Modeling human trophoblast, the placental epithelium at the maternal fetal interface.

Appropriate human trophoblast lineage specification and differentiation is crucial for the establishment of normal placentation and maintenance of pregnancy. However, due to the lack of proper modeling systems, the molecular mechanisms of these processes are still largely unknown. Much of the early studies in this area have been based on animal models and tumor-derived trophoblast cell lines, both of which are suboptimal for modeling this unique human organ. Recent advances in regenerative and stem cell biology methods have led to development of novel in vitro model systems for studying human trophoblast. These include derivation of human embryonic and induced pluripotent stem cells and establishment of methods for the differentiation of these cells into trophoblast, as well as the more recent derivation of human trophoblast stem cells. In addition, advances in culture conditions, from traditional two-dimensional monolayer culture to 3D culturing systems, have led to development of trophoblast organoid and placenta-on-a-chip model, enabling us to study human trophoblast function in context of more physiologically accurate environment. In this review, we will discuss these various model systems, with a focus on human trophoblast, and their ability to help elucidate the key mechanisms underlying placental development and function. This review focuses on model systems of human trophoblast differentiation, including advantages and limitations of stem cell-based culture, trophoblast organoid, and organ-on-a-chip methods and their applications in understanding placental development and disease

Leveraging patient experience measures as surrogate outcomes to evaluate health care interventions

Patient experience quality measure scores are widely accepted as outcomes in health services research. For some patients and in some settings, such as hospice care, they can be the most important outcomes. While these measures are widely used, the potential to use them as surrogate outcomes in a clinical trial sense has gone under-recognized. The purpose of this commentary is to discuss the use of patient experience measures as potential surrogate outcomes in evaluating the effect of a health care intervention. Experience Framework This article is associated with the Policy & Measurement lens of The Beryl Institute Experience Framework (https://theberylinstitute.org/experience-framework/). Access other PXJ articles related to this lens. Access other resources related to this lens

Trophoblast lineage-specific differentiation and associated alterations in preeclampsia and fetal growth restriction.

The human placenta is a poorly-understood organ, but one that is critical for proper development and growth of the fetus in-utero. The epithelial cell type that contributes to primary placental functions is called "trophoblast," including two main subtypes, villous and extravillous trophoblast. Cytotrophoblast and syncytiotrophoblast comprise the villous compartment and contribute to gas and nutrient exchange, while extravillous trophoblast invade and remodel the uterine wall and vessels, in order to supply maternal blood to the growing fetus. Abnormal differentiation of trophoblast contributes to placental dysfunction and is associated with complications of pregnancy, including preeclampsia (PE) and fetal growth restriction (FGR). This review describes what is known about the cellular organization of the placenta during both normal development and in the setting of PE/FGR. It also explains known trophoblast lineage-specific markers and pathways regulating their differentiation, and how these are altered in the setting of PE/FGR, focusing on studies which have used human placental tissues. Finally, it also highlights remaining questions and needed resources to advance this field

Power network and smart grids analysis from a graph theoretic perspective

The growing size and complexity of power systems has given raise to the use of complex network theory in their modelling, analysis, and synthesis. Though most of the previous studies in this area have focused on distributed control through well established protocols like synchronization and consensus, recently, a few fundamental concepts from graph theory have also been applied, for example in symmetry-based cluster synchronization. Among the existing notions of graph theory, graph symmetry is the focus of this proposal. However, there are other development around some concepts from complex network theory such as graph clustering in the study. In spite of the widespread applications of symmetry concepts in many real world complex networks, one can rarely find an article exploiting the symmetry in power systems. In addition, no study has been conducted in analysing controllability and robustness for a power network employing graph symmetry. It has been verified that graph symmetry promotes robustness but impedes controllability. A largely absent work, even in other fields outside power systems, is the simultaneous investigation of the symmetry effect on controllability and robustness. The thesis can be divided into two section. The first section, including Chapters 2-3, establishes the major theoretical development around the applications of graph symmetry in power networks. A few important topics in power systems and smart grids such as controllability and robustness are addressed using the symmetry concept. These topics are directed toward solving specific problems in complex power networks. The controllability analysis will lead to new algorithms elaborating current controllability benchmarks such as the maximum matching and the minimum dominant set. The resulting algorithms will optimize the number of required driver nodes indicated as FACTS devices in power networks. The second topic, robustness, will be tackled by the symmetry analysis of the network to investigate three aspects of network robustness: robustness of controllability, disturbance decoupling, and fault tolerance against failure in a network element. In the second section, including Chapters 4-8, in addition to theoretical development, a few novel applications are proposed for the theoretical development proposed in both sections one and two. In Chapter 4, an application for the proposed approaches is introduced and developed. The placement of flexible AC transmission systems (FACTS) is investigated where the cybersecurity of the associated data exchange under the wide area power networks is also considered. A new notion of security, i.e. moderated-k-symmetry, is introduced to leverage on the symmetry characteristics of the network to obscure the network data from the adversary perspective. In chapters 5-8, the use of graph theory, and in particular, graph symmetry and centrality, are adapted for the complex network of charging stations. In Chapter 5, the placement and sizing of charging stations (CSs) of the network of electric vehicles are addressed by proposing a novel complex network model of the charging stations. The problems of placement and sizing are then reformulated in a control framework and the impact of symmetry on the number and locations of charging stations is also investigated. These results are developed in Chapters 6-7 to robust placement and sizing of charging stations for the Tesla network of Sydney where the problem of extending the capacity having a set of pre-existing CSs are addressed. The role of centrality in placement of CSs is investigated in Chapter 8. Finally, concluding remarks and future works are presented in Chapter 9

Using a Surrogate with Heterogeneous Utility to Test for a Treatment Effect

The primary benefit of identifying a valid surrogate marker is the ability to use it in a future trial to test for a treatment effect with shorter follow-up time or less cost. However, previous work has demonstrated potential heterogeneity in the utility of a surrogate marker. When such heterogeneity exists, existing methods that use the surrogate to test for a treatment effect while ignoring this heterogeneity may lead to inaccurate conclusions about the treatment effect, particularly when the patient population in the new study has a different mix of characteristics than the study used to evaluate the utility of the surrogate marker. In this paper, we develop a novel test for a treatment effect using surrogate marker information that accounts for heterogeneity in the utility of the surrogate. We compare our testing procedure to a test that uses primary outcome information (gold standard) and a test that uses surrogate marker information, but ignores heterogeneity. We demonstrate the validity of our approach and derive the asymptotic properties of our estimator and variance estimates. Simulation studies examine the finite sample properties of our testing procedure and demonstrate when our proposed approach can outperform the testing approach that ignores heterogeneity. We illustrate our methods using data from an AIDS clinical trial to test for a treatment effect using CD4 count as a surrogate marker for RNA