round of lethal cisplatin-paclitaxel combination treatment.</p> <p>Methods</p> <p>We established an <it>in vitro</it> approach wherein ovarian cancer cells with various sensitivities to cisplatin or paclitaxel were exposed to a round of lethal doses of cisplatin for 1 h plus paclitaxel for 3 h. Thereafter, cells were maintained in media with or without mifepristone, and short- and long-term cytotoxicity was assessed.</p> <p>Results</p> <p>Four days after treatment the lethality of cisplatin-paclitaxel was evidenced by reduced number of cells, increased hypodiploid DNA content, morphological features of apoptosis, DNA fragmentation, and cleavage of caspase-3, and of its downstream substrate PARP. Short-term presence of mifepristone either enhanced or did not modify such acute lethality. Seven days after receiving cisplatin-paclitaxel, cultures showed signs of relapse with escaping colonies that repopulated the plate in a time-dependent manner. Conversely, cultures exposed to cisplatin-paclitaxel followed by mifepristone not only did not display signs of repopulation following initial chemotherapy, but they also had their clonogenic capacity drastically reduced when compared to cells repopulating after cisplatin-paclitaxel.</p> <p>Conclusions</p> <p>Cytostatic concentrations of mifepristone after exposure to lethal doses of cisplatin and paclitaxel in combination blocks repopulation of remnant cells surviving and escaping the cytotoxic drugs.</p

Human performance and strategies while solving an aircraft routing and sequencing problem: an experimental approach

As airport resources are stretched to meet increasing demand for services, effective use of ground infrastructure is increasingly critical for ensuring operational efficiency. Work in operations research has produced algorithms providing airport tower controllers with guidance on optimal timings and sequences for flight arrivals, departures, and ground movement. While such decision support systems have the potential to improve operational efficiency, they may also affect users’ mental workload, situation awareness, and task performance. This work sought to identify performance outcomes and strategies employed by human decision makers during an experimental airport ground movement control task with the goal of identifying opportunities for enhancing user-centered tower control decision support systems. To address this challenge, thirty novice participants solved a set of vehicle routing problems presented in the format of a game representing the airport ground movement task practiced by runway controllers. The games varied across two independent variables, network map layout (representing task complexity) and gameplay objective (representing task flexibility), and verbal protocol, visual protocol, task performance, workload, and task duration were collected as dependent variables. A logistic regression analysis revealed that gameplay objective and task duration significantly affected the likelihood of a participant identifying the optimal solution to a game, with the likelihood of an optimal solution increasing with longer task duration and in the less flexible objective condition. In addition, workload appeared unaffected by either independent variable, but verbal protocols and visual observations indicated that high-performing participants demonstrated a greater degree of planning and situation awareness. Through identifying human behavior during optimization problem solving, the work of tower control can be better understood, which, in turn, provides insights for developing decision support systems for ground movement management

People Efficiently Explore the Solution Space of the Computationally Intractable Traveling Salesman Problem to Find Near-Optimal Tours

Humans need to solve computationally intractable problems such as visual search, categorization, and simultaneous learning and acting, yet an increasing body of evidence suggests that their solutions to instantiations of these problems are near optimal. Computational complexity advances an explanation to this apparent paradox: (1) only a small portion of instances of such problems are actually hard, and (2) successful heuristics exploit structural properties of the typical instance to selectively improve parts that are likely to be sub-optimal. We hypothesize that these two ideas largely account for the good performance of humans on computationally hard problems. We tested part of this hypothesis by studying the solutions of 28 participants to 28 instances of the Euclidean Traveling Salesman Problem (TSP). Participants were provided feedback on the cost of their solutions and were allowed unlimited solution attempts (trials). We found a significant improvement between the first and last trials and that solutions are significantly different from random tours that follow the convex hull and do not have self-crossings. More importantly, we found that participants modified their current better solutions in such a way that edges belonging to the optimal solution (“good” edges) were significantly more likely to stay than other edges (“bad” edges), a hallmark of structural exploitation. We found, however, that more trials harmed the participants' ability to tell good from bad edges, suggesting that after too many trials the participants “ran out of ideas.” In sum, we provide the first demonstration of significant performance improvement on the TSP under repetition and feedback and evidence that human problem-solving may exploit the structure of hard problems paralleling behavior of state-of-the-art heuristics

Automated Discrimination of Brain Pathological State Attending to Complex Structural Brain Network Properties: The Shiverer Mutant Mouse Case

