Given a passage, construct a question on the basis of the information present in the passage. Construct the question in such a way that (i) it is unambiguous, (ii) it is answerable from the passage, (iii) its answer is unique (iv) it answer uses text phrases from the passage. Avoid creating questions that (i) can't be answered correctly without actually understanding the passage and (ii) uses the same words or phrases given in the passage.

Endometrial cancers (ECs) are the most common form of gynecologic malignancy. Recent studies have reported that ECs reveal distinct markers for molecular pathogenesis, which in turn is linked to the various histological types of ECs. To understand further the molecular events contributing to ECs and endometrial tumorigenesis in general, a more precise identification of cancer-associated molecules and signaling networks would be useful for the detection and monitoring of malignancy, improving clinical cancer therapy, and personalization of treatments.', 'ECs-specific gene co-expression networks were constructed by differential expression analysis and weighted gene co-expression network analysis (WGCNA). Important pathways and putative cancer hub genes contribution to tumorigenesis of ECs were identified. An elastic-net regularized classification model was built using the cancer hub gene signatures to predict the phenotypic characteristics of ECs. The 19 cancer hub gene signatures had high predictive power to distinguish among three key principal features of ECs: grade, type, and stage. Intriguingly, these hub gene networks seem to contribute to ECs progression and malignancy via cell-cycle regulation, antigen processing and the citric acid (TCA) cycle.
Does visual gene-network analysis reveal the cancer gene co-expression in human endometrial cancer?

It is common in public health and epidemiology that the outcome of interest is counts of events occurrence. Analysing these data using classical linear models is mostly inappropriate, even after transformation of outcome variables due to overdispersion. Zero-adjusted mixture count models such as zero-inflated and hurdle count models are applied to count data when over-dispersion and excess zeros exist. Main objective of the current paper is to apply such models to analyse risk factors associated with human helminths (S. haematobium) particularly in a case where there's a high proportion of zero counts.", 'The data were collected during a community-based randomised control trial assessing the impact of mass drug administration (MDA) with praziquantel in Malawi, and a school-based cross sectional epidemiology survey in Zambia. Count data models including traditional (Poisson and negative binomial) models, zero modified models (zero inflated Poisson and zero inflated negative binomial) and hurdle models (Poisson logit hurdle and negative binomial logit hurdle) were fitted and compared.', 'Using Akaike information criteria (AIC), the negative binomial logit hurdle (NBLH) and zero inflated negative binomial (ZINB) showed best performance in both datasets. With regards to zero count capturing, these models performed better than other models.
Do zero adjusted models with applications to analysing helminths count data?

It was recently reported that protein disulfide isomerase (PDI) stimulates factor X (FX) activation by factor VIIa (FVIIa) bound to soluble tissue factor (sTF) in a purified system and that PDI may be responsible for activating cellular tissue factor (TF) and switching it between its roles in blood coagulation and cellular signalling. This study further investigates the former effect of PDI.', 'FX activations by FVIIa-sTF(1-219) were carried out in the presence of different forms of PDI, with annexin V or detergent present in the system and using various forms of FVIIa and FX. In addition, FVIIa-lipidated TF was used as the FX activator.', 'Recombinant human PDI did not influence FX activation by FVIIa-sTF(1-219), whereas PDI purified from bovine liver enhanced the activation rate in a dose-dependent manner. The inclusion of annexin V or detergent abolished the stimulatory effect. Removal of the phospholipid-interactive gamma-carboxyglutamic acid (Gla)-containing domain from either FVIIa or FX obliterated the bovine PDI-induced enhancement of FX activation, as did the introduction of F4A or L8A mutation in FVIIa. The presence of 25 nM bovine PDI lowered the apparent K(m) for FX from far above 10 microM to 1-2 microM. No PDI effect was seen when FVIIa-lipidated TF was the FX activator.

Does protein disulfide isomerase have no stimulatory chaperone effect on factor X activation by factor VIIa-soluble tissue factor?