Neuroimaging classification procedures between normal and pathological subjects are sparse and highly dependent of an expert's clinical criterion. Here, we aimed to investigate whether possible brain structural network differences in the shiverer mouse mutant, a relevant animal model of myelin related diseases, can reflect intrinsic individual brain properties that allow the automatic discrimination between the shiverer and normal subjects. Common structural networks properties between shiverer (C3Fe.SWV Mbpshi/Mbpshi, n = 6) and background control (C3HeB.FeJ, n = 6) mice are estimated and compared by means of three diffusion weighted MRI (DW-MRI) fiber tractography algorithms and a graph framework. Firstly, we found that brain networks of control group are significantly more clustered, modularized, efficient and optimized than those of the shiverer group, which presented significantly increased characteristic path length. These results are in line with previous structural/functional complex brain networks analysis that have revealed topologic differences and brain network randomization associated to specific states of human brain pathology. In addition, by means of network measures spatial representations and discrimination analysis, we show that it is possible to classify with high accuracy to which group each subject belongs, providing also a probability value of being a normal or shiverer subject as an individual anatomical classifier. The obtained correct predictions (e.g., around 91.6–100%) and clear spatial subdivisions between control and shiverer mice, suggest that there might exist specific network subspaces corresponding to specific brain disorders, supporting also the point of view that complex brain network analyses constitutes promising tools in the future creation of interpretable imaging biomarkers

Characterization of an Nmr Homolog That Modulates GATA Factor-Mediated Nitrogen Metabolite Repression in Cryptococcus neoformans

Nitrogen source utilization plays a critical role in fungal development, secondary metabolite production and pathogenesis. In both the Ascomycota and Basidiomycota, GATA transcription factors globally activate the expression of catabolic enzyme-encoding genes required to degrade complex nitrogenous compounds. However, in the presence of preferred nitrogen sources such as ammonium, GATA factor activity is inhibited in some species through interaction with co-repressor Nmr proteins. This regulatory phenomenon, nitrogen metabolite repression, enables preferential utilization of readily assimilated nitrogen sources. In the basidiomycete pathogen Cryptococcus neoformans, the GATA factor Gat1/Are1 has been co-opted into regulating multiple key virulence traits in addition to nitrogen catabolism. Here, we further characterize Gat1/Are1 function and investigate the regulatory role of the predicted Nmr homolog Tar1. While GAT1/ARE1 expression is induced during nitrogen limitation, TAR1 transcription is unaffected by nitrogen availability. Deletion of TAR1 leads to inappropriate derepression of non-preferred nitrogen catabolic pathways in the simultaneous presence of favoured sources. In addition to exhibiting its evolutionary conserved role of inhibiting GATA factor activity under repressing conditions, Tar1 also positively regulates GAT1/ARE1 transcription under non-repressing conditions. The molecular mechanism by which Tar1 modulates nitrogen metabolite repression, however, remains open to speculation. Interaction between Tar1 and Gat1/Are1 was undetectable in a yeast two-hybrid assay, consistent with Tar1 and Gat1/Are1 each lacking the conserved C-terminus regions present in ascomycete Nmr proteins and GATA factors that are known to interact with each other. Importantly, both Tar1 and Gat1/Are1 are suppressors of C. neoformans virulence, reiterating and highlighting the paradigm of nitrogen regulation of pathogenesis

Evaluating the importance of the convex hull in solving the Euclidean version of the traveling salesperson problem: reply to Lee and Vickers (2000).

Lee and Vickers (2000) suggest that the results of MacGregor and Ormerod (1996), showing that the response uncertainty to traveling salesperson problems (TSPs) increases with increasing numbers of nonboundary points, may have resulted as an artifact of constraints imposed in the construction of stimuli. The fact that similar patterns of results have been obtained for our "constrained" stimuli, for a stimulus constructed under different constraints, for 13 randomly generated stimuli, and for random and patterned 48-point problems provides empirical evidence that the results are not artifactual. Lee and Vickers further suggest that, even if not artifactual, the results are in principle limited to arrays of fewer than 50 points and that, beyond this, the total number of points and number of nonboundary points are "diagnostically equivalent." This claim seems to us incorrect, since arrays of any size can be constructed that will permit experimental tests of whether problem difficulty is influenced by the number of nonboundary points, or the total number of points, or both. We present a reanalysis of our original data using hierarchical regression analysis which indicates that both factors may influence problem complexity